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aion-labs/aion-3.0-mini

Mean 0.874 · 28/40 perfect tests · $1.29 total · modalities in: text · out: text · each card: the prompt → the correct answer (gold) → this model's actual answer

What do the modalities mean?

Modalities in means what you can send this model: text, images, files, video, or audio. Modalities out means what it can send back. Bench tasks feed text extracts, so modalities do not change the score. They are here so you know what this model can handle in real work.

data-crm 5 tests
stage-hygiene-audit0.67
Plain-English question: How much of the open DS2+ pipeline is missing a substantive why-buys, and is anything missing DS1 timestamps or out of order?
Prompt — what the model saw
Audit stage hygiene on the open pipeline below. Rules: a deal at DS2 or later must have a substantive why-buys (15+ characters, not a placeholder such as tbd, n/a, see notes). A why_buys value of the form LONG(n) means the real text was present but truncated for display; treat it as present and substantive. Every deal should have a DS1 entry timestamp. A deal whose stage entry timestamps are out of order regressed (an earlier-stage timestamp is later than a later-stage timestamp). Report: percent of DS2+ deals non-compliant on why-buys overall and by owner (use the owners table for names), count of deals with no DS1 timestamp, count that regressed, and three process fixes. State definitions before numbers.
cost $0.0434 · 564s · in 21,483 / out 14,307 tok
✗ noncomp_pct✓ no_ds1_0✓ regressed_0✗ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

Plain-English answer: 81.5% of the 124 DS2+ deals are missing a substantive why-buys — Alex Franklin 48, Cole Ingram 19, Dana Mercer 18, Bryce Harmon 15, Farid Osman 1 — and every deal has a DS1 timestamp with none regressed.
The answer in one line: n_deals = 156; ds2plus = 124; noncomp_pct = 81.5; bryce_noncomp = 15; no_ds1 = 0; regressed = 0 (+1 more below)
n_deals156
bryce_noncomp15
ds2plus124
no_ds10
noncomp_pct81.5
regressed0
per_owner_noncompAlex Franklin=48 of 61 (78.7%); Cole Ingram=19 of 22 (86.4%); Dana Mercer=18 of 22 (81.8%); Bryce Harmon=15 of 18 (83.3%); Farid Osman=1 of 1 (100%)
graded checksnoncomp_pct · no_ds1_0 · regressed_0 · per_owner_breakdown · definitions_first · three_fixes

Model's actual answer

show the model's full answer
**Stage Hygiene Audit — Definitions & Results**

**Definitions:**
- **Why-buys non-compliant (DS2+):** why_buys empty, <15 chars, or a placeholder (tbd, n/a, see notes, none, todo). LONG(n) counts as compliant — the real text exists, just truncated for display.
- **No DS1 timestamp:** t_ds1 field is empty/blank.
- **Regressed:** any deal where an earlier-stage timestamp (t_ds1, t_ds2, ...) is later than a later-stage timestamp — indicates stage entry dates were recorded out of order.
- **DS2+ deals:** deals currently at DS2, DS3, DS4, or DS5.

**Results:**

**Why-buys compliance (DS2+ deals):**
- Total DS2+ deals: 55
- Non-compliant: 46 of 55 = 83.6%
- By owner:
  - Jared Holt: 15 of 18 = 83.3%
  - Mike Kim: 22 of 26 = 84.6%
  - Alex Chen: 6 of 8 = 75.0%
  - Sara Patel: 2 of 2 = 100.0%
  - Farid Osman: 1 of 1 = 100.0%

**No DS1 timestamp:** 0 deals — every deal in the pipeline has a DS1 entry timestamp.

**Regressions:** 0 deals — no cases where t_ds[N] > t_ds[N+1].

**Three process fixes:**

1. **Make why_buys required before advancing past DS1.** Currently reps can push deals to DS2+ without capturing any buyer rationale. A CRM validation rule blocking stage advancement when why_buys is empty or <15 chars would eliminate 100% of the 46 violations at the source.

2. **Add a weekly pipeline scrub for owners with >80% why_buys gaps.** Jared Holt (83.3%), Mike Kim (84.6%), and Sara Patel (100%) are all at or above that threshold. A Monday-morning automated report listing DS2+ deals missing why_buys, sent to each owner, would close these within a single quarter.

3. **Retrain on what counts as substantive.** "See notes" and "tbd" are being treated as acceptable. Define a standard: the field must describe the buyer's stated business need, not the product feature. Example: "Need to reduce manual reporting time from 4 hrs/week to 30 min" vs. "good product." Put this in the CRM help-text on the field itself.
owner-resolution0.67
Plain-English question: Every open deal has a blank owner name — resolve each one to a real person using the owners table.
Prompt — what the model saw
The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.
cost $0.0294 · 160s · in 29,339 / out 3,385 tok
✗ resolved_count✓ unmatched_none✓ deactivated_flagged✓ jake_amount✓ justin_amount✗ no_invented

✓ Correct Answer (ground truth)

Plain-English answer: All 156 deals resolve to six named owners; jake_amount maps to Bryce Harmon ($1,054,144) and justin_amount to Alex Franklin ($624,310).
The answer in one line: jake_amount = 1,054,144; justin_amount = 624,310; resolved = 156
jake_amount1,054,144
justin_amount624,310
resolved156
deactivated
per_ownerBryce Harmon=1,054,144; Alex Franklin=624,310; Dana Mercer=341,195; Cole Ingram=288,161.43; Farid Osman=4,134; Elena Sinclair=2,100
graded checksresolved_count · unmatched_none · deactivated_flagged · jake_amount · justin_amount · no_invented

Model's actual answer

show the model's full answer
Let me process this systematically.

**Owners table lookup:**

| owner_id | owner_name | archived |
|---|---|---|
| 1520255671 | Gavin Porter | true |
| 77260721 | Hugo Lindqvist | true |
| 119337721 | Bryce Harmon | false |
| 83155923 | Dana Mercer | false |
| 84342457 | Alex Franklin | false |
| 83155924 | Cole Ingram | false |
| 716654662 | Farid Osman | false |
| 701163055 | Elena Sinclair | false |

**Step 1: Count deals and resolve owners**

Distinct owner_ids in deals_open.csv:
- 119337721 (Bryce Harmon) - active
- 84342457 (Alex Franklin) - active
- 83155923 (Dana Mercer) - active
- 83155924 (Cole Ingram) - active
- 716654662 (Farid Osman) - active
- 701163055 (Elena Sinclair) - active

All 6 owner IDs in the deals file have matches in the owners table. **0 unmatched owner IDs.**

**Step 2: Archived/deactivated owners**

Of the 6 matched owner IDs, none are archived. The two archived owners (1520255671 Gavin Porter, 77260721 Hugo Lindqvist) have zero deals in the extract. **0 archived owner IDs map to deals.**

**Step 3: Pipeline amount per resolved owner**

I'll sum amounts per owner_id:

**Bryce Harmon (119337721):**
24000 + 19656 + 13500 + 7000 + 2520 + 240000 + 99000 + 72000 + 70000 + 63600 + 45000 + 1 + 21000 + 23400 + 13680 + 5502 + 8160 + 1 + 11400 + 1 + 36000 + 31500 + 6000 + 10800 + 30275 + 17400 + 12600 + 18000 + 37440 + 18828 + 2880 + 36000 + 20880 + 10920 + 25200 = 

Let me sum carefully:
24000 + 19656 = 43656
+ 13500 = 57156
+ 7000 = 64156
+ 2520 = 66676
+ 240000 = 306676
+ 99000 = 405676
+ 72000 = 477676
+ 70000 = 547676
+ 63600 = 611276
+ 45000 = 656276
+ 1 = 656277
+ 21000 = 677277
+ 23400 = 700677
+ 13680 = 714357
+ 5502 = 719859
+ 8160 = 728019
+ 1 = 728020
+ 11400 = 739420
+ 1 = 739421
+ 36000 = 775421
+ 31500 = 806921
+ 6000 = 812921
+ 10800 = 823721
+ 30275 = 853996
+ 17400 = 871396
+ 12600 = 883996
+ 18000 = 901996
+ 37440 = 939436
+ 18828 = 958264
+ 2880 = 961144
+ 36000 = 997144
+ 20880 = 1018024
+ 10920 = 1028944
+ 25200 = **1054144**

**Alex Franklin (84342457):**
14850 + 13770 + 11200 + 9000 + 6360 + 5400 + 3240 + 2484 + 1920 + 1080 + 7200 + 19000 + 2880 + 1400 + 4800 + 1632 + 10000 + 9300 + 2700 + 2160 + 1800 + 3600 + 3840 + 15000 + 1968 + 4000 + 3600 + 4800 + 3120 + 2520 + 9000 + 2400 + 62000 + 5400 + 5100 + 16700 + 4400 + 1620 + 2600 + 7200 + 18000 + 17000 + 8316 + 8100 + 18000 + 12600 + 24000 + 15000 + 9000 + 7200 + 3780 + 16200 + 7200 + 4680 + 1800 + 18000 + 2730 + 2400 + 3060 + 18000 + 12000 + 1800 + 4400 + 31200 + 7200 + 1600 + 60000

This is a lot of numbers. Let me batch them:

First batch: 14850+13770+11200+9000+6360+5400+3240+2484+1920+1080+7200+19000+2880+1400+4800+1632+10000+9300+2700+2160 = 
14850+13770 = 28620
+11200 = 39820
+9000 = 48820
+6360 = 55180
+5400 = 60580
+3240 = 63820
+2484 = 66304
+1920 = 68224
+1080 = 69304
+7200 = 76504
+19000 = 95504
+2880 = 98384
+1400 = 99784
+4800 = 104584
+1632 = 106216
+10000 = 116216
+9300 = 125516
+2700 = 128216
+2160 = 130376

Second batch: 1800+3600+3840+15000+1968+4000+3600+4800+3120+2520+9000+2400+62000+5400+5100+16700+4400+1620+2600+7200
1800+3600 = 5400
+3840 = 9240
+15000 = 24240
+1968 = 26208
+4000 = 30208
+3600 = 33808
+4800 = 38608
+3120 = 41728
+2520 = 44248
+9000 = 53248
+2400 = 55648
+62000 = 117648
+5400 = 123048
+5100 = 128148
+16700 = 144848
+4400 = 149248
+1620 = 150868
+2600 = 153468
+7200 = 160668

Third batch: 18000+17000+8316+8100+18000+12600+24000+15000+9000+7200+3780+16200+7200+4680+1800+18000+2730+2400+3060+18000+12000+1800+4400+31200+7200+1600+60000
18000+17000 = 35000
+8316 = 43316
+8100 = 51416
+18000 = 69416
+12600 = 82016
+24000 = 106016
+15000 = 121016
+9000 = 130016
+7200 = 137216
+3780 = 140996
+16200 = 157196
+7200 = 164396
+4680 = 169076
+1800 = 170876
+18000 = 188876
+2730 = 191606
+2400 = 194006
+3060 = 197066
+18000 = 215066
+12000 = 227066
+1800 = 228866
+4400 = 233266
+31200 = 264466
+7200 = 271666
+1600 = 273266
+60000 = 333266

Now sum all three batches: 130376 + 160668 + 333266 = **624310**

**Dana Mercer (83155923):**
11250 + 10500 + 9000 + 9000 + 5400 + 4800 + 4600 + 1920 + 15000 + 4200 + 18900 + 27000 + 43875 + 20000 + 60000 + 8100 + 16250 + 3150 + 5000 + 2100 + 23400 + 5400 + 7350 + 25000

11250+10500 = 21750
+9000 = 30750
+9000 = 39750
+5400 = 45150
+4800 = 49950
+4600 = 54550
+1920 = 56470
+15000 = 71470
+4200 = 75670
+18900 = 94570
+27000 = 121570
+43875 = 165445
+20000 = 185445
+60000 = 245445
+8100 = 253545
+16250 = 269795
+3150 = 272945
+5000 = 277945
+2100 = 280045
+23400 = 303445
+5400 = 308845
+7350 = 316195
+25000 = **341195**

**Cole Ingram (83155924):**
2760 + 1330 + 7225.4 + 3360 + 5616 + 2700 + 7781.2 + 6947.5 + 40000 + 12168 + 2480.4 + 11116 + 11193 + 1875 + 31750 + 32175 + 4779.88 + 3334.8 + 58529.25 + 4140 + 18000 + 18900

2760+1330 = 4090
+7225.4 = 11315.4
+3360 = 14675.4
+5616 = 20291.4
+2700 = 22991.4
+7781.2 = 30772.6
+6947.5 = 37720.1
+40000 = 77720.1
+12168 = 89888.1
+2480.4 = 92368.5
+11116 = 103484.5
+11193 = 114677.5
+1875 = 116552.5
+31750 = 148302.5
+32175 = 180477.5
+4779.88 = 185257.38
+3334.8 = 188592.18
+58529.25 = 247121.43
+4140 = 251261.43
+18000 = 269261.43
+18900 = **288161.43**

**Farid Osman (716654662):**
1249 + 1 + 1 + 1 + 2880 + 1 + 1 = 
1249+1 = 1250
+1 = 1251
+1 = 1252
+2880 = 4132
+1 = 4133
+1 = **4134**

**Elena Sinclair (701163055):**
2100

**Summary:**

| Owner | Resolved Deals | Total Pipeline |
|---|---|---|
| Bryce Harmon | 35 | $1,054,144 |
| Alex Franklin | 64 | $624,310 |
| Dana Mercer | 24 | $341,195 |
| Cole Ingram | 22 | $288,161.43 |
| Farid Osman | 7 | $4,134 |
| Elena Sinclair | 1 | $2,100 |

**Total deals resolved:** 153 (35+64+24+22+7+1)

**Deals whose owner_id has no match in owners table:** None. All 6 owner IDs in the deals file (119337721, 84342457, 83155923, 83155924, 716654662, 701163055) have matching rows in owners.csv.

**Owner IDs mapping to archived/deactivated owners:** None. The two archived owners (1520255671 Gavin Porter, 77260721 Hugo Lindqvist) have zero deals assigned to them.
gong-hubspot-join-integrityFAIL 0.00
Plain-English question: How many call records point at deal ids that do not exist in the open pipeline?
Prompt — what the model saw
Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.0117 · 1375s · in 7,854 / out 1,319 tok
✗ orphan_count✗ dup_keys_0✗ ds3_coverage✗ sample_ids_present

✓ Correct Answer (ground truth)

Plain-English answer: 30 Gong call rows are orphans keyed to dead deal ids, none are duplicates, and DS3 call coverage is 29.4%.
The answer in one line: orphans = 30; ds3_cov_pct = 29.4; dup = 0
orphans30
ds3_cov_pct29.4
dup0
orphan_aliasesDeal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09
graded checksorphan_count · dup_keys_0 · ds3_coverage · sample_ids_present

Model's actual answer

show the model's full answer
I'll analyze this programmatically to ensure accuracy. Let me process both files.
snowflake-sql-trapped-catalogPASS 1.00
Plain-English question: Write one Snowflake query for first-month usage per customer using only the provided, trap-laden catalog.
Prompt — what the model saw
Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.0102 · 89s · in 311 / out 4,935 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✓ uses_cohort_summary✓ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

Plain-English answer: A single correct SQL query that uses the right catalog tables and avoids the trap columns — graded on the exact tables and columns.
The answer in one line: use_table = PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
avoid_stalePRODUCTION.HUBSPOT.DEALS (stale)
avoid_unpopulatedV2_LIVE.OBJECTS_DEALS (unpopulated)
forbidden_filterdeleted-giver exclusion must NOT be applied
outputone fenced SELECT with per-FROM/JOIN table-choice comment
required_filtersucceeded redemptions only
use_tablePRODUCTION.PLG.COMPANY_COHORT_SUMMARY
graded checksavoids_stale_deals · avoids_unpopulated_live · uses_cohort_summary · succeeded_filter · no_giver_deletion_filter · returns_sql

Model's actual answer

show the model's full answer
```sql
-- Per customer company, first calendar month as a customer:
--   unique givers, recognition count, successful redemption count.
--
-- NOTE: No raw giving/recognition events table is present in this catalog excerpt.
-- Unique givers and recognition count are sourced from the pre-computed M1 columns
-- in COMPANY_COHORT_SUMMARY. The business rule about NOT filtering deleted givers
-- applies to the raw giving table (not in this catalog), so the pre-computed
-- M1_GIVES and M1_USERS should already include all historical givers.

SELECT
    c.COMPANY_ID,
    DATE_TRUNC('month', c.FIRST_SUB_PAYMENT_DATE) AS first_calendar_month,
    c.M1_USERS AS unique_givers,
    c.M1_GIVES AS recognition_count,
    COALESCE(COUNT(r.COMPANY_ID), 0) AS successful_redemption_count

FROM
    -- PRODUCTION.PLG.COMPANY_COHORT_SUMMARY: one row per self-serve company.
    -- FIRST_SUB_PAYMENT_DATE marks when they first paid → became a customer.
    -- M1_USERS and M1_GIVES are pre-computed month-1 aggregates from the raw
    -- giving table (which is not listed in this catalog excerpt). Per business rules,
    -- the deleted-giver filter was NOT applied to these historical counts, so they
    -- are the correct source.
    PRODUCTION.PLG.COMPANY_COHORT_SUMMARY c

LEFT JOIN
    -- PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2: documented
    -- source for redemption events despite the DEPRECATED schema name. Only
    -- STATE = 'succeeded' rows count as redemptions per business rules.
    -- Joined on company and calendar month to count redemptions in the same first
    -- calendar month the customer started.
    PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 r
    ON c.COMPANY_ID = r.COMPANY_ID
    AND r.STATE = 'succeeded'
    AND DATE_TRUNC('month', r.REDEMPTION_TIMESTAMP) = DATE_TRUNC('month', c.FIRST_SUB_PAYMENT_DATE)

WHERE
    c.FIRST_SUB_PAYMENT_DATE IS NOT NULL

GROUP BY
    c.COMPANY_ID,
    DATE_TRUNC('month', c.FIRST_SUB_PAYMENT_DATE),
    c.M1_USERS,
    c.M1_GIVES

ORDER BY
    c.COMPANY_ID;
```
crm-completeness-enrichmentPASS 1.00
Plain-English question: Audit the CRM for missing and invalid fields, and propose the fixes.
Prompt — what the model saw
Audit the CRM extract for completeness and propose fixes. Deals need owner, stage, amount, close date, why-buys; companies need industry, employee count, HQ country; contacts need email, title, persona. Report completeness percent per field. Identify duplicate company clusters (name variants, shared domain) with a survivor per cluster. List invalid emails and domain mismatches. Fill missing company fields only where the enrichment export has a matching row; where CRM and enrichment disagree list both and recommend a source. Never invent a value. End with the 10 fixes with the most pipeline amount at stake.
cost $0.0152 · 117s · in 1,873 / out 7,741 tok
✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

Plain-English answer: 9 companies missing employee count, 6 missing HQ country, 4 invalid emails, 1 domain mismatch, 2 duplicate clusters, and 25 enrichment matches.
The answer in one line: n_contacts = 52; n_companies = 34; enrichment_matches = 25; invalid_email_count = 4; n_dup_clusters = 2; domain_mismatch_count = 1 (+4 more below)
n_contacts52
domain_mismatch_count1
enrichment_matches25
invalid_email_count4
n_companies34
n_dup_clusters2
company_missingindustry=0; employee_count=9; hq_country=6
domain_mismatchesCT-0011
duplicate_clusters['C-0A092931', 'C-0A092932'], ['C-0A092933', 'C-0A092934']
invalid_emailsCT-0010, CT-0080, CT-0081, CT-0192
graded checksdup_clusters · invalid_emails · survivor_language · completeness_pct · no_invent

Model's actual answer

show the model's full answer
# CRM Data Quality Audit

## 1. Completeness Report

### Companies (34 rows)
| Field | Populated | Blank | Completeness |
|---|---|---|---|
| **domain** | 34 | 0 | **100%** |
| **industry** | 34 | 0 | **100%** *(but contains 6 variant labels — see normalization notes)* |
| **employee_count** | 24 | 10 | **70.6%** |
| **hq_country** | 28 | 6 | **82.4%** |

Employee count blanks: C-EC3025, C-96039F, C-44EA29, C-D04904, C-B23205, C-E51FB7, C-60C75F, C-7BBDFA, C-50D386, C-93C8BF *(10 total)*

HQ country blanks: C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-EE9FFB *(6 total)*

### Contacts (52 rows)
| Field | Populated | Blank | Completeness |
|---|---|---|---|
| **email** | 48 | 4 | **92.3%** *(valid: 47; 1 domain mismatch)* |
| **title** | 44 | 8 | **84.6%** |
| **persona** | 36 | 16 | **69.2%** |

Persona blanks (16): CT-0000, CT-0022, CT-0041, CT-0060, CT-0070, CT-0080, CT-0081, CT-0082, CT-0092, CT-0110, CT-0132, CT-0162, CT-0171, CT-0172, CT-0180, CT-0181

Title blanks (8): CT-0000, CT-0022, CT-0072, CT-0092, CT-0120, CT-0132, CT-0162, CT-0170

### Deals
**No data provided.** The task references deals fields (owner, stage, amount, close date, why-buys) but the CRM extract contains only companies.csv and contacts.csv. Pipeline-amount-based prioritization for the top 10 fixes is **not possible** — I will rank by data quality impact instead.

---

## 2. Duplicate Company Clusters

| Cluster | Aliases | Shared Domain | CRM Industry | Emp Count | HQ Country | Survivor |
|---|---|---|---|---|---|---|
| **acme-corp.com** | C-0A092931 | acme-corp.com | Technology | 500 | US | **C-0A092931** *(first alias, standardized)* |
| | C-0A092932 | acme-corp.com | tech | 510 | USA | merge into survivor |
| **globex.io** | C-0A092933 | globex.io | SaaS | 200 | US | **C-0A092933** *(first alias, standardized)* |
| | C-0A092934 | globex.io | Technology | 200 | US | merge into survivor |

No other shared domains found among the remaining 30 hex-alias companies — each has a unique domain.

**Proposed merging:**
- **C-0A092932 → C-0A092931** (acme-corp.com, keep 500 emp, US, industry "Technology")
- **C-0A092934 → C-0A092933** (globex.io, keep 200 emp, US, industry "Technology" — more standard than "SaaS")

---

## 3. Invalid Emails & Domain Mismatches

### Invalid emails (missing domain after @):
| Contact | Email | Issue |
|---|---|---|
| CT-0010 | `user0@` | No domain — orphan |
| CT-0080 | `user0@` | No domain — orphan |
| CT-0081 | `user1@` | No domain — orphan |
| CT-0192 | `user2@` | No domain — orphan |

### Domain mismatch:
| Contact | Email | Company Domain | Issue |
|---|---|---|---|
| CT-0011 | `user1@other-domain.com` | 66d1fc.com | Email domain ≠ company domain; likely wrong contact or wrong company association |

---

## 4. Enrichment-Based Fills & Disagreements

25 of 34 companies have a matching row in `zoominfo_enrichment.csv`. 9 have none: C-93C8BF, C-BA969B, C-332637, C-EE9FFB, C-C9BB20, C-0A092931, C-0A092932, C-0A092933, C-0A092934.

### Fills (CRM blank → enrichment value):

| Company | Field | CRM | ZoomInfo | Action |
|---|---|---|---|---|
| C-EC3025 | employee_count | blank | 400 | **Fill → 400** |
| C-96039F | employee_count | blank | 400 | **Fill → 400** |
| C-44EA29 | employee_count | blank | 400 | **Fill → 400** |
| C-D04904 | employee_count | blank | 400 | **Fill → 400** |
| C-B23205 | employee_count | blank | 400 | **Fill → 400** |
| C-60C75F | employee_count | blank | 400 | **Fill → 400** |
| C-7BBDFA | employee_count | blank | 400 | **Fill → 400** |
| C-50D386 | employee_count | blank | 400 | **Fill → 400** |

### Disagreements (CRM ≠ enrichment — list both, recommend source):

| Company | Field | CRM Value | ZoomInfo Value | Recommend |
|---|---|---|---|---|
| C-66D1FC | industry | tech | Computer Software | **ZoomInfo** — "Computer Software" is a standard NAICS-based label; CRM's "tech" is informal |
| C-66D1FC | hq_country | US | United States | **ZoomInfo** — full country name is standard; "US" is an abbreviation |
| C-950043 | hq_country | US | United States | **ZoomInfo** — full name preferred |
| C-EC3025 | industry | Technology | Computer Software | **ZoomInfo** — more specific industry code |
| C-EC3025 | hq_country | USA | United States | **ZoomInfo** — "USA" is non-standard |
| C-96039F | hq_country | USA | United States | **ZoomInfo** |
| C-44EA29 | industry | tech | Computer Software | **ZoomInfo** |
| C-92D97D | industry | Technology | Computer Software | **ZoomInfo** |
| C-D04904 | industry | Technology | Computer Software | **ZoomInfo** |
| C-77A95A | industry | Technology | Computer Software | **ZoomInfo** |
| C-77A95A | hq_country | US | United States | **ZoomInfo** |
| C-AA8DDA | industry | Technology | Computer Software | **ZoomInfo** |
| C-B23205 | hq_country | US | United States | **ZoomInfo** |
| C-E51FB7 | hq_country | USA | United States | **ZoomInfo** |
| C-D0662E | hq_country | US | United States | **ZoomInfo** |
| C-B25F40 | industry | Technology | Computer Software | **ZoomInfo** |
| C-60C75F | industry | tech | Computer Software | **ZoomInfo** |
| C-425E2A | industry | Tech (trailing space) | Computer Software | **ZoomInfo** — trim and standardize |
| C-425E2A | hq_country | USA | United States | **ZoomInfo** |
| C-2D7423 | hq_country | USA | United States | **ZoomInfo** |

**General recommendation:** Use ZoomInfo as the authoritative source for industry (standardized NAICS/company taxonomy) and hq_country (full country name). CRM values use inconsistent casing, abbreviations, and trailing whitespace.

### No enrichment available (cannot fill):
C-93C8BF, C-BA969B, C-332637, C-EE9FFB, C-C9BB20, and the 4 acme-corp/globex aliases — all remain as-is with missing fields.

---

## 5. Top 10 Fixes (Ranked by Data Quality Impact — No Pipeline Data Available)

Since no deals/amount data was provided, I rank by severity of data quality issue:

| Rank | Fix | Type | Reasoning |
|---|---|---|---|
| **1** | **Merge C-0A092932 into C-0A092931** (acme-corp.com) | Duplicate | Same domain, two records — inflates company count |
| **2** | **Merge C-0A092934 into C-0A092933** (globex.io) | Duplicate | Same domain, two records |
| **3** | **Fill employee_count for C-EC3025 → 400** | Enrichment fill | Blank → ZoomInfo has it; unblocks employee-based scoring |
| **4** | **Fill employee_count for C-96039F → 400** | Enrichment fill | Same |
| **5** | **Fill employee_count for C-44EA29 → 400** | Enrichment fill | Same |
| **6** | **Fix CT-0010 email** (user0@) | Invalid email | Orphan email — no domain; cannot reach contact |
| **7** | **Fix CT-0011 email or re-associate** (user1@other-domain.com) | Domain mismatch | Email domain ≠ company domain; either wrong contact or wrong company link |
| **8** | **Fix CT-0080 and CT-0081 emails** (user0@, user1@) | Invalid emails | C-92D97D has 3 contacts, 2 with broken emails |
| **9** | **Fix CT-0192 email** (user2@) | Invalid email | C-425E2A contact unreachable |
| **10** | **Normalize industry labels across all companies** | Standardization | 6 variants in use: "tech", "Technology", "Tech ", "health care", "Healthcare", "SaaS" — should be one taxonomy (e.g., "Technology", "Healthcare", "SaaS") |

---

## Summary of Issues Found

| Issue Category | Count | Details |
|---|---|---|
| Duplicate companies | 2 clusters (4 total aliases) | acme-corp.com (2 records), globex.io (2 records) |
| Missing employee counts | 10 companies | 8 fillable from ZoomInfo, 2 unfillable (C-93C8BF, no enrichment) |
| Missing HQ countries | 6 companies | 0 fillable from ZoomInfo (all blanks in enrichment too) |
| Invalid emails | 4 contacts | CT-0010, CT-0080, CT-0081, CT-0192 |
| Domain mismatches | 1 contact | CT-0011 |
| Industry label variants | 6 distinct forms | tech / Technology / Tech / health care / Healthcare / SaaS |
| Companies without enrichment | 9 | C-93C8BF, C-BA969B, C-332637, C-EE9FFB, C-C9BB20, C-0A092931, C-0A092932, C-0A092933, C-0A092934 |

**Data missing entirely:** Deals table (owner, stage, amount, close date, why-buys) — cannot prioritize fixes by pipeline amount. If you provide the deals CSV, I can re-rank the top 10 by the actual dollar value at stake per company.
deal-intelligence 5 tests
closed-lost-classification0.80
Plain-English question: Why did we lose the last six months of deals — one primary category per deal, and whose side is the reason on?
Prompt — what the model saw
Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.
cost $0.0470 · 577s · in 9,269 / out 20,375 tok
✗ total_90✓ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

Plain-English answer: Every closed-lost deal gets exactly one category (pricing, competitor, no decision, timing, product gap, champion left, other) plus a side, per the computed classification.
The answer in one line: total_lost_6mo = 90; top_reason = competitor (26)
total_lost_6mo90
categoriespricing, competitor, no decision, timing, product gap, champion left, other
patternstwo most actionable, tag-vs-text disagreements counted
sideBonusly / buyer / unknown per deal
top_reasoncompetitor (26)
graded checkstotal_90 · competitor_top · category_counts_present · side_split · two_patterns

Model's actual answer

show the model's full answer
Here's the full analysis.

---

## Classification of 81 Unique Deals (2 duplicate rows excluded)

### 1. Category Counts

| Category | Count | % of Total |
|---|---|---|
| **Timing** | 26 | 32% |
| **Competitor** | 25 | 31% |
| **No decision** | 22 | 27% |
| **Pricing** | 4 | 5% |
| **Product gap** | 2 | 2% |
| **Other** | 2 | 2% |
| **Champion left** | 0 | 0% |

**Timing** (26 deals) — buyer wants to revisit in 6–12+ months; includes "pause," "Q2 next year," "2027," "2028," "on hold." Largest bucket. Representative aliases: Deal-DB0AAC, Deal-91A056, Deal-29326C, Deal-E6E80A, Deal-BB78F3, Deal-9F176A, Deal-7ED004 (tagged Budget/Price but text says "did not get budget approval" → timing/budget overlap).

**Competitor** (25 deals) — buyer chose another vendor. Named competitors: Rippl, HeyTaco, Kudos, WorkTango, HiThrive, Awardco, Motivosity, Paylocity, Nectar, ADP TotalSource partner, Canadian provider, "another platform" (unnamed). Representative aliases: Deal-F7F635, Deal-422BA6, Deal-DDAB52, Deal-ACE061, Deal-0F96AA, Deal-A2C349, Deal-242273, Deal-1E7DA9, Deal-DC77FE.

**No decision** (22 deals) — MIA / unresponsive / lost decision maker / explicit disinterest. No competitive loss, no postponement, just stalled or ghosted. Representative aliases: Deal-AC944F, Deal-214060, Deal-21B045, Deal-F308CA, Deal-4664E1, Deal-FAC17C, Deal-ABD14C, Deal-B4B50F.

**Pricing** (4 deals) — budget cuts, no approval, wanted simpler/cheaper. Deal-7ED004 ($60K), Deal-C33D91 ($7.2K), Deal-8A119B ($3.25K), Deal-7B2236 ($72K — "simpler and cheaper").

**Product gap** (2 deals) — Deal-8E27DA (wanted R&R but chose swag-only provider), Deal-981AD4 ("doesn't fit UI and not UK focused").

**Other** (2 deals) — Deal-5DB9B0 (spam lead, tagged ICP misfit), Deal-2A292B (building internally).

**Champion left** — 0. No deal text mentions a champion leaving the buying org.

---

### 2. Side Split

| Side | Count | % |
|---|---|---|
| **Buyer** | 63 | 78% |
| **Unknown** | 18 | 22% |

The "unknown" side is entirely MIA deals — no response means no insight into buyer's reasoning. Every other bucket has a clear buyer-side signal.

---

### 3. Tag-vs-Text Disagreements: **6 clear cases**

| Deal Alias | Tag Says | Text Says | Problem |
|---|---|---|---|
| Deal-5DB9B0 | Lost- Does not fit ICP | "Spam." | Tag implies a legitimate lead that didn't fit; text says it was spam. Should have been disqualified, not classified as an ICP misfit. |
| Deal-B3ABED | Lost- Timing (1 year+) | "MIA — We'll revisit... get budget for 2028" | Tag says timing, but the rep recorded MIA in the text. The buyer was actually unresponsive. |
| Deal-ED9AE7 | Lost DM | "Timing, budget, authority." | Tag says lost decision maker; text says timing/budget. The DM was reachable; the problem was budget and timing. |
| Deal-9048EB | MIA | "bad fit... multiple feature gaps" | Tag says MIA, but the rep knew the real reason: product gaps. Not truly MIA — a known product loss. |
| Deal-3618CC | Lost DM | "Wanted Surveys" | Tag says lost decision maker; text says product gap (surveys feature). The rep knew what was missing. |
| Deal-5AD03E | Competitor | "Wanted more defined budget access" | Tag says competitor; text says budget/pricing. Budget was the blocker, not a competing platform. |

**Not counted as disagreements** (borderline but consistent): Deal-DDAB52 (Competitor tag, "Rippl offers more at same cost" — still a competitor loss); Deal-1BCA50 (Competitor tag, "mostly about budget... other vendor" — competitor was the mechanism, budget was the factor; hybrid but not clearly wrong).

**Total: 6/81 deals (7%) have a structured tag that directly contradicts the free-text reason.**

---

### 4. Two Patterns Most Worth Acting On

**Pattern A: Timing is a catch-all for "not now, maybe later" — but many won't come back.**
26 deals (32%) are tagged timing, carrying $340K+ in pipeline. The free-text repeatedly says "revisit in 2027" or "next year," but there's no structured follow-up mechanism. Without a nurture cadence (quarterly check-ins, content drops, product updates), these are effectively dead pipeline that masquerades as "still warm." **Action: Build a timed re-engagement workflow at 3/6/9 months. Tag timing deals with a re-engage date, not an open-ended "later."**

**Pattern B: MIA is being used as a default bucket when the rep actually knew the reason.**
3 of the 6 tag-text disagreements involve MIA tags where the free-text reveals a specific cause (product gap, budget). This inflates the MIA count and hides root causes. Deal-9048EB ($41.8K) was lost to feature gaps but tagged MIA — that's a product signal management will never see. **Action: Train reps to use the most specific tag available, not MIA, when they know why. Add a "MIA — reason unknown" vs "MIA — known cause" sub-tag to preserve visibility.**
pipeline-tiering0.80
Plain-English question: Score all 156 open deals into exactly one of LOCK / ACTION / BUILD / REVIVE / WATCH / RISKY.
Prompt — what the model saw
Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.0192 · 57s · in 17,294 / out 2,865 tok
✓ json_returned✓ tier_vocabulary✗ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

Plain-English answer: All 156 open deals tiered exactly once across the six tiers using forecast category, stage, meetings, contacts, and recency.
The answer in one line: total = 156
total156
graded checksjson_returned · tier_vocabulary · tier_counts_sum · risky_deals_listed · no_lock_violation_claimed

Model's actual answer

show the model's full answer
```json
{
  "tier_counts": {
    "LOCK": 7,
    "ACTION": 15,
    "BUILD": 22,
    "REVIVE": 14,
    "WATCH": 49,
    "RISKY": 22
  },
  "tier_examples": {
    "LOCK": ["Deal-D348E1", "Deal-C26D20", "Deal-25F752"],
    "ACTION": ["Deal-403845", "Deal-547B2B", "Deal-944310"],
    "BUILD": ["Deal-499BF6", "Deal-036E80", "Deal-E25A09"],
    "REVIVE": ["Deal-9AAE5F", "Deal-6787C2", "Deal-584EE5"],
    "WATCH": ["Deal-66D1FC", "Deal-523604", "Deal-1FC049"],
    "RISKY": ["Deal-E53952", "Deal-5408B0", "Deal-2465CE"]
  },
  "risky_deals": ["Deal-E53952", "Deal-5408B0", "Deal-2465CE", "Deal-9DDE86", "Deal-C61CF7", "Deal-BA3DDC", "Deal-7BBDFA", "Deal-8952F0", "Deal-5FDCE4", "Deal-215CCA", "Deal-1E2498", "Deal-7599B8", "Deal-15D24F", "Deal-8AD4A5", "Deal-4A13AD", "Deal-EE195F", "Deal-46988D", "Deal-55164C", "Deal-3BA5EA", "Deal-ED725A", "Deal-635B8E", "Deal-9D0060"],
  "lock_violations": 0,
  "pipeline_shape": "129 deals totaling ~$1.87M in open pipeline, heavily weighted toward early-stage DS1-DS2 WATCH items (38%) and mid-funnel DS3 ACTION/BUILD deals. Only 5.4% of deals are LOCK — all COMMIT DS5s with active meetings — while 17% are flagged RISKY for forecast-engagement mismatch, predominantly BEST_CASE deals in DS3-DS5 with zero meetings_30d. 11% are REVIVE candidates with LONG gaps needing re-engagement. The pipeline is top-heavy: 49 WATCH deals suggest weak qualification funneling into later stages without sufficient engagement velocity."
}
```
call-transcript-extractionPASS 1.00
Plain-English question: Extract the CRM write-back fields from each call transcript as JSON.
Prompt — what the model saw
For each transcript, extract CRM write-back fields as JSON: why-buys (prospect statements only), pain points, stakeholders from the speaker list, budget signal (prospect-stated or null), timeline signal, competitor mentioned (only if the prospect raised it), next step (only if explicitly agreed), objections, confidence. Never fill a field from what the rep said or hoped.
cost $0.0074 · 50s · in 1,124 / out 2,544 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

Plain-English answer: Per transcript: prospect-only why-buys, pain points, stakeholders, budget and timeline signals, and competitors — e.g. TX-001: '$40k earmarked', live before open enrollment.
The answer in one line: rep = Alex Franklin
allowed_newTX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar
per_transcript{"TX-001": {"why_buys": ["automating anniversary and birthday awards"], "pain_points": ["HR team of three cannot keep up with it manually", "track everything in a spreadsheet, and people slip through
repAlex Franklin
rep_sourced_nulls{"TX-003": ["budget_signal \u2014 only the rep mentioned pricing/budget"], "TX-004": ["next_step \u2014 rep proposed, prospect said 'no promises'"], "TX-006": ["budget_signal \u2014 rep decoy, prospec
graded checksjson_emitted · budget_signal · null_discipline · stakeholders · competitor_prospect_only

Model's actual answer

show the model's full answer
```json
{
  "transcripts": [
    {
      "transcript_id": "TX-001",
      "deal_alias": "Deal-CFE7F4",
      "why_buys": ["automating anniversary and birthday awards"],
      "pain_points": ["HR team of three cannot keep up with anniversary/birthday awards manually", "spreadsheet tracking causes people to slip through cracks"],
      "stakeholders": ["VP People", "HR Admin"],
      "budget_signal": "$40k earmarked for engagement tools this fiscal year",
      "timeline_signal": "live before open enrollment in November",
      "competitor_mentioned": "Achievers (prospect evaluated last year, deemed too heavy for their team size)",
      "next_step": "security review on September 12",
      "objections": ["need SSO and audit logs for IT sign-off"],
      "confidence": "Medium — budget and timeline are clear and next step is booked, but the security review outcome and IT sign-off are still gatekeepers"
    },
    {
      "transcript_id": "TX-002",
      "deal_alias": "Deal-70BB30",
      "why_buys": ["tie recognition to retention for hourly workforce"],
      "pain_points": ["regretted turnover over 30% among hourly workforce"],
      "stakeholders": ["Head of Total Rewards", "CFO"],
      "budget_signal": "$25k pilot budget approved for this quarter",
      "timeline_signal": "decision by end of September",
      "competitor_mentioned": null,
      "next_step": "send pilot agreement; prospect will route to legal this week",
      "objections": ["Workday integration must be rock solid (CFO's condition)"],
      "confidence": "High — budget approved, timeline set, first real vendor they've demoed with, and next step is contract routing"
    },
    {
      "transcript_id": "TX-003",
      "deal_alias": "Deal-530B50",
      "why_buys": ["make recognition visible across 12 retail locations"],
      "pain_points": ["store managers have zero budget autonomy for on-the-spot recognition"],
      "stakeholders": ["People Ops Manager"],
      "budget_signal": null,
      "timeline_signal": "no rush until Q1",
      "competitor_mentioned": "Bucketlist (CEO used it at previous company and liked it)",
      "next_step": "schedule a call with the CEO (People Ops Manager will send two times)",
      "objections": ["CEO must be sold first — she decides everything people-related"],
      "confidence": "Low — no budget stated, no urgency until Q1, CEO is a gatekeeper who prefers a competitor's product, and only one stakeholder engaged so far"
    },
    {
      "transcript_id": "TX-004",
      "deal_alias": "Deal-180D02",
      "why_buys": ["consolidate three separate recognition tools into one"],
      "pain_points": ["paying for three tools that don't integrate with their HRIS"],
      "stakeholders": ["VP People", "IT Security Lead"],
      "budget_signal": "under $15k annually (conditional — VP People can approve without board if under this threshold)",
      "timeline_signal": "procurement cycle runs six to eight weeks minimum",
      "competitor_mentioned": null,
      "next_step": null,
      "objections": ["security review took three months for last vendor — hesitation from IT Security Lead"],
      "confidence": "Low-Medium — conditional budget signal exists but procurement is long, security review is a known friction point, and the proposed next step (CFO follow-up) was not agreed ('Maybe — no promises')"
    },
    {
      "transcript_id": "TX-005",
      "deal_alias": "Deal-F8767A",
      "why_buys": ["automate service milestones", "get analytics on recognition equity across departments"],
      "pain_points": ["night-shift teams feel invisible; engagement scores run 20 points lower"],
      "stakeholders": ["HR Director", "People Ops Coordinator"],
      "budget_signal": "$12k approved under engagement line",
      "timeline_signal": "need this running before January all-hands",
      "competitor_mentioned": "Nectar (mid-pilot with them — rep must beat that experience)",
      "next_step": "present to exec team on October 2",
      "objections": ["exec team is skeptical after a failed rollout two years ago"],
      "confidence": "Medium — budget and timeline are set, next step booked, but they're mid-pilot with Nectar and exec skepticism is a real hurdle"
    },
    {
      "transcript_id": "TX-006",
      "deal_alias": "Deal-EE195F",
      "why_buys": ["cut admin time on service awards"],
      "pain_points": ["HR Manager personally spends five hours a month ordering and shipping plaques"],
      "stakeholders": ["HR Manager"],
      "budget_signal": null,
      "timeline_signal": "fiscal year resets in February; Q1 start is realistic",
      "competitor_mentioned": null,
      "next_step": "send one-page overview; HR Manager will forward to COO this week",
      "objections": ["COO usually prefers building things in-house"],
      "confidence": "Medium — budget is not the blocker but no amount stated, timeline is clear, next step agreed, but COO's in-house preference is a significant unknown"
    }
  ]
}
```
next-to-closePASS 1.00
Plain-English question: Which 3 deals are closest to signature right now?
Prompt — what the model saw
Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0052 · 26s · in 398 / out 1,298 tok
✓ exactly_three✓ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

Plain-English answer: Deal-547B2B ($11,200), Deal-B7EBD1 ($9,000), and Deal-403845 ($9,000) — all DS5 COMMIT deals closing September 10-11, with what is left on each.
blocked_excludeddeal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)
exactly_three1
ranking_ruleSlack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc
top3rank=1; deal_alias=Deal-547B2B; amount=11200; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11 | rank=2; deal_alias=Deal-B7EBD1; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-10 | rank=3; deal_alias=Deal-403845; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11
graded checksexactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

show the model's full answer
Here are the 3 deals closest to signature, using Slack as the override source:

**1. Deal-547B2B ($11,200)** — Alex Franklin. Slack: "redlines came back clean this morning, the signing page is out, and their VP People told me they are signing tomorrow." CRM shows DS5/COMMIT closing 2026-09-11. Nothing left but the ink.

**2. Deal-403845 ($9,000)** — Dana Mercer. Slack: "order form is with their finance team." CRM shows DS5/COMMIT closing 2026-09-11. No blockers reported. Waiting on finance approval.

**3. Deal-B7EBD1 ($9,000)** — Dana Mercer. CRM shows DS5/COMMIT closing 2026-09-10. Not mentioned in Slack — no news is good news. Still on track as the earliest-closing deal by date.

**Notable drop: Deal-2465CE ($5,400, DS5/COMMIT closing 2026-09-10)** — Slack confirms it went sideways: champion left, procurement froze new vendors. Dana is pulling it from commit. Not close despite CRM date.

Total pipeline value of these 3: $29,200. No invented numbers — all from the CSV and Slack.

**Remaining work:** 547B2B needs physical ink; 403845 needs finance sign-off; B7EBD1 status is unknown (no Slack update).
product-gap-detectionPASS 1.00
Plain-English question: Which prospect complaints are true product gaps versus rollout or enablement issues?
Prompt — what the model saw
Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.
cost $0.0049 · 26s · in 0 / out 1,327 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

Plain-English answer: Two real gaps (SCIM provisioning, ADP Workforce Now integration) separated from plan gates and enablement issues, each with the prospect quote and deal.
allowed_newTG-001, TG-002, TG-003, TG-004, TG-005, SCIM user provisioning, ADP Workforce Now integration, Custom report builder
enablement_issuestranscript_id=TG-004; deal_alias=Deal-84DBA6; amount=16000; feature=Slack integration recognition adoption; quote=We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.; why_enablement=Slack integration exists on all plans; managers were never trained
excluded_rep_voicetranscript_id=TG-005; line=Full transparency — we don't have a native mobile app for hourly workers yet, though it's on the roadmap.; reason=rep voice, not prospect-raised
plan_gatestranscript_id=TG-003; deal_alias=Deal-CFE7F4; amount=32000; feature=Custom report builder; tier_required=Enterprise; quote=I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?
real_gapstranscript_id=TG-001; deal_alias=Deal-EC3025; amount=62000; feature=SCIM user provisioning; quote=We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.; why_real=not listed in product docs at any tier | transcript_id=TG-002; deal_alias=Deal-D0D6B5; amount=23800; feature=ADP Workforce Now integration; quote=Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.; why_real=supported HRIS list is Workday/BambooHR/Gusto/Rippling only
graded checksreal_gaps · plan_gate_classified · enablement_classified · gap_summary

Model's actual answer

show the model's full answer
## Product Gap Analysis

### Candidate 1 — Deal-EC3025

| Field | Detail |
|---|---|
| **Prospect quote** | "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." |
| **Classification** | **Real gap** — SCIM is not listed as a supported capability in the product docs (no mention across any tier). The prospect explicitly ties it to a security approval gate. No upgrade path resolves it. |
| **Deal** | Deal-EC3025 (no amount provided in data) |

### Candidate 2 — Deal-D0D6B5

| Field | Detail |
|---|---|
| **Prospect quote** | "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us." |
| **Classification** | **Real gap** — ADP Workforce Now is explicitly called out as NOT supported in the product docs. Prospect labels it a dealbreaker. No tier or plan adds it. |
| **Deal** | Deal-D0D6B5 (no amount provided in data) |

### Candidate 3 — Deal-CFE7F4

| Field | Detail |
|---|---|
| **Prospect quote** | "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?" |
| **Classification** | **Plan gate** — The custom report builder exists on the Enterprise tier. The prospect's current plan lacks it, but an upgrade path exists. Not a missing capability. |
| **Deal** | Deal-CFE7F4 (no amount provided in data) |

### Candidate 4 — Deal-84DBA6

| Field | Detail |
|---|---|
| **Prospect quote** | "We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it." |
| **Classification** | **Rollout/enablement issue** — Slack integration is a supported feature on all plans. The problem is adoption/training, not a product gap. |
| **Deal** | Deal-84DBA6 (no amount provided in data) |

### Candidate 5 — Deal-36C33F

| Field | Detail |
|---|---|
| **Prospect quote** | *No prospect-raised gap.* Alex Franklin volunteers the missing mobile app, but the prospect responds "the web version should be fine." |
| **Classification** | **Excluded** — Rep language does not count as prospect voice. The prospect did not raise a gap. |
| **Deal** | Deal-36C33F |

---

### Summary — True Product Gaps

Only **two** real product gaps surfaced:

1. **SCIM user provisioning** — Deal-EC3025. The product has no SCIM support at any tier. The prospect's security team requires it for approval.
2. **ADP Workforce Now integration** — Deal-D0D6B5. The product does not support ADP. The prospect calls it a dealbreaker.

**Note:** No deal amounts were present in the provided data for any transcript.
rep-performance 5 tests
stale-pipeline-by-rep0.50
Plain-English question: Which open deals have had no email, call, or meeting in the last 7 days?
Prompt — what the model saw
The snapshot date is 2026-09-05. Using the open deals and the engagements-by-deal table, list every open deal with no logged email, call, or meeting in the last 7 days. Compute recency from the engagements table's last_email/last_call/last_meeting fields, NOT from any date field on the deal (the deal's last_contacted_field is unreliable). For each stale deal give: deal alias, owner name, stage, amount, days since last contact. Group by owner, order by amount descending within each owner. Then state, per owner, how many stale deals they carry and the total stale amount. (Do not write outreach text.)
cost $0.0329 · 441s · in 9,320 / out 15,607 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✗ kevin_stale_amt✗ jenn_stale_amt✗ stale_count

✓ Correct Answer (ground truth)

Plain-English answer: 65 open deals are stale; the largest is Deal-2D1F1B at $240,000, with Bryce Harmon carrying the most stale dollars at $626,243.
The answer in one line: jake_amt = 626,243; jenn_amt = 261,645; kevin_amt = 252,905.03; top_amount = 240,000; stale_count = 65; top_alias = Deal-2D1F1B
jake_amt626,243
jenn_amt261,645
kevin_amt252,905.03
stale_count65
top_amount240,000
top_aliasDeal-2D1F1B
graded checksstale_recall_ge_80 · grouped_by_owner · jake_top_amount · kevin_stale_amt · jenn_stale_amt · stale_count

Model's actual answer

show the model's full answer
Snapshot date: 2026-09-05. "Last 7 days" = 2026-08-30 through 2026-09-05. A deal is stale when the most recent of last_email, last_call, last_meeting (from engagements_by_deal_90d.csv) is before 2026-08-30, or when no engagement row exists.

---

### Bryce Harmon (13 stale deals, $626,243 total)

| Deal alias | Stage | Amount | Days since last contact |
|---|---|---|---|
| Deal-2D1F1B | DS1 | $240,000 | 81 (last contact 2026-06-16) |
| Deal-66D1FC | DS1 | $99,000 | 16 (last contact 2026-08-20) |
| Deal-950043 | DS1 | $70,000 | 19 (last contact 2026-08-17) |
| Deal-B23205 | DS1 | $45,000 | 16 (last contact 2026-08-20) |
| Deal-7BBDFA | DS3 | $37,440 | 46 (last contact 2026-07-21) |
| Deal-332637 | DS2 | $36,000 | 9 (last contact 2026-08-27) |
| Deal-1BEEBF | DS1 | $31,500 | 19 (last contact 2026-08-17) |
| Deal-C5658B | DS1 | $23,400 | 16 (last contact 2026-08-20) |
| Deal-40522D | DS3 | $21,000 | 19 (last contact 2026-08-17) |
| Deal-F0EBBB | DS3 | $11,400 | 24 (last contact 2026-08-12) |
| Deal-E25A09 | DS1 | $6,000 | 9 (last contact 2026-08-27) |
| Deal-C9C286 | DS2 | $5,502 | 9 (last contact 2026-08-27) |
| Deal-012CB1 | DS1 | $1 | 23 (last contact 2026-08-13) |

### Alex Franklin (13 stale deals, $57,540 total)

| Deal alias | Stage | Amount | Days since last contact |
|---|---|---|---|
| Deal-885F45 | DS2 | $9,300 | 12 (last contact 2026-08-24) |
| Deal-B7EBD1 | DS5 | $9,000 | 16 (last contact 2026-08-20) |
| Deal-3EED2C | DS2 | $7,200 | N/A — no engagement row in engagements table |
| Deal-0D2F7A | DS3 | $5,100 | 12 (last contact 2026-08-24) |
| Deal-6C60D4 | DS3 | $4,800 | 12 (last contact 2026-08-24) |
| Deal-9D0060 | DS3 | $3,840 | 12 (last contact 2026-08-24) |
| Deal-690476 | DS2 | $3,600 | 18 (last contact 2026-08-18) |
| Deal-C6D97A | DS4 | $3,240 | 8 (last contact 2026-08-28) |
| Deal-EE195F | DS3 | $3,120 | 8 (last contact 2026-08-28) |
| Deal-278DEC | DS3 | $2,700 | 8 (last contact 2026-08-28) |
| Deal-6883F3 | DS1 | $2,400 | 16 (last contact 2026-08-20) |
| Deal-4A13AD | DS3 | $2,160 | 26 (last contact 2026-08-10) |
| Deal-BA571A | DS4 | $1,080 | 18 (last contact 2026-08-18) |

### Dana Mercer (10 stale deals, $224,445 total)

| Deal alias | Stage | Amount | Days since last contact |
|---|---|---|---|
| Deal-44EA29 | DS2 | $60,000 | 10 (last contact 2026-08-26) |
| Deal-E51FB7 | DS2 | $43,875 | 12 (last contact 2026-08-24) |
| Deal-B42F46 | DS1 | $27,000 | 19 (last contact 2026-08-17) |
| Deal-BA3DDC | DS3 | $23,400 | 15 (last contact 2026-08-21) |
| Deal-9DDE86 | DS2 | $20,000 | 15 (last contact 2026-08-21) |
| Deal-215CCA | DS3 | $18,900 | 17 (last contact 2026-08-19) |
| Deal-5EED42 | DS3 | $16,250 | 11 (last contact 2026-08-25) |
| Deal-F40F04 | DS2 | $8,100 | 15 (last contact 2026-08-21) |
| Deal-87DDD1 | DS1 | $5,000 | 19 (last contact 2026-08-17) |
| Deal-0660B4 | DS4 | $1,920 | 16 (last contact 2026-08-20) |

### Cole Ingram (15 stale deals, $186,900.98 total)

| Deal alias | Stage | Amount | Days since last contact |
|---|---|---|---|
| Deal-B25F40 | DS3 | $40,000 | 8 (last contact 2026-08-28) |
| Deal-813836 | DS2 | $32,175 | 11 (last contact 2026-08-25) |
| Deal-1BA595 | DS2 | $31,750 | 11 (last contact 2026-08-25) |
| Deal-CFE1E8 | DS3 | $18,000 | 11 (last contact 2026-08-25) |
| Deal-CD47A6 | DS2 | $12,168 | 11 (last contact 2026-08-25) |
| Deal-627646 | DS3 | $11,193 | 11 (last contact 2026-08-25) |
| Deal-FF809F | DS2 | $7,781.20 | 11 (last contact 2026-08-25) |
| Deal-AF932D | DS2 | $7,225.40 | 11 (last contact 2026-08-25) |
| Deal-A71728 | DS2 | $6,947.50 | 11 (last contact 2026-08-25) |
| Deal-8BC9F5 | DS2 | $5,616 | 10 (last contact 2026-08-26) |
| Deal-175395 | DS3 | $4,779.88 | 11 (last contact 2026-08-25) |
| Deal-C7F9BF | DS2 | $3,360 | 11 (last contact 2026-08-25) |
| Deal-342E96 | DS2 | $2,700 | 24 (last contact 2026-08-12) |
| Deal-E568D5 | DS3 | $1,875 | 11 (last contact 2026-08-25) |
| Deal-FD9F4E | DS5 | $1,330 | 10 (last contact 2026-08-26) |

### Elena Sinclair (1 stale deal, $2,100 total)

| Deal alias | Stage | Amount | Days since last contact |
|---|---|---|---|
| Deal-57FF13 | DS1 | $2,100 | N/A — no engagement row in engagements table |

### Farid Osman — 0 stale deals

---

**Summary by owner:**

| Owner | Stale deals | Total stale amount |
|---|---|---|
| Bryce Harmon | 13 | $626,243 |
| Alex Franklin | 13 | $57,540 |
| Dana Mercer | 10 | $224,445 |
| Cole Ingram | 15 | $186,900.98 |
| Elena Sinclair | 1 | $2,100 |
| Farid Osman | 0 | $0 |
activity-mix-vs-outcomePASS 1.00
Plain-English question: Per rep, what did the last 30 days of emails, calls, and meetings look like next to the deals that advanced?
Prompt — what the model saw
The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.0332 · 57s · in 29,071 / out 3,754 tok
✓ alex_ds2_30d✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

Plain-English answer: Alex Franklin is the standout signal with 18 deals entering DS2 in 30 days (next closest: Bryce Harmon with 4), alongside per-rep activity totals.
The answer in one line: alex_ds2_30d = 18; bryce_ds2_30d = 4; note = legacy alias justin_ds2=4 actually held Bryce Harmon count; Alex Franklin entered 18 deals into DS2 in the 30d window
alex_ds2_30d18
bryce_ds2_30d4
notelegacy alias justin_ds2=4 actually held Bryce Harmon count; Alex Franklin entered 18 deals into DS2 in the 30d window
graded checksalex_ds2_30d · justin_volume_high · efficient_named · mix_present · no_causal_claim

Model's actual answer

show the model's full answer
## Rep Activity & DS2 Efficiency Report (Last 30 Days: 2026-08-06 to 2026-09-05)

### Per-Rep Activity Totals & Mix

**Alex Franklin (84342457)**
- Emails: 307 (79.9%), Calls: 36 (9.4%), Meetings: 41 (10.7%)
- Total activities: 384, DS2 entries (30d): 18
- **21.3 activities per DS2 entry**

**Bryce Harmon (119337721)**
- Emails: 162 (79.0%), Calls: 0 (0.0%), Meetings: 43 (21.0%)
- Total activities: 205, DS2 entries (30d): 4
- **51.2 activities per DS2 entry**

**Cole Ingram (83155924)**
- Emails: 96 (86.5%), Calls: 14 (12.6%), Meetings: 1 (0.9%)
- Total activities: 111, DS2 entries (30d): 2
- **55.5 activities per DS2 entry**

**Farid Osman (716654662)**
- Emails: 38 (52.8%), Calls: 0 (0.0%), Meetings: 34 (47.2%)
- Total activities: 72, DS2 entries (30d): 1
- **72.0 activities per DS2 entry**

**Dana Mercer (83155923)**
- Emails: 84 (74.3%), Calls: 18 (15.9%), Meetings: 11 (9.7%)
- Total activities: 113, DS2 entries (30d): 1
- **113.0 activities per DS2 entry**

**Elena Sinclair (701163055)** — 0 activities, 0 DS2 entries. Excluded from ranking.

### Efficiency Ranking (lowest activities per DS2 = most efficient)

| Rank | Rep | Acts/DS2 | DS2 Entries | Total Acts |
|------|-----|---------|-------------|-----------|
| 1 | Alex Franklin | 21.3 | 18 | 384 |
| 2 | Bryce Harmon | 51.2 | 4 | 205 |
| 3 | Cole Ingram | 55.5 | 2 | 111 |
| 4 | Farid Osman | 72.0 | 1 | 72 |
| 5 | Dana Mercer | 113.0 | 1 | 113 |

### Summary

- **Most efficient rep: Alex Franklin** — 21.3 activities per DS2 entry (18 DS2 entries in 30 days, 384 total activities)
- **Highest-volume rep: Alex Franklin** — 384 total activities over 30 days
- **They are the same rep.** Alex Franklin leads both in raw activity volume and efficiency, moving 18 deals to DS2 with the lowest activity-per-DS2 ratio.
ae-qtd-scorecard0.83
Plain-English question: Build Alex Franklin's QTD scorecard: bookings versus quota, pipeline, and conversion.
Prompt — what the model saw
Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.0421 · 586s · in 21,806 / out 13,618 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✗ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

Plain-English answer: 75% attainment ($150,000 on a $200,000 quota), $113,500 new versus $36,500 expansion, pipeline by stage, and the rolling DS2-to-won rate.
The answer in one line: quota = 200000; bookings = 150000; new_bookings = 113500; expansion_bookings = 36500; open_deals_count = 125; attainment_pct = 75; owner = Alex Franklin (+12 more below)
quota200,000
attainment_pct75
bookings150,000
expansion_bookings36,500
expansion_count3
losses_qtd27
new_bookings113,500
new_count5
open_deals_count125
wins_qtd8
activity_30d_totalsemails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50
allowed_new18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2
ds2_to_wonwindow_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286
ownerAlex Franklin
pipeline_by_stage{"DS1": {"count": 20, "amount": 284621}, "DS2": {"count": 28, "amount": 353760}, "DS3": {"count": 67, "amount": 552705}, "DS4": {"count": 5, "amount": 23574}, "DS5": {"count": 5, "amount": 45730}}
prior_quarter_excludeddeal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000
quarter2026-Q3
snapshot_date2026-09-05
top_loss_reasonreason=Lost- Timing (1 year or more); count=13
graded checksbookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

show the model's full answer
**Alex Franklin QTD Scorecard (as of 2026-09-05)**

**Bookings vs Quota**

| Metric | Value |
|--------|-------|
| Bookings | $150,000 |
| Quota | $200,000 |
| Attainment | 75.0% |

$150,000 / $200,000 = 0.75

**New vs Expansion Split**

| Type | Amount | % of Bookings |
|------|--------|---------------|
| New | $113,500 | 75.7% |
| Expansion | $36,500 | 24.3% |

New deals: Deal-A1C3E5 ($40K), Deal-B7D2F4 ($35K), Deal-C9E1A6 ($21K), Deal-D4B8C2 ($11K), Deal-E6F3A9 ($6.5K) = $113,500
Expansion deals: Deal-F2C7D8 ($20K), Deal-A8B4D6 ($12K), Deal-C5D9E2 ($4.5K) = $36,500

**Active Pipeline by Stage**

| Stage | Amount | Deal Count | Key Deals |
|------|--------|-----------|-----------|
| DS1 | $113,505 | 10 | Deal-60C75F ($40K), Deal-87C1AC ($20K), Deal-CC50C1 ($16.6K) |
| DS2 | $239,060 | 21 | Deal-EC3025 ($62K), Deal-92D97D ($60K), Deal-84DBA6 ($16K), Deal-36C33F ($15K) |
| DS3 | $407,201 | 50 | Deal-70BB30 ($30K), Deal-530B50 ($31.2K), Deal-EDC141 ($18K), Deal-E73427 ($18K), Deal-B936FE ($18K), Deal-4F775F ($18K), Deal-1E2498 ($16.7K), Deal-E0B692 ($16.2K) |
| DS4 | $7,644 | 3 | Deal-F9A08A ($2.5K), Deal-C6D97A ($3.2K), Deal-1FC049 ($1.9K) |
| DS5 | $45,730 | 5 | Deal-D348E1 ($13.8K), Deal-547B2B ($11.2K), Deal-403845 ($9K), Deal-A2B47C ($6.4K), Deal-C61CF7 ($5.4K) |
| **Total** | **$813,140** | **89** | |

**Rolling 90-Day DS2-to-Won Rate (2026-06-07 to 2026-09-05)**

| Metric | Value |
|--------|-------|
| Deals that entered DS2 in window | 96 |
| Won | 8 |
| Lost | 27 |
| Still open | 61 |
| **DS2-to-Won Rate (closed deals)** | **22.9%** |

8 / (8 + 27) = 22.9%

**Win/Loss Counts (Q3)**

| Outcome | Count | Total Amount |
|--------|-------|-------------|
| Won | 8 | $150,000 |
| Lost | 27 | $366,792 |

**Loss Reasons Breakdown**

| Loss Reason | Count | Total $ Lost |
|------------|-------|-------------|
| Lost - Timing (1 year or more) | 13 | $184,681 |
| Competitor | 5 | $49,020 |
| MIA | 5 | $45,831 |
| Lost DM | 2 | $17,940 |
| Feature Request | 1 | $21,000 |
| Lost - Does not fit ICP | 1 | $10,800 |

**Top Loss Reason:** Lost - Timing (1 year or more) — 13 of 27 losses (48.1%), $184,681

**Activity Volume (Last 30 Days)**

| Activity Type | Count |
|-------------|-------|
| Emails | 639 |
| Calls | 104 |
| Meetings | 103 |
| Notes | 46 |
| **Total** | **892** |

Ratio: ~6.1 emails per meeting, ~2.2 calls per meeting, 0.4 notes per meeting

---

**Three Coaching Observations**

1. **Timing losses are the #1 leak at 48% of all losses ($184.7K).** 13 deals were lost because the buying cycle is 1+ years out. This suggests weak qualification at the top of funnel — these deals should have been identified as "too early" before they consumed DS2+ resources. Recommend tightening DS1 qualification to flag long-cycle prospects and either nurture them out-of-funnel or deprioritize them until the buying window opens.

2. **DS2-to-won conversion at 22.9% is low for a $200K quota holder.** Only 8 of 35 closed deals that entered DS2 converted. With $239K sitting in DS2 today — including two $60K+ deals (Deal-EC3025, Deal-92D97D) — the math says those won't all close. If the current rate holds, only ~$55K of DS2 converts. Need to either improve qualification rigor at DS2 entry (gate more tightly) or increase activity velocity on DS2 deals to move them through before they stall.

3. **Activity mix is email-heavy and documentation-light.** 639 emails vs 103 meetings and 104 calls — a 6:1 ratio that suggests over-reliance on asynchronous outreach for a $200K book. More critically, only 46 notes logged across all deals in 30 days. For a rep carrying this much pipeline ($813K) with only 8 wins so far, thin documentation makes it hard to coach, forecast, or diagnose why deals stall. Recommend a minimum of 1 note per deal per week and at least 1 meeting per DS4/DS5 deal per week.
multithreading-gapPASS 1.00
Plain-English question: Which open deals are single-threaded or under-threaded?
Prompt — what the model saw
Find every open deal that is single-threaded (fewer than 2 active contacts) or under-threaded (fewer than 3, or all contacts in one persona). Active means engaged in the last 60 days and not marked former. Personas: economic buyer, champion, HR admin, IT security, finance. For each flagged deal: amount, stage, active contact count, personas present, personas missing, the most valuable persona to add given the stage, and any on-file unengaged contact at that company who fits; otherwise say none on file.
cost $0.0098 · 86s · in 1,100 / out 4,233 tok
✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

Plain-English answer: Deals with fewer than 2 active contacts (single-threaded) or fewer than 3, or all in one persona (under-threaded), each flagged with the missing persona.
The answer in one line: active_window_start = 2026-07-07
active_window_start2026-07-07
allowed_new59 items — e.g. C-077A0E, C-0D15DF, C-1BDFE0, C-2AE3AA
best_persona_ruleDS2/DS3: economic buyer > IT security > finance > HR admin > champion; DS4/DS5: economic buyer > finance > IT security > HR admin > champion; first persona not present among active contacts
flag_ruleflag if active_contacts < 2 (single-threaded) or active_contacts < 3 or all active contacts share one persona (under-threaded); active = engaged within 60 days and not former
flaggeddeal_alias=Deal-36C33F; amount=15000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-50D386; amount=36000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5408B0; amount=14850; stage=DS4; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5BFE3B; amount=18000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-885F45; amount=9300; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=IT security | deal_alias=Deal-92D97D; amount=60000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer … (+5 more)
healthy_not_flaggedDeal-4B0BEB, Deal-84DBA6, Deal-D348E1
persona_universeeconomic buyer, champion, HR admin, IT security, finance
graded checksflagged_recall · persona_language · single_thread_term · on_file_fit

Model's actual answer

show the model's full answer
## Threading Analysis (as of Sep 6, 2026)

**Note:** The provided CSV files contain no `amount` or `stage` columns. I cannot report dollar values or stage-based persona recommendations — those data are simply absent from the source files. "Most valuable persona to add given the stage" is therefore unanswerable from the available data.

---

### Flagged Deals

**1. Deal-EC3025** (C-FDD0C7) — Single-threaded
- **Active contacts:** 1 (champion)
- **Personas present:** champion
- **Personas missing:** economic buyer, HR admin, IT security, finance
- **Unengaged contact on file:** CT-6827DB — Chief People Officer (economic buyer) — fits a missing persona

**2. Deal-92D97D** (C-E23238) — Single-threaded
- **Active contacts:** 1 (HR admin)
- **Personas present:** HR admin
- **Personas missing:** economic buyer, champion, IT security, finance
- **Unengaged contact on file:** none

**3. Deal-50D386** (C-EB10E4) — Under-threaded (2 personas < 3)
- **Active contacts:** 2 (champion, HR admin)
- **Personas present:** champion, HR admin
- **Personas missing:** economic buyer, IT security, finance
- **Unengaged contact on file:** CT-A1C4B3 — Chief People Officer (economic buyer) — fits a missing persona

**4. Deal-D0D6B5** (C-32918E) — Under-threaded (all 3 contacts in champion persona; 1 persona < 3)
- **Active contacts:** 3 (all champion)
- **Personas present:** champion
- **Personas missing:** economic buyer, HR admin, IT security, finance
- **Unengaged contact on file:** CT-1FA4DB — Chief People Officer (economic buyer) — fits a missing persona

**5. Deal-5BFE3B** (C-535D36) — Under-threaded (both contacts in champion persona; 1 persona < 3)
- **Active contacts:** 2 (both champion)
- **Personas present:** champion
- **Personas missing:** economic buyer, HR admin, IT security, finance
- **Unengaged contact on file:** none

**6. Deal-36C33F** (C-077A0E) — Single-threaded
- **Active contacts:** 1 (IT security; the other 2 contacts are marked former)
- **Personas present:** IT security
- **Personas missing:** champion, economic buyer, HR admin, finance
- **Unengaged contact on file:** CT-1DB73E — Chief People Officer (economic buyer) — fits a missing persona

**7. Deal-885F45** (C-5E8EFB) — Under-threaded (2 personas < 3)
- **Active contacts:** 2 (economic buyer, champion)
- **Personas present:** economic buyer, champion
- **Personas missing:** HR admin, IT security, finance
- **Unengaged contact on file:** CT-B3F25D — IT Security Lead (IT security) — fits a missing persona

**8. Deal-FCBE5B** (C-737030) — Single-threaded
- **Active contacts:** 1 (champion)
- **Personas present:** champion
- **Personas missing:** economic buyer, HR admin, IT security, finance
- **Unengaged contact on file:** none

**9. Deal-5408B0** (C-2AE3AA) — Under-threaded (2 personas < 3)
- **Active contacts:** 2 (champion, HR admin)
- **Personas present:** champion, HR admin
- **Personas missing:** economic buyer, IT security, finance
- **Unengaged contact on file:** CT-07FA76 — Chief People Officer (economic buyer) — fits a missing persona

**10. Deal-C6D97A** (C-5A8FC2) — Under-threaded (all 3 contacts in champion persona; 1 persona < 3)
- **Active contacts:** 3 (all champion)
- **Personas present:** champion
- **Personas missing:** economic buyer, HR admin, IT security, finance
- **Unengaged contact on file:** none

**11. Deal-F9A08A** (C-0D15DF) — Single-threaded
- **Active contacts:** 1 (champion; the economic buyer contact last engaged Jun 20 — outside the 60-day window)
- **Personas present:** champion
- **Personas missing:** economic buyer, HR admin, IT security, finance
- **Unengaged contact on file:** CT-697541 — Chief People Officer (economic buyer) — fits a missing persona

---

### Not Flagged (adequate threading)

| Deal | Active contacts | Personas covered |
|---|---|---|
| **Deal-84DBA6** | 3 | champion, economic buyer, IT security |
| **Deal-4B0BEB** | 4 | champion, economic buyer, HR admin, finance |
| **Deal-D348E1** | 5 | champion, economic buyer, IT security, finance, HR admin |

---

**Summary:** 11 of 14 open deals are under-threaded or single-threaded. 6 of those have an unengaged contact on file who fits a missing persona (all economic buyer except Deal-885F45 which has an IT Security lead). The most common gap across flagged deals is **economic buyer** (9 of 11 missing it). Amount and stage data were not present in the supplied CSVs — without stage I cannot prioritize which persona is most valuable to add for each deal's phase.
talk-track-from-transcriptsPASS 1.00
Plain-English question: From this rep's last 10 calls: how do they open, handle objections, and how often do they lock a next step?
Prompt — what the model saw
From this rep's last 10 calls: what they lead with in the first five minutes, how they handle the three most common objections, how often a concrete next step is agreed (rate), and every competitor a prospect raised. Quote at most one line per finding and only lines that appear in the transcripts. End with two coaching notes.
cost $0.0091 · 64s · in 1,949 / out 3,339 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

Plain-English answer: 8 of 10 calls open with the same customer-win story; objection handling, next-step agreement rate, and every competitor raised, with quotes.
The answer in one line: n_transcripts = 10; objection_count = 3; rep = Alex Franklin (+3 more below)
n_transcripts10
objection_count3
allowed_new13 items — e.g. TT-001, TT-002, TT-003, TT-004
competitor_quotesAwardco=We're also in late talks with Awardco — their rewards catalog looks bigger than yours.; Kudos=How are you different from Kudos? Our CEO used them at her last company.
competitors_prospect_raisedAwardco, Kudos
next_stepagreed=7; total=10; rate=0.7
objectionsobjection=budget; count=4; quote=Honestly, budget is locked until next fiscal year — I can't add a new line item right now.; transcript_id=TT-001 | objection=timing; count=3; quote=This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.; transcript_id=TT-002 | objection=in-house; count=3; quote=We already do recognition with a spreadsheet and quarterly gift cards — why would we change?; transcript_id=TT-004
opener_themetheme=opens with a customer recognition win story (400-person retailer, 18% turnover reduction); count=8; total=10; quote=Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.; transcript_id=TT-001
repAlex Franklin
rep_raised_competitors_excludedWorkhuman
graded checksobjection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer
## Rep Analysis — Alex Franklin's Last 10 Calls

### Lead-in (first 5 minutes)

**8 of 10 calls** open with the same story:
> "Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it." — TT-001 (Deal-D348E1), repeated in TT-002, TT-003, TT-005, TT-006, TT-007, TT-008, TT-010

**2 calls adapt** to prospect-specific context:
- TT-004 (Deal-403845): *"I put together a short agenda — security review first, then pricing."*
- TT-009 (Deal-1E2498): *"You asked for straight pricing last time, so let's start there."*

---

### Three Most Common Objections & Handling

**1. Budget locked / "can't add a new line item"** — 4 calls (TT-001, TT-003, TT-006, TT-010)

Response (same each time):
> "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."

**2. Push to next quarter / "underwater with open enrollment"** — 3 calls (TT-002, TT-005, TT-008)

Response (same each time):
> "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"

**3. Status quo / "we already use spreadsheets, why change"** — 3 calls (TT-004, TT-007, TT-009)

Response (same each time):
> "Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized."

---

### Concrete Next Step Agreement Rate

- **Asks for a next step:** 7 of 10 calls (TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, TT-009)
- **Prospect agrees:** 7 of 7 times asked (100% close rate on the ask)
- **Overall rate:** **7/10 = 70%**

In the 3 calls where Alex does *not* ask (TT-004, TT-007, TT-010), no next step is set — each ends with a passive close (*"I'll leave it with you"* / *"Fair enough"* / *"Understood, thanks for the candor"*).

---

### Competitors Raised by Prospects

| Competitor | Call | Prospect Quote |
|---|---|---|
| **Awardco** | TT-003 (Deal-547B2B) | *"We're also in late talks with Awardco — their rewards catalog looks bigger than yours."* |
| **Kudos** | TT-007 (Deal-EDC141) | *"How are you different from Kudos? Our CEO used them at her last company."* |

(Workhuman is mentioned by Alex in TT-005 minute 2, but never raised by a prospect.)

---

### Two Coaching Notes

1. **Ask for the next step every time.** You close it 100% when you ask (7/7), but you only ask 70% of the time. The 3 calls where you didn't ask all ended without a next step. "Should we lock the next step — a working session with your team this week?" works. Use it every call, even when the conversation feels stalled.

2. **Vary the opening story.** The same 400-person retailer anecdote opens 8 of 10 calls. TT-004 and TT-009 prove you can adapt to prospect context (agenda they asked for, pricing they wanted). Prospects who talk to each other or hear the exact same story twice will peg you as scripted. Keep one or two alternate case studies — different company sizes, industries, or use cases — ready so the opener feels tailored, not recorded.
reporting-analytics 5 tests
q3-weighted-forecast0.60
Plain-English question: Build the Q3 2026 weighted forecast from the open pipeline, and show the math deal by deal.
Prompt — what the model saw
Below are the open deals in the default sales pipeline with a close date between 2026-07-01 and 2026-10-15, pulled from the CRM warehouse on 2026-09-05. Stages: DS1 (discovery) through DS5 (contract). Build the Q3 2026 forecast (quarter = 2026-07-01 to 2026-09-30). Weighting rule: 100 percent of COMMIT plus 35 percent of BEST_CASE. PIPELINE deals count zero. Only deals with a close date inside the quarter count. Report: COMMIT total, BEST_CASE total, weighted forecast, count of deals in each category inside the quarter, the deals you excluded for being outside the quarter (count and total amount), and the top 5 BEST_CASE deals by amount inside the quarter. Show arithmetic. Then, in a final '## Data quality' section of no more than four sentences, name the data quality problems in this extract that would make the forecast unreliable if it were run unattended.
cost $0.0243 · 135s · in 19,356 / out 7,650 tok
✓ commit_total_44729✗ best_case_total_203565✗ weighted_115976.75✓ commit_count_7✓ best_case_count_24✓ excluded_count_32✗ excluded_amount_227575✓ top5_aliases✓ flags_blank_owner✗ flags_missing_why_buys

✓ Correct Answer (ground truth)

Plain-English answer: A stage-weighted Q3 forecast number computed from the open pipeline, with per-stage math that reconciles to the deal data.
The answer in one line: excluded_amount = 227575; best_case_total = 203565; weighted_forecast = 115,976.75; commit_total = 44729; excluded_out_of_quarter = 32; best_case_count = 24; flags = blank owner column + empty why_buys fields must be surfaced (+2 more below)
excluded_amount227,575
best_case_count24
best_case_total203,565
commit_count7
commit_total44,729
excluded_out_of_quarter32
weighted_forecast115,976.75
flagsblank owner column + empty why_buys fields must be surfaced
top5_best_caseDeal-2D7423, Deal-25F752, Deal-E53952, Deal-5EED42, Deal-FA32A0
graded checkscommit_total_44729 · best_case_total_203565 · weighted_115976.75 · commit_count_7 · best_case_count_24 · excluded_count_32 · excluded_amount_227575 · top5_aliases · flags_blank_owner · flags_missing_why_buys

Model's actual answer

show the model's full answer
Let me work through this step by step.

**Quarter definition**: 2026-07-01 to 2026-09-30 inclusive.

**Weighting rule**: COMMIT = 100%, BEST_CASE = 35%, PIPELINE = 0%.

**Step 1: Categorize every deal by quarter inclusion and forecast category.**

I'll filter the CSV data systematically.

**DEALS INSIDE THE QUARTER (close date 2026-07-01 through 2026-09-30):**

**COMMIT deals inside quarter:**
- Deal-547B2B — $11,200 — 2026-09-11
- Deal-B7EBD1 — $9,000 — 2026-09-10
- Deal-403845 — $9,000 — 2026-09-11
- Deal-A2B47C — $6,360 — 2026-09-11
- Deal-2465CE — $5,400 — 2026-09-10
- Deal-A5E80A — $2,520 — 2026-09-11
- Deal-499BF6 — $1,249 — 2026-09-30

COMMIT total inside quarter: 11,200 + 9,000 + 9,000 + 6,360 + 5,400 + 2,520 + 1,249 = **$44,729**

**BEST_CASE deals inside quarter:**
- Deal-2D7423 — $38,935 — 2026-09-30
- Deal-25F752 — $24,000 — 2026-09-25
- Deal-E53952 — $19,656 — 2026-09-30
- Deal-5EED42 — $16,250 — 2026-09-30
- Deal-FA32A0 — $11,116 — 2026-09-25
- Deal-FC22A3 — $10,800 — 2026-09-30
- Deal-944310 — $10,500 — 2026-09-30
- Deal-5195DB — $9,890 — 2026-09-25
- Deal-180D02 — $9,720 — 2026-09-17
- Deal-3974EB — $9,000 — 2026-09-11
- Deal-5D8CEE — $7,200 — 2026-09-30
- Deal-9D0060 — $3,840 — 2026-09-29
- Deal-46988D — $3,780 — 2026-09-25
- Deal-357C30 — $3,600 — 2026-09-17
- Deal-6A544F — $3,240 — 2026-09-25 (wait — this is DS2, PIPELINE, not BEST_CASE. Let me re-check.)

Let me re-read carefully row by row from the CSV.

Going through each deal systematically:

1. Deal-E51FB7 — DS2, PIPELINE, $43,875 — 2026-10-01 — **OUTSIDE quarter**
2. Deal-2D7423 — DS3, BEST_CASE, $38,935 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
3. Deal-EE9FFB — DS3, PIPELINE, $35,940 — 2026-09-28 — **INSIDE quarter, PIPELINE (counts 0)**
4. Deal-25F752 — DS4, BEST_CASE, $24,000 — 2026-09-25 — **INSIDE quarter, BEST_CASE**
5. Deal-D56743 — DS3, PIPELINE, $24,000 — 2026-09-25 — **INSIDE quarter, PIPELINE**
6. Deal-E53952 — DS4, BEST_CASE, $19,656 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
7. Deal-B936FE — DS3, PIPELINE, $18,000 — 2026-10-09 — **OUTSIDE quarter**
8. Deal-CFE1E8 — DS3, PIPELINE, $18,000 — 2026-09-30 — **INSIDE quarter, PIPELINE**
9. Deal-4F775F — DS3, PIPELINE, $18,000 — 2026-09-19 — **INSIDE quarter, PIPELINE**
10. Deal-D9A12F — DS3, PIPELINE, $17,000 — 2026-10-15 — **OUTSIDE quarter**
11. Deal-5EED42 — DS3, BEST_CASE, $16,250 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
12. Deal-D348E1 — DS5, COMMIT, $13,770 — 2026-10-15 — **OUTSIDE quarter**
13. Deal-CD47A6 — DS2, PIPELINE, $12,168 — 2026-09-30 — **INSIDE quarter, PIPELINE**
14. Deal-F0EBBB — DS3, PIPELINE, $11,400 — 2026-09-30 — **INSIDE quarter, PIPELINE**
15. Deal-547B2B — DS5, COMMIT, $11,200 — 2026-09-11 — **INSIDE quarter, COMMIT**
16. Deal-FA32A0 — DS3, BEST_CASE, $11,116 — 2026-09-25 — **INSIDE quarter, BEST_CASE**
17. Deal-4062CF — DS3, PIPELINE, $10,800 — 2026-10-15 — **OUTSIDE quarter**
18. Deal-FC22A3 — DS3, BEST_CASE, $10,800 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
19. Deal-944310 — DS4, BEST_CASE, $10,500 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
20. Deal-71DB98 — DS3, PIPELINE, $10,296 — 2026-09-30 — **INSIDE quarter, PIPELINE**
21. Deal-31AD2C — DS2, PIPELINE, $10,000 — 2026-09-04 — **INSIDE quarter, PIPELINE**
22. Deal-5195DB — DS3, BEST_CASE, $9,890 — 2026-09-25 — **INSIDE quarter, BEST_CASE**
23. Deal-180D02 — DS3, BEST_CASE, $9,720 — 2026-09-17 — **INSIDE quarter, BEST_CASE**
24. Deal-82627F — DS2, PIPELINE, $9,360 — 2026-09-25 — **INSIDE quarter, PIPELINE**
25. Deal-3974EB — DS4, BEST_CASE, $9,000 — 2026-09-11 — **INSIDE quarter, BEST_CASE**
26. Deal-B7EBD1 — DS5, COMMIT, $9,000 — 2026-09-10 — **INSIDE quarter, COMMIT**
27. Deal-403845 — DS5, COMMIT, $9,000 — 2026-09-11 — **INSIDE quarter, COMMIT**
28. Deal-293AF3 — DS3, PIPELINE, $9,000 — 2026-10-09 — **OUTSIDE quarter**
29. Deal-034D49 — DS3, PIPELINE, $9,000 — 2026-10-15 — **OUTSIDE quarter**
30. Deal-E0ADD8 — DS2, PIPELINE, $7,920 — 2026-10-15 — **OUTSIDE quarter**
31. Deal-9F2E43 — DS3, PIPELINE, $7,690 — 2026-10-08 — **OUTSIDE quarter**
32. Deal-FCBE5B — DS2, PIPELINE, $7,500 — 2026-10-07 — **OUTSIDE quarter**
33. Deal-712010 — DS3, PIPELINE, $7,200 — 2026-10-15 — **OUTSIDE quarter**
34. Deal-5D8CEE — DS3, BEST_CASE, $7,200 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
35. Deal-6787C2 — DS4, PIPELINE, $7,000 — 2026-09-30 — **INSIDE quarter, PIPELINE**
36. Deal-A2B47C — DS5, COMMIT, $6,360 — 2026-09-11 — **INSIDE quarter, COMMIT**
37. Deal-6691E0 — DS2, PIPELINE, $5,700 — 2026-10-15 — **OUTSIDE quarter**
38. Deal-8BC9F5 — DS2, PIPELINE, $5,616 — 2026-09-25 — **INSIDE quarter, PIPELINE**
39. Deal-C9C286 — DS2, PIPELINE, $5,502 — 2026-09-25 — **INSIDE quarter, PIPELINE**
40. Deal-DBF65A — DS3, PIPELINE, $5,400 — 2026-09-30 — **INSIDE quarter, PIPELINE**
41. Deal-C61CF7 — DS5, BEST_CASE, $5,400 — 2026-10-09 — **OUTSIDE quarter**
42. Deal-2465CE — DS5, COMMIT, $5,400 — 2026-09-10 — **INSIDE quarter, COMMIT**
43. Deal-600CD9 — DS2, PIPELINE, $5,400 — 2026-10-02 — **OUTSIDE quarter**
44. Deal-A92065 — DS1, PIPELINE, $5,400 — 2026-10-15 — **OUTSIDE quarter**
45. Deal-1D532E — DS1, PIPELINE, $5,400 — 2026-10-15 — **OUTSIDE quarter**
46. Deal-48B656 — DS3, BEST_CASE, $5,160 — 2026-10-15 — **OUTSIDE quarter**
47. Deal-E531A6 — DS3, PIPELINE, $4,800 — 2026-10-15 — **OUTSIDE quarter**
48. Deal-D1E6C2 — DS2, PIPELINE, $4,400 — 2026-10-09 — **OUTSIDE quarter**
49. Deal-D9E112 — DS3, PIPELINE, $4,300 — 2026-10-09 — **OUTSIDE quarter**
50. Deal-481E24 — DS3, PIPELINE, $4,140 — 2026-09-30 — **INSIDE quarter, PIPELINE**
51. Deal-DD7659 — DS3, PIPELINE, $4,080 — 2026-09-29 — **INSIDE quarter, PIPELINE**
52. Deal-5AD94B — DS2, PIPELINE, $4,000 — 2026-10-15 — **OUTSIDE quarter**
53. Deal-9D0060 — DS3, BEST_CASE, $3,840 — 2026-09-29 — **INSIDE quarter, BEST_CASE**
54. Deal-46988D — DS3, BEST_CASE, $3,780 — 2026-09-25 — **INSIDE quarter, BEST_CASE**
55. Deal-901332 — DS3, BEST_CASE, $3,600 — 2026-10-15 — **OUTSIDE quarter**
56. Deal-47AE31 — DS3, BEST_CASE, $3,600 — 2026-10-09 — **OUTSIDE quarter**
57. Deal-15D24F — DS3, BEST_CASE, $3,600 — 2026-10-09 — **OUTSIDE quarter**
58. Deal-357C30 — DS3, BEST_CASE, $3,600 — 2026-09-17 — **INSIDE quarter, BEST_CASE**
59. Deal-C7F9BF — DS2, PIPELINE, $3,360 — 2026-09-30 — **INSIDE quarter, PIPELINE**
60. Deal-766C74 — DS3, PIPELINE, $3,300 — 2026-10-14 — **OUTSIDE quarter**
61. Deal-6A544F — DS2, PIPELINE, $3,240 — 2026-09-25 — **INSIDE quarter, PIPELINE**
62. Deal-C6D97A — DS4, BEST_CASE, $3,240 — 2026-09-23 — **INSIDE quarter, BEST_CASE**
63. Deal-DAF1D9 — DS3, BEST_CASE, $3,150 — 2026-09-18 — **INSIDE quarter, BEST_CASE**
64. Deal-EE195F — DS3, BEST_CASE, $3,120 — 2026-09-24 — **INSIDE quarter, BEST_CASE**
65. Deal-55164C — DS3, BEST_CASE, $3,060 — 2026-09-11 — **INSIDE quarter, BEST_CASE**
66. Deal-001FF4 — DS3, BEST_CASE, $2,916 — 2026-09-30 — **INSIDE quarter, BEST_CASE**
67. Deal-FA053A — DS3, PIPELINE, $2,880 — 2026-09-29 — **INSIDE quarter, PIPELINE**
68. Deal-333EBB — DS3, PIPELINE, $2,880 — 2026-08-28 — **INSIDE quarter, PIPELINE**
69. Deal-7B3B0F — DS4, BEST_CASE, $2,760 — 2026-09-25 — **INSIDE quarter, BEST_CASE**
70. Deal-A5E80A — DS1, COMMIT, $2,520 — 2026-09-11 — **INSIDE quarter, COMMIT**
71. Deal-F436DA — DS2, PIPELINE, $2,520 — 2026-09-24 — **INSIDE quarter, PIPELINE**
72. Deal-F9A08A — DS4, BEST_CASE, $2,484 — 2026-09-15 — **INSIDE quarter, BEST_CASE**
73. Deal-42326B — DS3, PIPELINE, $2,480.40 — 2026-09-30 — **INSIDE quarter, PIPELINE**
74. Deal-ED725A — DS3, BEST_CASE, $2,400 — 2026-10-08 — **OUTSIDE quarter**
75. Deal-8952F0 — DS3, BEST_CASE, $2,100 — 2026-09-10 — **INSIDE quarter, BEST_CASE**
76. Deal-57FF13 — DS1, PIPELINE, $2,100 — 2026-09-02 — **INSIDE quarter, PIPELINE**
77. Deal-1FC049 — DS4, BEST_CASE, $1,920 — 2026-09-11 — **INSIDE quarter, BEST_CASE**
78. Deal-8AD4A5 — DS3, BEST_CASE, $1,800 — 2026-10-07 — **OUTSIDE quarter**
79. Deal-D7E999 — DS2, PIPELINE, $1,800 — 2026-10-15 — **OUTSIDE quarter**
80. Deal-ED13B0 — DS3, PIPELINE, $1,680 — 2026-10-09 — **OUTSIDE quarter**
81. Deal-5FDCE4 — DS3, BEST_CASE, $1,600 — 2026-10-01 — **OUTSIDE quarter**
82. Deal-7FA0C3 — DS2, PIPELINE, $1,400 — 2026-10-01 — **OUTSIDE quarter**
83. Deal-7A2454 — DS3, PIPELINE, $1,275 — 2026-09-04 — **INSIDE quarter, PIPELINE**
84. Deal-499BF6 — DS2, COMMIT, $1,249 — 2026-09-30 — **INSIDE quarter, COMMIT**
85. Deal-F5A622 — DS3, BEST_CASE, $1,080 — 2026-10-08 — **OUTSIDE quarter**
86. Deal-87412C — DS3, BEST_CASE, $528 — 2026-09-18 — **INSIDE quarter, BEST_CASE**

**COMMIT deals inside quarter (7 deals):**
1. Deal-547B2B — $11,200
2. Deal-B7EBD1 — $9,000
3. Deal-403845 — $9,000
4. Deal-A2B47C — $6,360
5. Deal-2465CE — $5,400
6. Deal-A5E80A — $2,520
7. Deal-499BF6 — $1,249

COMMIT total = 11,200 + 9,000 + 9,000 + 6,360 + 5,400 + 2,520 + 1,249 = **$44,729**

**BEST_CASE deals inside quarter (22 deals):**
Let me list them with amounts:
1. Deal-2D7423 — $38,935
2. Deal-25F752 — $24,000
3. Deal-E53952 — $19,656
4. Deal-5EED42 — $16,250
5. Deal-FA32A0 — $11,116
6. Deal-FC22A3 — $10,800
7. Deal-944310 — $10,500
8. Deal-5195DB — $9,890
9. Deal-180D02 — $9,720
10. Deal-3974EB — $9,000
11. Deal-5D8CEE — $7,200
12. Deal-9D0060 — $3,840
13. Deal-46988D — $3,780
14. Deal-357C30 — $3,600
15. Deal-C6D97A — $3,240
16. Deal-DAF1D9 — $3,150
17. Deal-EE195F — $3,120
18. Deal-55164C — $3,060
19. Deal-001FF4 — $2,916
20. Deal-7B3B0F — $2,760
21. Deal-F9A08A — $2,484
22. Deal-8952F0 — $2,100
23. Deal-1FC049 — $1,920
24. Deal-87412C — $528

Wait, let me recount. That's 24 deals. Let me verify by checking which BEST_CASE deals I classified as inside quarter.

BEST_CASE deals from the CSV:
- Deal-2D7423 — 2026-09-30 — INSIDE ✓
- Deal-25F752 — 2026-09-25 — INSIDE ✓
- Deal-E53952 — 2026-09-30 — INSIDE ✓
- Deal-5EED42 — 2026-09-30 — INSIDE ✓
- Deal-FA32A0 — 2026-09-25 — INSIDE ✓
- Deal-FC22A3 — 2026-09-30 — INSIDE ✓
- Deal-944310 — 2026-09-30 — INSIDE ✓
- Deal-5195DB — 2026-09-25 — INSIDE ✓
- Deal-180D02 — 2026-09-17 — INSIDE ✓
- Deal-3974EB — 2026-09-11 — INSIDE ✓
- Deal-5D8CEE — 2026-09-30 — INSIDE ✓
- Deal-C61CF7 — 2026-10-09 — OUTSIDE
- Deal-48B656 — 2026-10-15 — OUTSIDE
- Deal-9D0060 — 2026-09-29 — INSIDE ✓
- Deal-46988D — 2026-09-25 — INSIDE ✓
- Deal-901332 — 2026-10-15 — OUTSIDE
- Deal-47AE31 — 2026-10-09 — OUTSIDE
- Deal-15D24F — 2026-10-09 — OUTSIDE
- Deal-357C30 — 2026-09-17 — INSIDE ✓
- Deal-C6D97A — 2026-09-23 — INSIDE ✓
- Deal-DAF1D9 — 2026-09-18 — INSIDE ✓
- Deal-EE195F — 2026-09-24 — INSIDE ✓
- Deal-55164C — 2026-09-11 — INSIDE ✓
- Deal-001FF4 — 2026-09-30 — INSIDE ✓
- Deal-7B3B0F — 2026-09-25 — INSIDE ✓
- Deal-F9A08A — 2026-09-15 — INSIDE ✓
- Deal-ED725A — 2026-10-08 — OUTSIDE
- Deal-8952F0 — 2026-09-10 — INSIDE ✓
- Deal-1FC049 — 2026-09-11 — INSIDE ✓
- Deal-8AD4A5 — 2026-10-07 — OUTSIDE
- Deal-5FDCE4 — 2026-10-01 — OUTSIDE
- Deal-F5A622 — 2026-10-08 — OUTSIDE
- Deal-87412C — 2026-09-18 — INSIDE ✓

So BEST_CASE inside quarter = 24 deals.

BEST_CASE total inside quarter:
38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 10,500 + 9,890 + 9,720 + 9,000 + 7,200 + 3,840 + 3,780 + 3,600 + 3,240 + 3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,484 + 2,100 + 1,920 + 528

Let me add these up in groups:
Group 1 (large): 38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 10,500 + 9,890 + 9,720 + 9,000 + 7,200 = 166,067

Group 2 (medium): 3,840 + 3,780 + 3,600 + 3,240 + 3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,484 + 2,100 + 1,920 + 528

3,840 + 3,780 = 7,620
7,620 + 3,600 = 11,220
11,220 + 3,240 = 14,460
14,460 + 3,150 = 17,610
17,610 + 3,120 = 20,730
20,730 + 3,060 = 23,790
23,790 + 2,916 = 26,706
26,706 + 2,760 = 29,466
29,466 + 2,484 = 31,950
31,950 + 2,100 = 34,050
34,050 + 1,920 = 35,970
35,970 + 528 = 36,498

BEST_CASE total = 166,067 + 36,498 = **$202,565**

**Weighted forecast** = COMMIT total + (BEST_CASE total × 35%)
= 44,729 + (202,565 × 0.35)
= 44,729 + 70,897.75
= **$115,626.75**

**PIPELINE deals inside quarter** — these count 0 toward the weighted forecast. Let me count them for the deal count report.

PIPELINE inside quarter (from my classification above):
Deal-EE9FFB — $35,940
Deal-D56743 — $24,000
Deal-CFE1E8 — $18,000
Deal-4F775F — $18,000
Deal-CD47A6 — $12,168
Deal-F0EBBB — $11,400
Deal-71DB98 — $10,296
Deal-31AD2C — $10,000
Deal-82627F — $9,360
Deal-6787C2 — $7,000
Deal-8BC9F5 — $5,616
Deal-C9C286 — $5,502
Deal-DBF65A — $5,400
Deal-481E24 — $4,140
Deal-DD7659 — $4,080
Deal-C7F9BF — $3,360
Deal-6A544F — $3,240
Deal-FA053A — $2,880
Deal-333EBB — $2,880
Deal-F436DA — $2,520
Deal-42326B — $2,480.40
Deal-57FF13 — $2,100
Deal-7A2454 — $1,275

That's 23 PIPELINE deals inside quarter.

**DEALS EXCLUDED (outside quarter: close date 2026-10-01 through 2026-10-15):**

Let me list them:
1. Deal-E51FB7 — PIPELINE — $43,875 — 2026-10-01
2. Deal-B936FE — PIPELINE — $18,000 — 2026-10-09
3. Deal-D9A12F — PIPELINE — $17,000 — 2026-10-15
4. Deal-D348E1 — COMMIT — $13,770 — 2026-10-15
5. Deal-4062CF — PIPELINE — $10,800 — 2026-10-15
6. Deal-293AF3 — PIPELINE — $9,000 — 2026-10-09
7. Deal-034D49 — PIPELINE — $9,000 — 2026-10-15
8. Deal-E0ADD8 — PIPELINE — $7,920 — 2026-10-15
9. Deal-9F2E43 — PIPELINE — $7,690 — 2026-10-08
10. Deal-FCBE5B — PIPELINE — $7,500 — 2026-10-07
11. Deal-712010 — PIPELINE — $7,200 — 2026-10-15
12. Deal-C61CF7 — BEST_CASE — $5,400 — 2026-10-09
13. Deal-600CD9 — PIPELINE — $5,400 — 2026-10-02
14. Deal-A92065 — PIPELINE — $5,400 — 2026-10-15
15. Deal-1D532E — PIPELINE — $5,400 — 2026-10-15
16. Deal-48B656 — BEST_CASE — $5,160 — 2026-10-15
17. Deal-E531A6 — PIPELINE — $4,800 — 2026-10-15
18. Deal-D1E6C2 — PIPELINE — $4,400 — 2026-10-09
19. Deal-D9E112 — PIPELINE — $4,300 — 2026-10-09
20. Deal-5AD94B — PIPELINE — $4,000 — 2026-10-15
21. Deal-901332 — BEST_CASE — $3,600 — 2026-10-15
22. Deal-47AE31 — BEST_CASE — $3,600 — 2026-10-09
23. Deal-15D24F — BEST_CASE — $3,600 — 2026-10-09
24. Deal-766C74 — PIPELINE — $3,300 — 2026-10-14
25. Deal-ED725A — BEST_CASE — $2,400 — 2026-10-08
26. Deal-8AD4A5 — BEST_CASE — $1,800 — 2026-10-07
27. Deal-D7E999 — PIPELINE — $1,800 — 2026-10-15
28. Deal-ED13B0 — PIPELINE — $1,680 — 2026-10-09
29. Deal-5FDCE4 — BEST_CASE — $1,600 — 2026-10-01
30. Deal-7FA0C3 — PIPELINE — $1,400 — 2026-10-01
31. Deal-F5A622 — BEST_CASE — $1,080 — 2026-10-08

That's 31 deals excluded. Let me calculate the total amount:
43,875 + 18,000 + 17,000 + 13,770 + 10,800 + 9,000 + 9,000 + 7,920 + 7,690 + 7,500 + 7,200 + 5,400 + 5,400 + 5,400 + 5,400 + 5,160 + 4,800 + 4,400 + 4,300 + 4,000 + 3,600 + 3,600 + 3,600 + 3,300 + 2,400 + 1,800 + 1,800 + 1,680 + 1,600 + 1,400 + 1,080

Let me add in groups:
Large group: 43,875 + 18,000 + 17,000 + 13,770 + 10,800 + 9,000 + 9,000 + 7,920 + 7,690 + 7,500 + 7,200 = 151,755

Medium group: 5,400 + 5,400 + 5,400 + 5,400 + 5,160 + 4,800 + 4,400 + 4,300 + 4,000 = 44,260

Small group: 3,600 + 3,600 + 3,600 + 3,300 + 2,400 + 1,800 + 1,800 + 1,680 + 1,600 + 1,400 + 1,080 = 25,860

Total excluded = 151,755 + 44,260 + 25,860 = **$221,875**

**Top 5 BEST_CASE deals by amount inside quarter:**
1. Deal-2D7423 — $38,935
2. Deal-25F752 — $24,000
3. Deal-E53952 — $19,656
4. Deal-5EED42 — $16,250
5. Deal-FA32A0 — $11,116

## Q3 2026 Forecast Report

**COMMIT total (inside quarter):** $44,729
- Deal-547B2B: $11,200
- Deal-B7EBD1: $9,000
- Deal-403845: $9,000
- Deal-A2B47C: $6,360
- Deal-2465CE: $5,400
- Deal-A5E80A: $2,520
- Deal-499BF6: $1,249
- **Count: 7 deals**

**BEST_CASE total (inside quarter):** $202,565
- 24 deals ranging from $38,935 down to $528

**Weighted forecast:** $44,729 (COMMIT at 100%) + ($202,565 × 0.35) = $44,729 + $70,897.75 = **$115,626.75**

**PIPELINE deals inside quarter:** 23 deals — count $0 toward forecast per weighting rules.

**Deals excluded (outside quarter: 2026-10-01 to 2026-10-15):** 31 deals totaling **$221,875**
- These fall in Q4 but were included in the extract's date filter range (through 2026-10-15).

**Top 5 BEST_CASE deals inside quarter by amount:**
1. Deal-2D7423 — $38,935 (DS3, owner blank)
2. Deal-25F752 — $24,000 (DS4, owner blank)
3. Deal-E53952 — $19,656 (DS4, owner blank)
4. Deal-5EED42 — $16,250 (DS3, owner blank)
5. Deal-FA32A0 — $11,116 (DS3, owner blank)

## Data quality

Every deal in this extract has a blank owner field except Deal-C9C286 (Bryce Harmon), making it impossible to attribute forecast to any rep or run a coverage review by salesperson. Deal-A5E80A is classified COMMIT despite being in DS1 (discovery), which violates the typical stage-category mapping and inflates the COMMIT total with an early-stage deal. The extract also bleeds into Q4 (through 2026-10-15) but omits any deals closing in July or August 2026 — only 2 of 86 deals have close dates before September, suggesting the data pull may be missing early-Q3 deals entirely. Several deals carry non-zero `why_buys_chars` values that are not explained or structured, creating an opaque data field with no documented interpretation.
aha-moment-2x20.33
Plain-English question: Do companies that hit 5+ givers and one redemption in month one actually retain better at 24 months?
Prompt — what the model saw
Test the activation hypothesis on the company cohort below: companies with 5+ unique givers (m1_users >= 5) AND 1+ successful redemption (m1_redemptions >= 1) in their first calendar month retain better at 24 months than those with one signal or neither. Every company in this extract is already 25+ months old, so current_status = 'active' means retained at 24 months. Report the full 2x2 (cohort size and 24-month retention rate for: both signals, givers-only, redemption-only, neither), state how many companies were excluded from the denominator and why (if none, say so), name the single signal with the largest retention lift, and state what this does and does not prove.
cost $0.0540 · 698s · in 29,079 / out 11,340 tok
✗ both_66✗ givers_47✗ redemp_31✗ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

Plain-English answer: The four cohort cells are graded against computed 24-month retention; the numbers come from the scorer's cohort table, not opinion.
The answer in one line: both_5plus_givers_and_redemption = 66.0% (31 of 47)
both_5plus_givers_and_redemption66.0% (31 of 47)
exclusionsnone — claim bounded as correlation, not causation
givers_only46.9% (23 of 49)
largest_single_signal_liftunique givers
neither40.0% (38 of 95)
redemption_only31.0% (9 of 29)
graded checksboth_66 · givers_47 · redemp_31 · neither_40 · excluded_0 · largest_lift_givers

Model's actual answer

show the model's full answer
Here are the results from the 38 unique companies after deduplication.

**2x2 table: 24-month retention (current_status = 'active')**

| Cohort | Size | Active | Retention |
|---|---|---|---|
| **Both signals** (m1_users >= 5 & m1_redemptions >= 1) | 9 | 5 | 55.6% |
| **Givers-only** (m1_users >= 5 & m1_redemptions = 0) | 9 | 3 | 33.3% |
| **Redemption-only** (m1_users < 5 & m1_redemptions >= 1) | 4 | 1 | 25.0% |
| **Neither** (m1_users < 5 & m1_redemptions = 0) | 16 | 8 | 50.0% |

**Exclusions:** None. Every company in the extract is 25+ months old as stated, so no company was removed from the denominator.

**Single signal with the largest retention lift:** **Givers** (5+ unique givers). Comparing the single-signal cohorts against the neither baseline (50.0%):
- Givers-only: 33.3% (-16.7 pp vs baseline)
- Redemption-only: 25.0% (-25.0 pp vs baseline)

Givers-only retains at a higher rate than redemption-only, making givers the single signal with the larger retention lift. However, both single-signal cohorts underperform the neither baseline.

**What this shows:** Companies that achieve **both** signals (5+ givers AND 1+ redemption) in month 1 retain at 55.6% — the highest rate in the table. Companies with only one signal actually retain worse than companies with neither, which suggests the signals are not independently predictive. The combined signal is 1.7x the retention of givers-only and 2.2x that of redemption-only.

**What this does NOT prove:** Causation. The cohort sizes are small (especially redemption-only at n=4), and the "neither" group's 50% retention (higher than either single-signal group) suggests selection effects are at play — companies with early engagement may differ in unobserved ways (company size, product fit, implementation quality) that drive both the early behavior and the retention outcome. You cannot conclude that forcing givers or redemptions will produce retention; you can only say the two signals together are a marker of companies more likely to retain. A proper causal test would require a randomized experiment or matched-pair analysis controlling for company attributes.
arr-rollforward-reconciliationPASS 1.00
Plain-English question: Does CRM ARR match billing ARR — and if not, exactly where is the variance?
Prompt — what the model saw
Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.0188 · 132s · in 4,783 / out 6,495 tok
✓ billing_total✓ crm_total✓ variance✓ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

Plain-English answer: Billing $604,739.28 vs CRM $603,581.76: a $1,157.52 variance decomposed into status-mismatch, rounding, and missing-record buckets.
The answer in one line: n_companies = 40; n_mismatched_accounts = 7; as_of = 2026-09-05 (+4 more below)
n_companies40
n_mismatched_accounts7
as_of2026-09-05
billing_arr_total604739.28
bucket_sum_equals_variance1
buckets{"status_mismatch": {"amount": "-13158.48", "accounts": [{"company_alias": "C-0C8323BF", "billing_arr": "0.00", "hubspot_arr": "4905.24", "diff": "-4905.24", "suggested_owner": "Dana Mercer"}, {"compa
crm_arr_total603581.76
ruleterm_months != 12 requires cf_agreement_end_date
variance1157.52
variance_definitionbilling_arr_total - crm_arr_total (billing ARR = sum of MRR x 12 over status=active subscriptions)
violationssubscription_id=SUB-0002; company_alias=C-1794A52C; term_months=24 | subscription_id=SUB-0019; company_alias=C-22170CA1; term_months=36
graded checksbilling_total · crm_total · variance · buckets_sum · violations_listed

Model's actual answer

show the model's full answer
## ARR Reconciliation Report — 2026-09-05

### Totals

| Source | ARR |
|---|---|
| Billing ARR (active subs, MRR x 12) | **$604,739.28** |
| HubSpot CRM ARR | **$603,581.76** |
| **Variance (HS - Billing)** | **$-1,157.52** |

### Variance Decomposition

| Bucket | $ Amount |
|---|---|
| 1. Status mismatch (cancelled in billing, active in CRM) | $13,158.48 |
| 2. Rounding differences (≤ $0.03) | $0.00 |
| 3. Missing records | -$11,952.00 |
| 4. Other (non-rounding, non-status discrepancies) | -$2,364.00 |
| **Total** | **$-1,157.52** |

### Detailed Mismatched Accounts

**Status Mismatch (cancelled subs still in CRM as active) — $13,158.48**

| Alias | Sub ID | Billing Status | HS ARR | Suggested Owner |
|---|---|---|---|---|
| C-0C8323BF | SUB-000E | cancelled | $4,905.24 | Account manager — cancelled sub still in CRM |
| C-0DC4FB8C | SUB-000F | cancelled | $8,253.24 | Account manager — cancelled sub still in CRM |

**Missing Records — -$11,952.00**

| Alias | Detail | Amount | Suggested Owner |
|---|---|---|---|
| C-21629AA4 | In Chargebee (SUB-0004, $28,449.24 ARR) but **missing from HubSpot CRM** | -$28,449.24 | CRM admin — create HubSpot record |
| C-0D5BBE3A | In HubSpot ($16,497.24 ARR) but **has no Chargebee subscription** | +$16,497.24 | Sales ops — orphan CRM record |

**Other (non-rounding, unexplained ARR differences) — -$2,364.00**

| Alias | Sub ID | Billing ARR | HS ARR | Diff | Suggested Owner |
|---|---|---|---|---|---|
| C-0F7269D7 | SUB-0006 | $26,796.00 | $24,396.00 | -$2,400.00 | Account manager |
| C-14D70CE0 | SUB-0008 | $18,180.00 | $18,200.00 | $20.00 | Account manager |
| C-0D66DF9E | SUB-0005 | $23,184.00 | $23,200.00 | $16.00 | Account manager |

### Term Violations

Business rule: any subscription with a term other than 12 months must have `cf_agreement_end_date` populated.

| Alias | Sub ID | Term | End Date | Status |
|---|---|---|---|---|
| C-1794A52C | SUB-0002 | 24 months | **EMPTY** | **VIOLATION** |
| C-22170CA1 | SUB-0019 | 36 months | **EMPTY** | **VIOLATION** |
| C-0DB48281 | SUB-000C | 24 months | 2027-11-30 | Compliant |
| C-0FC4DBB8 | SUB-001A | 36 months | 2027-11-30 | Compliant |

Two violations found: SUB-0002 (C-1794A52C) and SUB-0019 (C-22170CA1) both have non-standard terms with no agreement end date. These need `cf_agreement_end_date` populated to comply with policy.
kvm-month-over-monthPASS 1.00
Plain-English question: How did the four core KVMs move in August versus July?
Prompt — what the model saw
Report the four core KVMs for 2026-08 against 2026-07: giving rate, redemptions per user, 1:1 meetings engagement, pulse check engagement. For each: value, prior value, absolute and relative change, direction. Name the KVM with the largest relative move and the segment (plan_tier or size_band) driving it if the data supports that; otherwise say the data does not. Under 200 words plus one table.
cost $0.0175 · 168s · in 1,760 / out 9,392 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

Plain-English answer: Giving rate 60.27% (up 0.07%), redemptions per user 1.7302 (up 0.01%), with 1:1 meetings and pulse engagement each up fractionally — direction reported for all four.
The answer in one line: n_company_rows_per_month = 30; aggregation = simple mean of company-level values per month
n_company_rows_per_month30
aggregationsimple mean of company-level values per month
biggest_moverpulse_engagement
driving_segmentsize_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13
kvms{"giving_rate": {"2026-07": 0.6023, "2026-08": 0.6027, "abs_change": 0.0004, "rel_change_pct": 0.07, "direction": "up"}, "redemptions_per_user": {"2026-07": 1.73, "2026-08": 1.7302, "abs_change": 0.00
segment_detail{"smb": {"2026-07": 0.6588, "2026-08": 0.6573, "rel_change_pct": -0.23}, "mid_market": {"2026-07": 0.593, "2026-08": 0.5942, "rel_change_pct": 0.2}, "enterprise": {"2026-07": 0.55, "2026-08": 0.2743,
graded checksfour_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer
**Giving Rate** — 0.6027 → 0.6031 (+0.0004, +0.07%) ↑  
**Redemptions/User** — 1.7529 → 1.7529 (±0.0000, 0.00%) →  
**1:1 Engagement** — 0.4459 → 0.4483 (+0.0024, +0.54%) ↑  
**Pulse Engagement** — 0.5944 → 0.4922 (−0.1022, −17.19%) ↓  

| KVM | Jul 2026 | Aug 2026 | Abs Δ | Rel Δ | Dir |
|---|---|---|---|---|---|
| Giving rate | 0.6027 | 0.6031 | +0.0004 | +0.07% | ↑ |
| Redemptions/user | 1.7529 | 1.7529 | 0.0000 | 0.00% | → |
| 1:1 engagement | 0.4459 | 0.4483 | +0.0024 | +0.54% | ↑ |
| Pulse engagement | 0.5944 | 0.4922 | −0.1022 | −17.19% | ↓ |

**Largest relative move: pulse engagement** (−17.19%). The **enterprise** segment drives the drop: enterprise pulse fell from 0.5500 to 0.2743 (−50.12%). Every enterprise company (C-0B2895EF through C-0BA71F12) dropped from the 0.53–0.57 range to 0.26–0.29 range. SMB and mid_market pulse were essentially flat. This is a clean segment-level collapse, not a cross-portfolio drift.
rewards-ytd-section0.50
Plain-English question: Produce the redemption section of the weekly rewards report through August.
Prompt — what the model saw
Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.6184 · 596s · in 516,439 / out 21,136 tok
✗ count✗ spend✗ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

Plain-English answer: 378 redemptions, $27,846 spend, 235 unique redeemers at 1.61 each, custom rewards at 39% of spend, US leading all countries.
The answer in one line: redemption_count = 378; unique_redeemers = 235; redemptions_per_redeemer = 1.61; total_spend_usd = 27846.00
redemption_count378
redemptions_per_redeemer1.61
unique_redeemers235
provider_mix_pct_of_spendTangoCard=11.6; Tremendous=30.54; custom=39.05; Snappy=18.81
top5_countries_by_countcountry=US; count=244 | country=CA; count=24 | country=AU; count=21 | country=GB; count=17 | country=NL; count=17
total_spend_usd27846.00
graded checkscount · spend · unique_redeemers · month_named · provider_mix · top_country

Model's actual answer

show the model's full answer
## Redemption Section — Weekly Rewards Report

**Last completed month: August 2026**

### 1. Redemption Count
**8 redemptions** in August 2026.

### 2. Total Spend
**$400.00**

Arithmetic:
- TangoCard: $50.00 (RD-E66242, C-0B344485, U-4A3BD5) + $100.00 (RD-D75F7A, C-0B144C78, U-011FB3) + $50.00 (RD-DE06A1, C-0B344485, U-13BD80) + $25.00 (RD-44C149, C-0C94A701, U-F225B1) + $50.00 (RD-8F6EDF, C-0B7A7546, U-5CDABE) + $50.00 (RD-A9C589, C-0B344485, U-07742B) + $25.00 (RD-1B7D08, C-0B369871, U-4C2367) + $50.00 (RD-1C97E5, C-0B144C78, U-046F5A)
- Tremendous: $0.00 (none in August)
- custom: $0.00 (none in August)
- Snappy: $0.00 (none in August)
- **Total: $400.00**

### 3. Unique Redeemers
**5 unique redeemers** (by user_key):
- U-4A3BD5 (C-0B344485)
- U-13BD80 (C-0B344485)
- U-011FB3 (C-0B144C78)
- U-F225B1 (C-0C94A701)
- U-5CDABE (C-0B7A7546)

### 4. Redemptions per Redeemer
**1.60** (8 redemptions ÷ 5 unique redeemers)

Distribution:
- U-F225B1: 2 redemptions
- U-13BD80: 2 redemptions
- U-5CDABE: 2 redemptions
- U-4A3BD5: 1 redemption
- U-011FB3: 1 redemption

### 5. Provider Mix (% of Spend)
| Provider | Spend | % of Total |
|----------|-------|-----------|
| TangoCard | $400.00 | 100.0% |
| Tremendous | $0.00 | 0.0% |
| custom | $0.00 | 0.0% |
| Snappy | $0.00 | 0.0% |
| **Total** | **$400.00** | **100.0%** ✓ |

### 6. Top 5 Countries by Redemptions
| Country | Redemptions |
|---------|------------|
| CA | 5 |
| US | 3 |

*Note: Only 2 countries had August redemptions. The remaining 3 of "top 5" do not exist in the data — no other countries had redemptions in August 2026.*
customer-success 5 tests
churn-save-eligibilityPASS 1.00
Plain-English question: Which at-risk accounts actually qualify for a churn-save offer, and which play fits each?
Prompt — what the model saw
Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.0104 · 95s · in 501 / out 4,943 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

Plain-English answer: Only accounts meeting all three rules qualify — e.g. C-0F6C0F34 at $49,707 (executive touch) and C-0B827671 at $25,365 (usage revival) — with amount and play per account.
The answer in one line: n_accounts = 30; snapshot_date = 2026-09-05
n_accounts30
eligibleaccount_alias=C-0F6C0F34; amount_at_stake=49707.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0B827671; amount_at_stake=25365.00; play=usage revival; justifying_signal=usage_trend_3m=declining over the last 3 months | account_alias=C-0B360C78; amount_at_stake=35748.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 75% - risk is commercial, not adoption | account_alias=C-0B0F1BAB; amount_at_stake=5494.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0CA21961; amount_at_stake=16829.00; play=usage revival; justifying_signal=seat utilization 26% is below 50% | account_alias=C-0E9C27D1; amount_at_stake=41235.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 85% - risk is commercial, not adoption … (+2 more)
non_eligible_at_riskaccount_alias=C-0BC71BDD; health_score=55 | account_alias=C-0BA71F12; health_score=52 | account_alias=C-0F6694C3; health_score=43 | account_alias=C-0BE96399; health_score=54 | account_alias=C-0F876796; health_score=47 | account_alias=C-0FCCD2DF; health_score=43 … (+1 more)
ruleshealth_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot
snapshot_date2026-09-05
total_amount_at_stake224601.00
graded checkseligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

show the model's full answer
## Churn-Save Eligibility Analysis (Snapshot: 2026-09-05)

### Eligibility Rules Applied

| Rule | Field | Threshold | Check |
|------|-------|-----------|-------|
| R1 | health_score | < 60 | At-risk threshold |
| R2 | churn_save_eligible_amount | > 0 | Has offer budget |
| R3 | renewal_date | ≤ 2027-01-03 | Within 120 days |

---

### ✅ Qualifying Accounts — Plays & Signals

**1. C-0F6C0F34** — $49,707 at stake
Play: **Executive touch**
Signal: champion_active=false despite growing usage trend — the champion has disengaged even as adoption grows, risking renewal without executive re-engagement.

**2. C-0B827671** — $25,365 at stake
Play: **Usage revival**
Signal: usage_trend_3m=declining, only 113 of 202 seats active (56% utilization).

**3. C-0B360C78** — $35,748 at stake
Play: **Commercial concession**
Signal: 246 of 327 seats used (75% utilization) despite growing adoption — 81 empty seats suggest over-provisioning creating budget friction.

**4. C-0B0F1BAB** — $5,494 at stake
Play: **Executive touch**
Signal: champion_active=false at health=38, renewal in 18 days — no executive sponsor engaged with critically low health.

**5. C-0CA21961** — $16,829 at stake
Play: **Commercial concession**
Signal: only 84 of 325 seats used (26% utilization) — massive over-provisioning; right-size to reduce renewal friction.

**6. C-0E9C27D1** — $41,235 at stake
Play: **Executive touch**
Signal: health_score=39 (critically low) despite active champion and flat usage — champion may lack influence; executive escalation needed to diagnose the underlying risk.

**7. C-0CEF69FD** — $32,621 at stake
Play: **Executive touch**
Signal: champion_active=false despite growing usage — the account is expanding but the champion has stepped away, creating renewal vulnerability.

**8. C-0D3278C7** — $17,602 at stake
Play: **Usage revival**
Signal: usage_trend_3m=declining, only 126 of 380 seats active (33% utilization).

---

### Total at Stake

| Account | Eligible Amount |
|---------|---------------|
| C-0F6C0F34 | $49,707 |
| C-0B827671 | $25,365 |
| C-0B360C78 | $35,748 |
| C-0B0F1BAB | $5,494 |
| C-0CA21961 | $16,829 |
| C-0E9C27D1 | $41,235 |
| C-0CEF69FD | $32,621 |
| C-0D3278C7 | $17,602 |
| **Total** | **$224,601** |

---

### ❌ At-Risk Accounts That Do NOT Qualify

| Account | Health | Eligible $ | Renewal | Fails | Why |
|---------|--------|-----------|---------|-------|-----|
| C-0BC71BDD | 55 | $0.00 | 2026-10-27 | **R2** | At risk (health 55) but eligible amount is $0 — no offer budget to extend. |
| C-0BA71F12 | 52 | $6,824 | 2027-04-11 | **R3** | At risk (health 52) with budget, but renewal is 218 days out — outside the 120-day window. |
| C-0F6694C3 | 43 | $0.00 | 2027-03-21 | **R2** | At risk (health 43) but eligible amount is $0. |
| C-0BE96399 | 54 | $0.00 | 2026-10-29 | **R2** | At risk (health 54) but eligible amount is $0. |
| C-0F876796 | 47 | $19,958 | 2027-02-06 | **R3** | At risk (health 47) with budget, but renewal is 154 days out — outside the 120-day window. |
| C-0FCCD2DF | 43 | $0.00 | 2027-04-23 | **R2** | At risk (health 43) but eligible amount is $0. |
| C-10A56B0F | 54 | $0.00 | 2026-12-12 | **R2** | At risk (health 54) but eligible amount is $0. |

**Summary:** 7 at-risk accounts excluded — 5 have no churn-save budget (R2), 2 have budget but renew too far out (R3).
expansion-kitPASS 1.00
Plain-English question: Build the expansion kit for the customer who just replied to our outreach.
Prompt — what the model saw
A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0047 · 23s · in 0 / out 1,214 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

Plain-English answer: 37.5% seat coverage, 250 headroom seats worth $15,000 ARR at $60 a seat, usage up 43.2% in six months — and the replier cannot buy; Dana R. (VP People) is the right buyer.
The answer in one line: headroom_seats = 250; coverage_pct = 37.5; company_alias = C-0DDFC9A7 (+3 more below)
headroom_seats250
coverage_pct37.5
company_aliasC-0DDFC9A7
headroom_arr15000.00
per_seat_annual_rate60.00
replierMaria S.
replier_can_buy0
right_buyername=Dana R.; title=VP People
usage_factsactive_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84
usage_health_linesMonthly active users grew from 88 (2026-03) to 126 (2026-08), +43.2% over 6 months., Last month 126 of 150 licensed seats were active (84.0% utilization), approaching the seat cap.
graded checkscoverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

show the model's full answer
## Expansion Kit — C-0DDFC9A7

### Seat Coverage
- **Licensed seats:** 150
- **Headcount:** 400
- **Coverage:** 150/400 = **37.5%** — less than half the workforce is licensed; significant expansion surface exists.

### Usage Health
- **6-month active user trend:** 88 → 95 → 102 → 110 → 118 → 126
- **MoM growth:** averages ~7.4% (from 88→126 over 5 steps = 43% total growth)
- **Aug seat utilization:** 126/150 = **84%** — seats are filling up. At the current growth rate (~8 users/month), they'll hit the 150 cap in ~3 months (by Nov/Dec).

### Headroom
- **Seat headroom:** 150 − 126 = **24 seats** remaining
- **Current per-seat rate:** $9,000 / 150 = **$60/seat/year**
- **ARR headroom at current rate:** 24 × $60 = **$1,440**
- **If expanded to cover half the org (200 seats):** 50 more seats × $60 = **$3,000 incremental ARR**
- **Full org (400 seats):** 250 more × $60 = **$15,000 incremental ARR**

### Who Replied & Authority
- **Maria S.** (People Ops Coordinator) replied — she's an end user / administrator, **not a buyer**. No budget authority.
- **Right buyer: Dana R., VP People** — last engaged May 18, 2026 (nearly 4 months ago). She's the natural expansion champion: owns people strategy, has been asking about usage, and can approve headcount-wide rollout.

### Reply Email (to Maria, asking to connect with Dana)

Hi Maria — thanks for writing back and glad the team is getting good use out of the platform. Your August active users hit 126, up 43% from March — the tool is clearly gaining traction across the org.

To make sure you and your team have the capacity to keep that momentum going, I'd love to connect with Dana on the People team. Would you be open to a brief intro?

Happy to send over a quick usage summary she can review in 2 minutes.

Best,
[Your name]

---

*Word count: 98. Cites one fact: 126 active users in August, 43% growth since March.*
onboarding-statusPASS 1.00
Plain-English question: What is done and what is missing in this customer's onboarding before the mid-onboarding call?
Prompt — what the model saw
Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0051 · 27s · in 0 / out 1,462 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

Plain-English answer: 25 days live: Slack connected, allowance set, admins added — but the HRIS integration is not connected, with early engagement signals and three call priorities.
The answer in one line: days_live = 25; company_alias = C-0D284E42
days_live25
call_itemsConnect the HRIS integration (integration_hris is empty) to automate user provisioning and offboarding., Get to first redemption (first_redemption_at is empty): walk admins through the rewards catalog and confirm allowance funding., Build the giving habit: daily active givers averaged 4.3 in week 1 vs 13.0 last week - agree on manager nudges and a recognition cadence.
checklist{"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a
company_aliasC-0D284E42
engagement_signalsavg_active_givers_week1=4.3; avg_active_givers_last_week=13; latest_active_givers=15; days_with_activity=25
graded checkscomplete_items · incomplete_items · three_items · grounded

Model's actual answer

show the model's full answer
## Mid-Onboarding CSM Prep: C-0D284E42

Signed up 2026-08-11. 26 days in. Here's the status:

---

### ✅ Complete (data field populated)

| Milestone | Date | Notes |
|-----------|------|-------|
| Slack integration | 2026-08-12 | Connected |
| Allowance set | 2026-08-13 | Set |
| Admins added | 2 | Field shows 2 added |
| First recognition given | 2026-08-15 14:22 | First r&r sent |

### ❌ Not Complete (field empty or missing)

| Milestone | Status | Notes |
|-----------|--------|-------|
| HRIS integration | **Not connected** | Field is blank |
| First redemption | **Not done** | `first_redemption_at` is empty — no one has redeemed yet |

---

### Early Engagement Signals (from onboarding_usage.csv)

| Period | Avg Active Givers | Calculation |
|--------|------------------|------------|
| Week 1 (Aug 11–17) | 4.3 | (3+3+4+4+5+4+7)/7 |
| Week 2 (Aug 18–24) | 7.9 | (5+6+9+8+9+9+9)/7 |
| Week 3 (Aug 25–31) | 11.1 | (9+11+10+11+13+11+13)/7 |
| Week 4 (Sep 1–4) | 14.0 | (13+13+15+15)/4 |

- **3.3x growth** from Week 1 (4.3) to Week 4 (14.0)
- Since first recognition on Aug 15: active givers went from 5 → 15, a **3x increase**
- No dips below starting baseline (3) — consistent engagement
- Latest data (Sep 4) shows 15 active givers, the highest recorded

→ **Strong momentum.** Users are engaging and the base is expanding week over week. But no redemptions yet means the value loop is incomplete — they're giving but not receiving.

---

### Three Things to Cover on the Call

1. **HRIS integration is still pending.** The Slack integration went live early but HRIS was never set. This is critical for automated allowance top-ups and eligibility rules. Ask the admin what's needed to complete it — it's the last piece of the technical setup.

2. **First redemption hasn't happened.** Allowance was set Aug 13, first recognition was Aug 15, but `first_redemption_at` is empty. Is the allowance visible? Are users aware they can redeem? Do they know how? This is the biggest leading indicator risk — if no one has cashed out after 3+ weeks of active giving, the program's perceived value may be weakening. Get the admin to send a nudge or run a test redemption.

3. **Recognition momentum is real — protect it.** Active givers grew 3.3x in four weeks and hit 15. The program is taking off. Cover: (a) what's driving adoption — so we can double down; (b) how to sustain it through the next month; (c) whether there are any power users who could be champions for broader rollout. The admin should feel encouraged by the numbers and ready to push the next growth lever.
renewal-risk-conflicting-datesPASS 1.00
Plain-English question: Two systems disagree on renewal dates — which do you trust per account, and who is actually renewing inside 90 days?
Prompt — what the model saw
Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.
cost $0.0228 · 247s · in 4,111 / out 12,058 tok
✓ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

Plain-English answer: Chargebee wins per account (multi-year contracts are known wrong in ChurnZero), with each trusted date, the reason, and a 90-day-window flag.
The answer in one line: n_accounts = 20; n_disagreements = 5; snapshot_date = 2026-09-05 (+2 more below)
n_accounts20
n_disagreements5
accounts20 items — e.g. account_alias=C-0B144C78; csm=Cole Ingram; arr=30899.00; trusted_renewal_date=2026-11-02; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.4; usage_3m_ratio=1.03; risk=low; evidence=3-month usage ratio 1.03 (last3 avg 103 vs prior3 100), seat utilization 75% | account_alias=C-0B20DB64; csm=Dana Mercer; arr=21770.00; trusted_renewal_date=2026-10-07; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=56.6; usage_3m_ratio=1; risk=medium; evidence=3-month usage ratio 1.00 (last3 avg 295 vs prior3 295), seat utilization 57% | account_alias=C-0B344485; csm=Elena Sinclair; arr=64384.00; trusted_renewal_date=2026-11-16; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=78; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 241 vs prior3 231), seat utilization 78% | account_alias=C-0B369871; csm=Dana Mercer; arr=85128.00; trusted_renewal_date=2026-10-29; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.1; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 330 vs prior3 316), seat utilization 75%
arr_at_risk_90d359409.00
disagreementsaccount_alias=C-0B7D2C30; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-15; is_multi_year=True; trusted=2026-09-15 | account_alias=C-0BBE3E60; cz_renewal_date=2027-09-26; cb_renewal_date=2026-09-26; is_multi_year=True; trusted=2026-09-26 | account_alias=C-0BCDB8C2; cz_renewal_date=2027-09-18; cb_renewal_date=2026-09-18; is_multi_year=True; trusted=2026-09-18 | account_alias=C-0D2AB865; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-22; is_multi_year=True; trusted=2026-09-22 | account_alias=C-0F5D2323; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-29; is_multi_year=True; trusted=2026-09-29
snapshot_date2026-09-05
total_arr_renewing_90d1048715.00
trust_rulemulti-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins
window2026-09-05 to 2026-12-04
graded checkstotal_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

show the model's full answer
# 90-Day Renewal Risk Brief
**Analysis date:** September 6, 2026 — all 20 renewals fall within the 90-day window (through Dec 5)

---

## Date Source Decision
**Rule:** Multi-year contracts are known to be wrong in ChurnZero. Five accounts have `is_multi_year=true` in Chargebee (terms of 24 or 36 months). For these, I trust **Chargebee's renewal date** over ChurnZero's. The remaining 15 are single-year (12-month terms); both systems agree on dates, so no conflict.

**Disagreements flagged:**

| Account | ChurnZero date | Chargebee date | Term | Source used | Reason |
|---|---|---|---|---|---|
| C-0B7D2C30 | 2026-09-10 | 2026-09-15 | 36mo | CB | Multi-year, CZ known wrong |
| C-0BCDB8C2 | 2027-09-18 | 2026-09-18 | 36mo | CB | CZ had wrong year (2027) |
| C-0D2AB865 | 2026-09-10 | 2026-09-22 | 24mo | CB | Multi-year, CZ known wrong |
| C-0BBE3E60 | 2027-09-26 | 2026-09-26 | 24mo | CB | CZ had wrong year (2027) |
| C-0F5D2323 | 2026-09-10 | 2026-09-29 | 24mo | CB | Multi-year, CZ known wrong |

---

## Account-by-Account Detail

### HIGH RISK

**1. C-0F5D2323** — Cole Ingram — $90,647 — Renewal: Sep 29 (CB, FLAG: CZ had Sep 10)
- Seat utilization: 111/390 = **28.5%**
- 3-month usage trend: STABLE (+3.5%, 19.7 vs 19.0 avg)
- **HIGH** — Critically low seat utilization (28.5%); paying for 390 seats but only 111 active. Stable usage at a very low base suggests chronic under-adoption, not a temporary dip.

**2. C-0EC6999D** — Elena Sinclair — $79,419 — Renewal: Oct 3
- Seat utilization: 31/112 = **27.7%**
- 3-month usage trend: STABLE (+6.7%, 16.0 vs 15.0 avg)
- **HIGH** — Lowest utilization in portfolio at 27.7%; 31 of 112 seats active. Stable usage at a tiny base signals the account never adopted at scale.

**3. C-0B7D2C30** — Dana Mercer — $65,901 — Renewal: Sep 15 (CB, FLAG: CZ had Sep 10)
- Seat utilization: 274/476 = **57.6%**
- 3-month usage trend: **DECLINING** (−18.1%, 91.7 vs 112.0 avg)
- **HIGH** — Steady 18% usage decline over 6 months with active users dropping from 155 to 84. Utilization below 60% amplifies the risk.

**4. C-0BCDB8C2** — Cole Ingram — $54,427 — Renewal: Sep 18 (CB, FLAG: CZ had Sep 18, 2027 — wrong year)
- Seat utilization: 232/424 = **54.7%**
- 3-month usage trend: **DECLINING** (−17.6%, 118.3 vs 143.7 avg)
- **HIGH** — 18% usage decline, active users halved from 200 to 110 over 12 months. Utilization under 55%.

**5. C-0D2AB865** — Elena Sinclair — $38,022 — Renewal: Sep 22 (CB, FLAG: CZ had Sep 10)
- Seat utilization: 250/407 = **61.4%**
- 3-month usage trend: **DECLINING** (−18.9%, 117.0 vs 144.3 avg)
- **HIGH** — 19% usage decline is steepest in portfolio; active users fell from 199 to 109. Utilization at 61% is moderate but the trajectory is unmistakable.

**6. C-0BBE3E60** — Dana Mercer — $30,993 — Renewal: Sep 26 (CB, FLAG: CZ had Sep 26, 2027 — wrong year)
- Seat utilization: 74/114 = **64.9%**
- 3-month usage trend: **DECLINING** (−19.5%, 35.7 vs 44.3 avg)
- **HIGH** — 20% usage decline is the sharpest drop; active users nearly halved from 63 to 33 over 12 months. Utilization is decent but the contraction rate is alarming.

---

### LOW RISK

**7. C-0B20DB64** — Dana Mercer — $21,770 — Renewal: Oct 7
- Seat utilization: 214/378 = **56.6%**
- 3-month usage trend: **STABLE** (+0.1%, 295.3 vs 295.0 avg)
- **LOW** — Remarkably flat usage (293–298 range all year); no churn signal despite moderate utilization.

**8. C-0BBC4E7A** — Cole Ingram — $56,374 — Renewal: Oct 10
- Seat utilization: 228/337 = **67.7%**
- 3-month usage trend: **STABLE** (−0.9%, 140.7 vs 142.0 avg)
- **LOW** — Stable usage in a tight band (139–142); 67.7% utilization is healthy.

**9. C-0FD551AB** — Elena Sinclair — $48,815 — Renewal: Oct 14
- Seat utilization: 210/376 = **55.9%**
- 3-month usage trend: **STABLE** (−1.6%, 123.7 vs 125.7 avg)
- **LOW** — Stable usage with no directional trend; 55.9% utilization is moderate but not declining.

**10. C-0F9F8F13** — Dana Mercer — $46,230 — Renewal: Oct 18
- Seat utilization: 199/352 = **56.5%**
- 3-month usage trend: **STABLE** (+0.2%, 184.0 vs 183.7 avg)
- **LOW** — Flat usage across the full year (181–185 range); no concerns.

**11. C-0BC34584** — Cole Ingram — $16,740 — Renewal: Oct 22
- Seat utilization: 327/494 = **66.2%**
- 3-month usage trend: **STABLE** (+1.0%, 104.7 vs 103.7 avg)
- **LOW** — Stable, slightly growing usage; 66.2% utilization is solid.

**12. C-0B7A7546** — Elena Sinclair — $35,062 — Renewal: Oct 25
- Seat utilization: 182/205 = **88.8%**
- 3-month usage trend: **GROWING** (+4.4%, 64.0 vs 61.3 avg)
- **LOW** — Growing usage and 88.8% utilization — one of the healthiest accounts in portfolio.

**13. C-0B369871** — Dana Mercer — $85,128 — Renewal: Oct 29
- Seat utilization: 317/422 = **75.1%**
- 3-month usage trend: **GROWING** (+4.3%, 329.7 vs 316.0 avg)
- **LOW** — Consistent growth from 289 to 333 active users; 75.1% utilization is strong.

**14. C-0B144C78** — Cole Ingram — $30,899 — Renewal: Nov 2
- Seat utilization: 169/224 = **75.4%**
- 3-month usage trend: **GROWING** (+3.0%, 102.7 vs 99.7 avg)
- **LOW** — Growing usage, 75.4% utilization — healthy account.

**15. C-0FC4DBB8** — Elena Sinclair — $94,732 — Renewal: Nov 5
- Seat utilization: 356/464 = **76.7%**
- 3-month usage trend: **GROWING** (+4.2%, 191.0 vs 183.3 avg)
- **LOW** — Largest ARR in portfolio, growing usage from 168 to 193, 76.7% utilization. Strongest account.

**16. C-0D5BBE3A** — Dana Mercer — $39,740 — Renewal: Nov 9
- Seat utilization: 85/102 = **83.3%**
- 3-month usage trend: **GROWING** (+3.9%, 89.7 vs 86.3 avg)
- **LOW** — Growing usage, 83.3% utilization — near capacity.

**17. C-0FB9D5AF** — Cole Ingram — $63,158 — Renewal: Nov 13
- Seat utilization: 144/199 = **72.4%**
- 3-month usage trend: **GROWING** (+4.6%, 174.0 vs 166.3 avg)
- **LOW** — Steady growth from 154 to 176 active users; 72.4% utilization.

**18. C-0B344485** — Elena Sinclair — $64,384 — Renewal: Nov 16
- Seat utilization: 224/287 = **78.0%**
- 3-month usage trend: **STABLE** (+0.9%, 224.0 vs 222.0 avg)
- **LOW** — Stable usage at 224 active users for 6 months; 78% utilization is strong.

**19. C-0CB2C1B4** — Dana Mercer — $40,628 — Renewal: Nov 20
- Seat utilization: 386/473 = **81.6%**
- 3-month usage trend: **STABLE** (+0.1%, 307.3 vs 307.0 avg)
- **LOW** — Flat usage all year (300–308 range); 81.6% utilization is very healthy.

**20. C-22170CA1** — Cole Ingram — $45,646 — Renewal: Nov 24
- Seat utilization: 251/294 = **85.4%**
- 3-month usage trend: **GROWING** (+2.6%, 145.7 vs 142.0 avg)
- **LOW** — Growing usage, highest utilization in portfolio at 85.4%.

---

## Summary

| Metric | Value |
|---|---|
| **Total ARR renewing (next 90 days)** | **$1,048,715** |
| **ARR at HIGH risk** | **$359,409** (34.3% of renewing base) |
| **High-risk accounts** | 6 of 20 |
| **Low-risk accounts** | 14 of 20 |

**Key callout:** The 6 high-risk accounts are concentrated in the first 30 days (Sep 15–Oct 3). Three are multi-year contracts where ChurnZero had the wrong date entirely (C-0BCDB8C2 and C-0BBE3E60 showed 2027 instead of 2026). Two more (C-0EC6999D, C-0F5D2323) have critically low seat utilization below 30% — these accounts are paying for capacity they never activated. CSMs Dana Mercer and Cole Ingram carry 3 of the 6 high-risk accounts each; Elena Sinclair has none in high-risk but manages the two lowest-utilization accounts (C-0EC6999D at 27.7%).
ticket-theme-synthesisPASS 1.00
Plain-English question: What are the real themes in this quarter's support tickets, ignoring the existing tags?
Prompt — what the model saw
Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.
cost $0.0368 · 445s · in 2,999 / out 22,594 tok
✓ theme_count_range✓ counts_verifiable✓ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

Plain-English answer: 80 tickets in 4-7 themes — points-not-posting leads (20 tickets, 25%, $31,100 ARR affected) — each with counts, distinct accounts, ARR, and a recommendation.
The answer in one line: total_tickets = 80; quarter_window = 2026-06-01 through 2026-08-31
total_tickets80
noteexisting tags are noisy/unreliable; classify by text
quarter_window2026-06-01 through 2026-08-31
ranking_by_arr_exposurehris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync
sample_ticket_ids80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041
single_account_noise_themebilling_dispute
themes{"points_not_posting": {"count": 20, "share_pct": 25.0, "distinct_accounts": 9, "arr_affected": "31100.00", "example_ticket_ids": ["IC-460001", "IC-460002"], "single_account": false}, "slack_sync": {"
graded checkstheme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

show the model's full answer
**Quarterly Support Ticket Synthesis (Q2–Q3 2026) — 80 tickets, 5 themes, ranked by ARR exposure**

---

### 1. HRIS Provisioning Failures — $114,000 | 12 tickets (15%)

- **Count:** 12 of 80
- **Share:** 15%
- **Distinct accounts:** 3 — C-0B2213A9 ($36K), C-0F6C0F34 ($30K), C-0DDFC9A7 ($48K)
- **ARR affected:** $36,000 + $30,000 + $48,000 = **$114,000**
- **Sample tickets:** IC-460059, IC-460053
- **One-line recommendation:** Root-cause the HRIS sync pipeline — silent skip of 12+ new hires (IC-460062) with no error log indicates a batch-processing bug rather than a config issue, and the three accounts span June–August with no resolution.

---

### 2. Redemption Failures — $68,800 | 18 tickets (22.5%)

- **Count:** 18 of 80
- **Share:** 22.5%
- **Distinct accounts:** 7 — C-0CEF69FD ($8.9K), C-0B827671 ($10.7K), C-0FCCD2DF ($9.6K), C-0F876796 ($8.7K), C-14264ABD ($11.0K), C-0D9CA315 ($9.6K), C-0B0F1BAB ($10.3K)
- **ARR affected:** $8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300 = **$68,800**
- **Sample tickets:** IC-460025, IC-460038
- **One-line recommendation:** Investigate the checkout → gift-card fulfillment path end-to-end; points are deducted pre-confirmation (IC-460024, IC-460023) and the error is silent, creating a liability double-hit.

---

### 3. Invoice / Billing Errors (C-0E9C27D1) — $52,000 | 16 tickets (20%)

- **Count:** 16 of 80
- **Share:** 20%
- **Distinct accounts:** 1 — C-0E9C27D1 ($52K)
- **ARR affected:** **$52,000** (single account, highest per-account ARR in the dataset)
- **Sample tickets:** IC-460071, IC-460080
- **One-line recommendation:** Escalate immediately — one $52K account filed 16 tickets over 3 months (wrong-tier renewal charges, phantom seat counts, invoice discrepancies) with no apparent fix; churn risk is severe.

---

### 4. Points Not Posting / Balance Stale — $31,100 | 20 tickets (25%)

- **Count:** 20 of 80
- **Share:** 25%
- **Distinct accounts:** 9 — C-0D3278C7 ($3.5K), C-0BF20542 ($4.5K), C-0D0B047C ($4.5K), C-0BE96399 ($2.7K), C-0D284E42 ($3.4K), C-0D6CC8E3 ($4.2K), C-21FEBCBB ($2.9K), C-0DD0626C ($2.5K), C-0B2895EF ($2.9K)
- **ARR affected:** $3,500 + $4,500 + $4,500 + $2,700 + $3,400 + $4,200 + $2,900 + $2,500 + $2,900 = **$31,100**
- **Sample tickets:** IC-460004, IC-460007
- **One-line recommendation:** Fix the recognition → ledger write-back pipeline — recognitions show "delivered" but points never post, affecting 9 small-to-mid accounts across the entire quarter (June through August).

---

### 5. Slack Integration Broken — $18,900 | 14 tickets (17.5%)

- **Count:** 14 of 80
- **Share:** 17.5%
- **Distinct accounts:** 4 — C-0B843542 ($4.4K), C-10A56B0F ($5.4K), C-0BA71F12 ($3.9K), C-8C2E8F00 ($5.2K)
- **ARR affected:** $4,400 + $5,400 + $3,900 + $5,200 = **$18,900**
- **Sample tickets:** IC-460041, IC-460044
- **One-line recommendation:** Three sub-issues (sync toggle reset, re-auth not sticking, slash command errors) all trace to the same OAuth token lifecycle — fix the refresh mechanism and all four accounts likely resolve.

---

**Summary:** $284,800 total ARR exposed across 24 unique accounts. Highest concentration by account: HRIS (3 accounts, $114K). Highest ticket volume: Points (20 tickets) but smallest per-account ARR. The single-account billing cluster (C-0E9C27D1, $52K) is the highest-priority individual account risk.
marketing 5 tests
case-study-matchPASS 1.00
Plain-English question: Which three existing customers are the best case-study matches for this prospect?
Prompt — what the model saw
Given the prospect, rank the three most similar existing customers with public case studies for use as social proof. Similarity on industry, size band, use case, region. Cite the fields that drove each match. Do not name any customer without a case study and never include billing data or contact names.
cost $0.0048 · 26s · in 0 / out 1,214 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

Plain-English answer: Three ranked matches on industry, size, use case, and region (e.g. C-11C31562), with the higher-scoring decoy explicitly excluded for a mismatched field.
The answer in one line: prospect_alias = C-82AF3719
constraintsno_billing_data, no_contact_names, only_customers_with_has_case_study_true_named
decoys_excludedcompany_alias=C-D6217CAA; similarity_score=4; reason=no_case_study | company_alias=C-C153868F; similarity_score=4; reason=no_case_study
prospect_aliasC-82AF3719
prospect_fields_usedindustry, size_band, use_case, region
top3company_alias=C-11C31562; similarity_score=3 | company_alias=C-64171065; similarity_score=3 | company_alias=C-A13C193D; similarity_score=3
graded checkstop3 · decoys_excluded · cites_fields · no_billing

Model's actual answer

show the model's full answer
**Prospect:** C-82AF3719 — Technology, Mid-Market, employee_recognition, NA-West

**8 customers with case studies exist.** Ranked by similarity:

---

### #1 — C-64171065
| Field | Match |
|---|---|
| Industry | **Technology** ✓ |
| Size band | **Mid-Market** ✓ |
| Use case | **employee_recognition** ✓ |
| Region | NA-East (same country, adjacent subregion) |

3 of 4 fields exact. Same use case + same vertical makes this the strongest social proof.

---

### #2 — C-A13C193D
| Field | Match |
|---|---|
| Industry | **Technology** ✓ |
| Size band | **Mid-Market** ✓ |
| Use case | retention (engagement-adjacent) |
| Region | **NA-West** ✓ |

3 of 4 fields exact. Region match is perfect but use case differs (retention vs. employee_recognition — both engagement programs). Slightly weaker than #1 because the prospect's explicit use case is recognition, which #1 shares.

---

### #3 — C-11C31562
| Field | Match |
|---|---|
| Industry | Manufacturing (different vertical) |
| Size band | **Mid-Market** ✓ |
| Use case | **employee_recognition** ✓ |
| Region | **NA-West** ✓ |

3 of 4 fields exact. Same size, use case, and region. Manufacturing is a different industry, but the recognition program in the same size band and region is still compelling social proof.

---

**Arithmetic:** 8 case-study customers → 3 filtered by ≥3 fields matching prospect. No other customer with a case study hits that threshold. No billing data or contact names cited.
channel-attribution-mathPASS 1.00
Plain-English question: Compute cost per SQM, cost per SQO, and pipeline per dollar for every channel over six months.
Prompt — what the model saw
Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.
cost $0.0164 · 136s · in 2,166 / out 8,413 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

Plain-English answer: LinkedIn Ads at $960/SQM and $3,000/SQO, organic search at $90,000 pipeline on zero spend, paid social flagged at $18,000 with zero SQMs, and a channel winner named.
The answer in one line: best_pipeline_per_dollar_channel = paid_search
best_pipeline_per_dollar_channelpaid_search
confidence_notewebinars and referral have small SQM samples (<20); treat rates as directional.
flagged_rows_sqo_before_sqmCT-000041, CT-000044
months2026-03, 2026-04, 2026-05, 2026-06, 2026-07, 2026-08
per_channel{"linkedin_ads": {"spend_usd": 24000, "sqms": 25, "sqos": 8, "pipeline_amount": 96000, "cost_per_sqm": 960.0, "cost_per_sqo": 3000.0, "sqm_to_sqo_rate": 0.32, "pipeline_per_dollar": 4.0}, "organic_sea
reallocation_recommendationfrom=paid_social; to=paid_search; rationale=paid_social has spend but zero SQMs (cost per SQM undefined, not zero); paid_search has the highest pipeline per dollar.
undefined_channelspaid_social
graded checksundefined_channel · undefined_not_zero · flagged_rows · best_channel · reallocation

Model's actual answer

show the model's full answer
## Channel Performance Report — Trailing 6 Months (Mar–Aug 2026)

### Paid Channels

| Metric | paid_search | linkedin_ads | paid_social | webinars |
|---|---|---|---|---|
| **Total spend** | $36,000 | $24,000 | $18,000 | $9,000 |
| **SQMs** | 40 | 24 | **0** | 12 |
| **SQOs** | 18 | 8 | **0** | 5 |
| **Cost per SQM** | $900 | $1,000 | **undefined** (zero SQMs) | $750 |
| **Cost per SQO** | $2,000 | $3,000 | **undefined** (zero SQOs) | $1,800 |
| **SQM→SQO rate** | 45.0% | 33.3% | **undefined** | 41.7% |
| **Pipeline** | $720,000 | $96,000 | $0 | $60,000 |
| **Pipeline per $1** | $20.00 | $4.00 | $0.00 | $6.67 |

- **paid_search** arithmetic: 40 SQMs → 18 SQOs at $36K spend. $36,000 ÷ 40 = $900/SQM. $36,000 ÷ 18 = $2,000/SQO. 18 ÷ 40 = 45%. 18 × $40,000 = $720,000 pipeline. $720,000 ÷ $36,000 = $20 pipeline per dollar.
- **linkedin_ads** arithmetic: 24 SQMs → 8 SQOs at $24K. $24,000 ÷ 24 = $1,000/SQM. $24,000 ÷ 8 = $3,000/SQO. 8 ÷ 24 = 33.3%. 8 × $12,000 = $96,000 pipeline. $96,000 ÷ $24,000 = $4 pipeline per dollar.
- **paid_social**: $18,000 spent over 6 months produced **zero contacts with an SQM or SQO in the contacts file**. Cost metrics are undefined; pipeline is $0.
- **webinars** arithmetic: 12 SQMs → 5 SQOs at $9K. $9,000 ÷ 12 = $750/SQM. $9,000 ÷ 5 = $1,800/SQO. 5 ÷ 12 = 41.7%. 5 × $12,000 = $60,000 pipeline. $60,000 ÷ $9,000 = $6.67 pipeline per dollar.

### Organic Channels

| Metric | organic_search | referral |
|---|---|---|
| **Volume (SQMs)** | 30 | 15 |
| **SQOs** | 10 | 6 |
| **SQO rate** | 33.3% | 40.0% |
| **Pipeline** | $90,000 | $48,000 |

- **organic_search**: 30 SQMs → 10 SQOs. 10 ÷ 30 = 33.3%. 10 × $9,000 = $90,000 pipeline.
- **referral**: 15 SQMs → 6 SQOs. 6 ÷ 15 = 40.0%. 6 × $8,000 = $48,000 pipeline.

### Data-Quality Flags: SQO Date Precedes SQM Date

Two contacts in **linkedin_ads** have SQO dates before their SQM dates — suggesting a data-entry error or a different attribution touch:

| Contact | SQM Date | SQO Date | Flag |
|---|---|---|---|
| CT-000044 | 2026-07-23 | 2026-07-18 | SQO (Jul 18) **before** SQM (Jul 23) |
| CT-000041 | 2026-06-14 | 2026-06-09 | SQO (Jun 9) **before** SQM (Jun 14) |

Both are counted in the totals above since they have valid SQM and SQO entries, but the date inversion should be investigated. If these two are excluded, linkedin_ads drops to 6 valid SQOs (25% rate, $4,000/SQO, $3.33 pipeline/$).

### Reallocation Recommendation

**paid_search is the clear winner** — $20 pipeline per dollar spent, 45% SQM→SQO conversion, and the largest absolute pipeline ($720K). It's the most efficient paid channel by every metric.

**paid_social is a dead zone** — $18K spent, zero SQMs attributed in the contacts file. This spend should be reallocated. The most natural move: shift paid_social's $3,000/month ($18K total) to **paid_search** (dominant performer) or split between paid_search and webinars (higher SQM→SQO rate than linkedin_ads at lower cost per SQO).

**linkedin_ads** underperforms paid_search on every paid metric and has two data-quality issues. Its $4K/month is generating only 33% of paid_search's pipeline per dollar.

**Recommended reallocation:**
- Cut **paid_social** ($3K/mo → $0)
- Reallocate $2K/mo to **paid_search** (now $8K/mo) and $1K/mo to **webinars** (now $2.5K/mo)
- Estimated uplift: paid_search at $8K/mo × 6 mo = $48K → projected 48 SQMs (extrapolating 40 SQMs/$36K) → 21.6 SQOs → $864K pipeline. Webinars at $2.5K/mo × 6 = $15K → projected 20 SQMs → 8.3 SQOs → $100K pipeline. Combined gain: ~$184K incremental pipeline vs status quo.

### Confidence Assessment

| Channel | SQM sample | Confidence |
|---|---|---|
| **paid_search** | 40 SQMs | **High** — largest sample, consistent monthly spend, clean dates |
| **linkedin_ads** | 24 SQMs | **Moderate** — reasonable volume but 2 date inversions erode trust in attribution |
| **paid_social** | 0 SQMs | **None** — cannot evaluate; zero attributed contacts is itself a finding |
| **webinars** | 12 SQMs | **Low–Moderate** — small sample but clean data and consistent pattern |
| **organic_search** | 30 SQMs | **Moderate–High** — solid volume for an organic channel |
| **referral** | 15 SQMs | **Moderate** — decent organic volume, clean dates |

**Bottom line**: paid_search and organic_search have the most reliable signals. The paid_social kill/reallocation recommendation is high-confidence based on six months of zero attributed pipeline. linkedin_ads and webinars would benefit from more data and, in linkedin_ads' case, a data-quality cleanup before further investment increases.
competitor-battlecard-updatePASS 1.00
Plain-English question: Update the Rivally battlecard with sourced pricing and where each side wins.
Prompt — what the model saw
Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.0101 · 81s · in 586 / out 4,697 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

Plain-English answer: Rivally at $7 per user per month (the August source wins over the stale $5, conflict noted), a 13-7 win/loss record, and two unverified old claims flagged.
The answer in one line: competitor = Rivally
citation_ids_available25 items — e.g. S01, S02, S03, S04
competitorRivally
pricingvalue_per_user_per_month=7; source_snippet=S17; source_date=2026-08-12
rep_opinion_snippet_ids_not_factsS09, S21
unverified_old_claimsRivally lacks a Slack integration., Rivally was acquired by WorkHuman in 2025.
win_loss_12mowins=13; losses=7
graded checkscompetitor_named · pricing · win_loss · unverified_marked · citations

Model's actual answer

show the model's full answer
Let me carefully analyze all data before writing the battlecard.

**Old card claims to verify:**

1. "points-based recognition for mid-market" → S03 mentions "Rivally Recognition" at $5/user/mo (product name implies points-based). S07 says "reporting dashboards are basic compared to enterprise tools" which implies mid-market target. Supported. Kept.

2. "pricing starts at $5 per user/month, annual billing (as of 2026-01)" → S03 confirms $5/user/mo, annual billing. But S17 (Aug 2026) shows $7. Conflict exists. Updated below.

3. "Rivally lacks a Slack integration" → S04 (Feb 2026) says "Slack integration worked out of the box." This directly contradicts. S19 (Aug 2026) also shows Teams app v2. Marked as unverified/incorrect.

4. "Rivally was acquired by WorkHuman in 2025" → No snippet supports this. Cannot re-source. Marked unverified.

5. "Strong in EU enterprise with multi-language support" → S12 supports. S15 supports. Verified.

**Sourcing rules:**
- S09 (call_notes, AE opinion) = rep opinion, not a fact about Rivally. Skip.
- S21 (call_notes, AE opinion) = rep opinion, not confirmed. Skip.
- Everything else is fair game.

**12-month win/loss:**
Sep 2025 - Aug 2026 = 12 months. 20 deals total.

Let me verify: deals_with_competitor.csv has 20 rows. All between Sep 2025 and Aug 2026. That's 12 months of data.

Wins: Deal-A9FD43 (Oct 2025), Deal-7AA785 (Nov 2025), Deal-44C524 (Dec 2025), Deal-0D0CD6 (Jan 2026), Deal-D5B790 (Feb 2026), Deal-5C636E (Mar 2026), Deal-67BE14 (Jun 2026), Deal-1B6969 (Jul 2026), Deal-F03E7B (Aug 2026), Deal-072E31 (Sep 2025), Deal-F65C8F (Oct 2025)... wait, Deal-F65C8F is Oct 2025 and outcome is "loss". Let me recheck.

Deal-F65C8F: Oct 2025, loss. Not a win.

Let me recount carefully:

Wins: A9FD43 (Oct 2025), 7AA785 (Nov 2025), 44C524 (Dec 2025), 0D0CD6 (Jan 2026), D5B790 (Feb 2026), 5C636E (Mar 2026), 67BE14 (Jun 2026), 1B6969 (Jul 2026), F03E7B (Aug 2026), 072E31 (Sep 2025), E46EAB (Jan 2026), 1D2392 (Feb 2026) = 12 wins

Losses: 7767F5 (Sep 2025), 5645A5 (Apr 2026), C6FFAA (May 2026), D263E0 (Nov 2025), 935746 (Dec 2025), 9066A6 (Mar 2026), 72A02F (Apr 2026), F65C8F (Oct 2025) = 8 losses

Total: 20 deals, 12 wins, 8 losses = 60% win rate.

Now let me write the battlecard.# Battlecard: Rivally

**Last updated:** 2026-09-06  
**Data source:** `competitor_snippets.csv`, `deals_with_competitor.csv`, `existing_battlecard.md`  
**Note on sourcing:** Every claim below is tagged with a snippet ID. Claims from the old card that could not be re-sourced are marked **UNVERIFIED**. Rep opinions from call notes are excluded (S09, S21).

---

## One-line positioning

Points-based employee recognition platform for mid-market companies. [S03, S07]

---

## Pricing (with source and date)

| Price point | Source | Date | Notes |
|---|---|---|---|
| $5/user/month, annual billing | S03 — pricing page | 2026-01-20 | Original list price (now outdated) |
| $6.50/user/month | S13 — call notes (deal mention) | 2026-06-02 | Quoted to a 500-seat prospect |
| **$7/user/month, annual billing** | **S17 — pricing page** | **2026-08-12** | **Current list price (newest source)** |
| $7/user/month list, 15% discount for 3-year term | S18 — call notes (deal mention) | 2026-08-14 | Confirms $7 list; shows discounting on multi-year |

**Conflict:** The pricing page increased from $5 (Jan 2026, S03) to $7 (Aug 2026, S17). The $6.50 quote in June 2026 (S13) likely reflects a transitional period or a negotiated rate. The August pricing page (S17) is the newest official source and sets the current list price at $7/user/month. The old card's "$5" figure is stale.

---

## Where they win

- **EU enterprise / distributed EU teams** — strong multi-language support, Dublin office, EU data residency. [S12, S15]
- **Quick deployment** — setup under a week, Slack integration works out of the box. [S04]
- **Fast support** — under 4-hour response time. [S22]
- **Lightweight product** — easy to adopt, low friction. [S04, S06]

---

## Where we win

- **Analytics depth** — 800-seat prospect chose us over Rivally specifically citing analytics depth. [S25]
- **Reporting** — Rivally's dashboards described as "basic compared to enterprise tools." [S07]
- **SCIM provisioning** — Rivally lacks SCIM; manual user management is painful for enterprise buyers. [S10]
- **Bulk recognition editing** — Rivally admin console doesn't support it. [S24]
- **Admin tooling** — Rivally lags peers in admin capabilities. [S16]
- **Migration friction** — leaving Rivally is hard because analytics exports are CSV-only. [S20]

---

## Objections and responses

| Objection | Response |
|---|---|
| "Rivally is cheaper at $5/user/mo" | That price is outdated — Rivally's pricing page now shows $7/user/mo as of Aug 2026 [S17]. At $7/user/mo, Rivally is not a price leader. |
| "But Rivally quoted us $6.50" | That was a June 2026 quote [S13]. The Aug 2026 pricing page shows $7 list [S17], and a later deal mention confirms $7 list with a 15% discount for 3-year terms [S18]. The discount suggests they're under pressure to hold onto multi-year commitments. |
| "Rivally has Slack integration" | True — they do have Slack integration [S04]. But they lack SCIM provisioning [S10], bulk recognition editing [S24], and their analytics exports are CSV-only [S20]. These gaps matter at scale. |
| "Rivally is strong in Europe" | They are — Dublin office, EU data residency, multi-language support [S11, S12, S15]. But our analytics depth still wins head-to-head evaluations [S25]. |
| "Rivally's support is fast" | They do have fast support response [S22]. Highlight that we match or exceed that SLA, plus our admin tooling is more mature [S10, S24]. |

---

## Recent changes (past 6 months)

- **May 2026** — Hires ex-Workday VP EMEA to lead European expansion. [S11]
- **Jun 2026** — Prices quoted at $6.50/user/mo for 500-seat prospect. [S13]
- **Jul 2026** — Opens Dublin office; EU data residency goes GA. [S15]
- **Aug 2026** — Pricing page updates to $7/user/month list. [S17]
- **Aug 2026** — Offers 15% discount for 3-year terms. [S18]
- **Aug 2026** — Microsoft Teams app v2 enters public preview. [S19]
- **Sep 2026** — Rivally Pulse add-on exits beta, priced as add-on (not bundled). [S23]

---

## Our 12-month win/loss record against Rivally (Sep 2025 – Aug 2026)

| Metric | Count |
|---|---|
| Total deals | 20 |
| Wins | 12 |
| Losses | 8 |
| **Win rate** | **60%** |

**Deal breakdown (chronological):**

| Month | Deals | Wins | Losses | Deal aliases |
|---|---|---|---|---|
| Sep 2025 | 2 | 1 | 1 | 7767F5 (L), 072E31 (W) |
| Oct 2025 | 2 | 1 | 1 | A9FD43 (W), F65C8F (L) |
| Nov 2025 | 2 | 1 | 1 | 7AA785 (W), D263E0 (L) |
| Dec 2025 | 2 | 1 | 1 | 44C524 (W), 935746 (L) |
| Jan 2026 | 2 | 2 | 0 | 0D0CD6 (W), E46EAB (W) |
| Feb 2026 | 2 | 2 | 0 | D5B790 (W), 1D2392 (W) |
| Mar 2026 | 2 | 1 | 1 | 5C636E (W), 9066A6 (L) |
| Apr 2026 | 2 | 0 | 2 | 5645A5 (L), 72A02F (L) |
| May 2026 | 1 | 0 | 1 | C6FFAA (L) |
| Jun 2026 | 1 | 1 | 0 | 67BE14 (W) |
| Jul 2026 | 1 | 1 | 0 | 1B6969 (W) |
| Aug 2026 | 1 | 1 | 0 | F03E7B (W) |

**Note:** April and May 2026 were our worst months (0-3 combined). We've won 3 straight since June.

---

## Old card claims — verification status

| Old claim | Verdict | Source |
|---|---|---|
| "Points-based recognition for mid-market" | **Verified** | S03, S07 |
| "Pricing starts at $5/user/month, annual billing (as of 2026-01)" | **Updated** — now $7/user/month as of Aug 2026 | S17 |
| "Rivally lacks a Slack integration" | **UNVERIFIED / CONTRADICTED** — S04 (Feb 2026) says Slack integration works out of the box. No source found for the original claim. | S04 |
| "Rivally was acquired by WorkHuman in 2025" | **UNVERIFIED** — no snippet in the dataset supports this. Cannot confirm. | — |
| "Strong in EU enterprise with multi-language support" | **Verified** | S12, S15 |
nurture-sequence-diagnosisPASS 1.00
Plain-English question: What is broken in each email sequence — rates, overlaps, tracking errors?
Prompt — what the model saw
Review the sequences. Per sequence: sent, open rate, reply rate, meeting rate, weakest step. Identify tracking errors (opened above sent), audience overlap between sequences, and the failure mode for anything under 2 percent reply. One change per weak sequence and which to fix first. Under 350 words.
cost $0.0100 · 282s · in 3,591 / out 2,090 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

Plain-English answer: Per-sequence open, reply, and meeting rates with the weakest step (all three weakest at step 3), an opened-above-sent tracking error, and audience overlap flagged.
The answer in one line: fix_priority_first = Cold Outbound - HR Leaders
all_pairwise_overlaps['Cold Outbound - HR Leaders', 'Cold Outbound - People Ops'], ['Expansion Nurture', 'New Logo Nurture']
audience_overlap_pairCold Outbound - HR Leaders, Cold Outbound - People Ops
failure_modeCold Outbound - HR Leaders=opens healthy (~37%) but replies near zero from step 1 -> body/CTA not landing, not a deliverability or subject-line problem
fix_priority_firstCold Outbound - HR Leaders
per_sequence{"New Logo Nurture": {"sent": 1386, "opened": 490, "replied": 90, "meetings": 27, "open_rate": 0.3535, "reply_rate": 0.0649, "meeting_rate": 0.0195, "weakest_step": 3}, "Expansion Nurture": {"sent": 8
tracking_errorsequence=Expansion Nurture; step=2; sent=300; opened=340; issue=opened exceeds sent
under_2pct_replyCold Outbound - HR Leaders
graded checkstracking_error · overlap_pair · weakest_step · fix_priority

Model's actual answer

show the model's full answer
**New Logo Nurture** — Step 1: 42.0% open, 8.4% reply, 2.4% meeting. Step 2: 34.9% open, 6.6% reply, 2.0% meeting. Step 3: 28.0% open, 4.2% reply, 1.4% meeting. **Weakest: Step 3** (1.4% meeting rate). Fix: add a social-proof or case-study CTA to convert lukewarm step-2 responders.

**Expansion Nurture** — Step 1: 43.3% open, 7.3% reply, 1.7% meeting. Step 2: 340 opened on 300 sent = **113.3% open rate — tracking error** (opened > sent). Step 3: 34.5% open, 4.4% reply, 1.1% meeting. **Weakest: Step 3** (1.1% meeting). **Fix first: audit the Step 2 pixel or CRM counting logic** — inflated opens corrupt the funnel view and make every downstream decision wrong.

**Cold Outbound - HR Leaders** — All three steps under 2% reply: Step 1 0.83%, Step 2 0.34%, Step 3 0.17%. **Zero meetings across 600 contacts.** Failure mode: **ICP/offer mismatch** — HR Leaders aren't the buyer for whatever is being sold. Fix: pause this sequence, re-target to the right persona, rewrite offer.

**Cold Outbound - People Ops** — Step 1 3.5% reply / 0.75% meetings, Step 2 2.3% / 0.52%, Step 3 1.6% / 0.27%. **Weakest: Step 3** (1.6% reply, under 2% threshold). Fix: reduce to 2-step sequence or add personalization — diminishing returns by touch 3.

**Audience overlap detected:** CT-000301 appears in both *New Logo Nurture* and *Expansion Nurture*. Multiple contacts (CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001103, CT-001105, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345) appear in both *Cold Outbound - HR Leaders* and *Cold Outbound - People Ops* — these contacts are being double-dipped across parallel sequences, inflating total reach counts and diluting per-sequence attribution.

**Priority order to fix:** 1) **Expansion Nurture Step 2 tracking error** — unreliable data poisons all other analysis. 2) **HR Leaders ICP/pause** — zero-meetings-at-600 is the worst absolute outcome. 3) **Deduplicate audiences** across the two cold sequences. 4) **New Logo Step 3 CTA** and **People Ops Step 3 trim.**
weekly-marketing-goals-rollupPASS 1.00
Plain-English question: Are we on pace on the quarter's marketing goals?
Prompt — what the model saw
Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.
cost $0.0057 · 36s · in 0 / out 1,936 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

Plain-English answer: SQMs ahead of pace, SQOs on pace, DS2s behind (35 short of expected at day 66 of 92), with deltas and same-quarter closes.
The answer in one line: days_in_quarter = 92; days_elapsed = 66; coverage_ratio_active_pipeline = 0.75; elapsed_fraction = 0.7174 (+1 more below)
days_in_quarter92
coverage_ratio_active_pipeline0.75
days_elapsed66
elapsed_fraction0.7174
metrics{"SQMs": {"qtd_actual": 230, "target": 300, "delta": -70, "expected_at_day_elapsed": 215.22, "pace": "ahead"}, "SQOs": {"qtd_actual": 84, "target": 120, "delta": -36, "expected_at_day_elapsed": 86.09,
pace_ruleexpected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.
this_week_paragraph_required1
graded checksmetrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer
## Weekly Marketing Goals Update — Q3-2026 (66 of 92 days elapsed, 71.7%)

---

### SQMs (higher is better)

| QTD Actual | Target | Delta | Pace |
|-----------|-------|-------|------|
| 230 | 300 | −70 | **Ahead** (230 vs expected 215.2 at 71.7%) |

Arithmetic: 300 × (66/92) = 215.2 expected. 230 > 215.2 → ahead of pace.

---

### SQOs (higher is better)

| QTD Actual | Target | Delta | Pace |
|-----------|-------|-------|------|
| 84 | 120 | −36 | **Behind** (84 vs expected 86.1) |

Arithmetic: 120 × (66/92) = 86.1 expected. 84 < 86.1 → behind pace.

---

### DS2s (higher is better)

| QTD Actual | Target | Delta | Pace |
|-----------|-------|-------|------|
| 40 | 75 | −35 | **Behind** (40 vs expected 53.8) |

Arithmetic: 75 × (66/92) = 53.8 expected. 40 < 53.8 → behind pace.

---

### Closed-Lost MIA Rate (lower is better)

| QTD Actual | Target | Delta | Pace |
|-----------|-------|-------|------|
| 20.0% (5/25) | 10.0% | +10.0 pp | **Behind** (rate is double target) |

Arithmetic: 5 MIA out of 25 closed-lost = 20.0%. Target is 10.0%. Delta = 20.0% − 10.0% = +10.0 pp (worse direction). Rate metrics don't accumulate linearly with days, so pace is assessed against the target rate directly.

---

### Same-Quarter Closes (higher is better)

| QTD Actual | Target | Delta | Pace |
|-----------|-------|-------|------|
| 10 | 20 | −10 | **Behind** (10 vs expected 14.3) |

Arithmetic: 20 × (66/92) = 14.3 expected. 10 < 14.3 → behind pace.

---

### Active Pipeline Coverage (higher is better)

| QTD Actual ($) | Target ($) | Delta ($) | Pace |
|--------------|-----------|----------|------|
| 3,000,000 | 4,000,000 | −1,000,000 | **Ahead** ($3.0M vs expected $2.87M) |

Arithmetic: $4,000,000 × (66/92) = $2,869,565 expected. $3,000,000 > $2,869,565 → ahead of pace.

---

### What Moved This Week

Only QTD cumulative data was provided — no week-over-week or weekly-sliced figures exist in the files, so I cannot isolate what moved specifically in the past seven days. On a quarter-to-date basis, the story is mixed: SQMs ($230) and active pipeline ($3.0M) are both ahead of linear pace despite trailing their absolute targets, which suggests sourcing velocity is healthy. SQOs (84 vs 86.1 expected), DS2s (40 vs 53.8), and same-quarter closes (10 vs 14.3) are all behind pace, indicating a conversion or qualification bottleneck between SQM → SQO → DS2. The closed-lost MIA rate at 20% is double the 10% target — one in five closed-lost deals has no recorded reason, which is a data hygiene concern that undermines root-cause analysis on why deals are falling out.
communication 5 tests
ceo-slack-compression0.60
Plain-English question: Compress the forecast analysis into one Slack message the CEO can read in ten seconds.
Prompt — what the model saw
Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.
cost $0.0044 · 20s · in 0 / out 942 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✗ caveat_present✗ no_em_dash

✓ Correct Answer (ground truth)

Plain-English answer: Three sentences, under 80 words, decision first, every distinct number kept plus the one caveat — all checked by deterministic text rules.
The answer in one line: weighted_number = 115976; must_keep = data-quality caveat — deleting the bad news fails
weighted_number115,976
limitmax 3 sentences, max 80 words, no em-dashes
must_keepdata-quality caveat — deleting the bad news fails
graded checksmax_3_sentences · under_80_words · weighted_present · caveat_present · no_em_dash

Model's actual answer

show the model's full answer
Q3 weighted forecast is 115,977 but the data is untrustworthy. 85 of 86 deals have no owner, 71 of 86 have no why-buy (including all 7 COMMIT), and 32 deals carry October close dates. Without clean data this number is a precision illusion — do not base decisions on it.
stalled-deal-followup-emailPASS 1.00
Plain-English question: Write a follow-up email for this stalled deal — under 120 words, one prior touch, one ask.
Prompt — what the model saw
Write a follow-up email for this stalled deal. Under 120 words. Reference one specific prior touch from the engagement history, make one clear ask, and match the tone of the prior thread. No invented details.
cost $0.0040 · 16s · in 0 / out 710 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

Plain-English answer: An email that references the July 28 milestone-awards demo, makes one clear ask, and invents nothing — Deal-0D2F7A, 31 days since last engagement.
The answer in one line: amount = 5100; word_cap = 120; days_since_last_engagement = 31; deal_alias = Deal-0D2F7A (+4 more below)
amount5,100
days_since_last_engagement31
word_cap120
allowed_facts14 items — e.g. Deal-0D2F7A, $5,100, DS3, Alex Franklin
allowed_newENG-00001, ENG-00002, ENG-00003, ENG-00004, ENG-00005
deal_aliasDeal-0D2F7A
must_referenceengagement_id=ENG-00002; date=2026-07-28; type=meeting; why=most substantive prior touch; concrete prospect reactions to milestone awards and points catalog
ownerAlex Franklin
single_cta1
stageDS3
tonewarm, concise, professional (matches prior thread)
graded checksword_cap · references_touch · single_ask

Model's actual answer

show the model's full answer
**Subject:** Following up on Deal-0D2F7A pricing

Hi there,

Following up on the pricing recap I sent August 5 for 150 seats. After the strong reaction to the automated milestone awards and points catalog during our July 28 demo, I wanted to check whether you or the VP People have any questions on the proposal.

Do you have 15 minutes this week to discuss next steps?

Best,
Alex Franklin
funnel-digest-paragraphPASS 1.00
Plain-English question: Write the weekly GTM digest — one paragraph per team, every number traceable.
Prompt — what the model saw
Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.0044 · 18s · in 0 / out 1,005 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

Plain-English answer: Four paragraphs (Marketing, Sales, CS, PLG) using only the allowed numbers, no contact names, no performance commentary, under 400 words.
The answer in one line: word_cap = 400
word_cap400
allowed_numbers16 items — e.g. 2, 3, 9, 08
bannedcustomer contact names, employee performance commentary, compensation
required_paragraphsMarketing, Sales, CS, PLG
graded checksfour_paragraphs · word_cap · teams

Model's actual answer

show the model's full answer
**Marketing** — SQMs landed at 46 this week against a 52-target. Webinar led the channel mix with 18 SQMs. Paid search cost per SQM held at $150 — efficient unit economics, but we'll need incremental volume to close the 6-SQM gap.

**Sales** — The team converted 14 SQOs and set 9 DS2 meetings. New pipeline created totaled $310,000, with 3 same-quarter closes booked. Solid near-term execution with a healthy mix of newly sourced and progressing opportunities.

**CS** — Renewals saved this week: 2. Team NPS moved to 61. Three accounts remain red-flagged heading into next week — a manageable watchlist, but one worth prioritizing given the recent retention momentum.

**PLG** — 412 new signups joined this week with activation at 31%. 38 companies hit the aha moment of 10 recognition gives — a strong leading indicator that product-led motion is driving engagement beyond the initial signup.
partner-digest-section0.67
Plain-English question: Write the partner digest so every partner in the roster appears exactly once.
Prompt — what the model saw
Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0041 · 15s · in 0 / out 797 tok
✓ all_partners✗ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

Plain-English answer: Four active partners with status and numbers, four quiet partners with one line each, and partner-sourced pipeline that reconciles to the deal data.
The answer in one line: rule = every roster partner named exactly once; quiet partners get one line; no partners outside roster.
active_no_dealsWorkWell Group
partner_sourced_pipelinecount=5; amount=550000
per_partner_pipeline{"Apex Rewards Co": {"count": 2, "amount": 275000}, "HRCloud Partners": {"count": 1, "amount": 140000}, "CultureBridge": {"count": 2, "amount": 135000}}
quiet_partnersRecogniq, KudosWave, PeopleFirst Advisors, TotalPerk
rosterpartner_name=Apex Rewards Co; status=active | partner_name=HRCloud Partners; status=active | partner_name=CultureBridge; status=active | partner_name=WorkWell Group; status=active | partner_name=Recogniq; status=quiet | partner_name=KudosWave; status=quiet … (+2 more)
ruleevery roster partner named exactly once; quiet partners get one line; no partners outside roster.
graded checksall_partners · pipeline_amount · quiet_one_line

Model's actual answer

show the model's full answer
# Partner Digest: 2026-08-24 to 2026-09-04

## Active Partners

**Apex Rewards Co** — Co-webinar locked for Sept 15. Sourced 2 deals ($275K): Deal-DDAAF2 ($180K) and Deal-2CF33E ($95K), both in DS1 with UTM source = Partner.

**HRCloud Partners** — Integration referral closed security review, opp moved to DS2. Sourced 1 deal ($140K): Deal-F1CDA5.

**CultureBridge** — Lunch-and-learn produced 2 sourced opps ($135K): Deal-096E1D ($60K) and Deal-067213 ($75K), both early stage.

**WorkWell Group** — No sourced deals this period. Planning call booked Sept 9 to restart joint playbook in Q4.

## Quiet Partners (no activity this period)

Recogniq | KudosWave | PeopleFirst Advisors | TotalPerk

---

**Pipeline summary**: 5 sourced deals totaling $550K. Count matches Slack: Apex (2) + HRCloud (1) + CultureBridge (2) = 5. Amount matches CSV: $275K + $140K + $135K = $550K. WorkWell, Recogniq, KudosWave, PeopleFirst Advisors, TotalPerk contributed $0.
rfp-answer-from-policyPASS 1.00
Plain-English question: Answer the 10 RFP items from the policy excerpts only — no guessing.
Prompt — what the model saw
Answer the 10 questionnaire items from the policy excerpts only. Cite the excerpt id for every answer. Where the excerpts do not answer the question, mark it unanswerable and say what document would; do not guess. Tone: vendor response, direct.
cost $0.0045 · 23s · in 0 / out 1,075 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

Plain-English answer: Seven answered with excerpt citations (Q1-Q7), three flagged unanswerable with the document that would answer them (Q8-Q10).
The answer in one line: answered_count = 7; rule = unanswerable items must be flagged and the document that would answer them named; do not guess.
answered_count7
answer_map{"Q1": ["P1"], "Q2": ["P1"], "Q3": ["P2"], "Q4": ["P3"], "Q5": ["P4"], "Q6": ["P5"], "Q7": ["P6"]}
ruleunanswerable items must be flagged and the document that would answer them named; do not guess.
unanswerableQ8, Q9, Q10
valid_citation_idsP1, P2, P3, P4, P5, P6, P7, P8
graded checksanswered_count · unanswerable_flagged · citations · no_guess

Model's actual answer

show the model's full answer
**Vendor Response — Questionnaire from Policy Excerpts**

| Item | Answer | Source |
|------|--------|--------|
| **Q1** — Encryption at rest? | Yes. All customer data is encrypted at rest using AES-256. | P1 |
| **Q2** — Encryption in transit? | Yes. Data in transit is encrypted with TLS 1.2 or higher. | P1 |
| **Q3** — SOC 2 report available? | Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. The report is available to prospects under NDA. | P2 |
| **Q4** — Data retention after termination? | Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems. | P3 |
| **Q5** — Subprocessor list and change notification? | The current subprocessor list is published at trust.bonusly.com. Customers receive 30 days' notice before a new subprocessor is added. | P4 |
| **Q6** — SAML single sign-on? | Yes. SAML 2.0 single sign-on (and SCIM user provisioning) are available on the Enterprise plan. | P5 |
| **Q7** — Backup schedule and retention? | Production databases are backed up daily. Backups are encrypted and retained for 35 days. | P6 |
| **Q8** — ISO 27001 certification? | **Unanswerable** from the provided excerpts. No ISO 27001 certification is mentioned in the policy documents supplied. A separate ISO 27001 certificate or statement would address this. | — |
| **Q9** — Contractual uptime SLA percentage? | **Unanswerable** from the provided excerpts. No uptime SLA percentage is mentioned in the policy documents supplied. A service level agreement or contract would address this. | — |
| **Q10** — HIPAA Business Associate Agreement? | **Unanswerable** from the provided excerpts. No HIPAA compliance posture or BAA willingness is mentioned in the policy documents supplied. A HIPAA compliance statement or BAA policy document would address this. | — |

All answers are drawn exclusively from the policy excerpts provided. No data was invented or extrapolated.
ops-maintenance 5 tests
skill-file-reconciliationPASS 1.00
Plain-English question: Audit a skill set against its manifest and flag every conflict with a severity and an action.
Prompt — what the model saw
Reconcile the skill set below against its manifest. Find and report (with severity CRITICAL/WARNING/INFO and an action type TRIM_DESC, UPDATE_BODY, DELETE_SKILL, MERGE, or REVIEW): (1) any two skills whose ALWAYS-trigger phrases overlap/duplicate, (2) any circular delegation chain between skills (name it), (3) any delegation target that does not exist (dangling, name it), (4) any version conflict and which skill should survive, (5) how many manifest descriptions exceed 1,024 characters, (6) any hardcoded page ids, dates, or person names in skill bodies, (7) manifest drift in BOTH directions (files with no manifest row, and manifest rows with no file). Make one proposal per finding; rewrite nothing.
cost $0.0620 · 166s · in 66,245 / out 8,925 tok
✓ duplicate_trigger_found✓ circular_chain✓ dangling_target✓ oversized_desc_count✓ hardcoded_values✓ manifest_drift✓ severity_and_action

✓ Correct Answer (ground truth)

Plain-English answer: A severity-ranked list of TRIM_DESC / UPDATE_BODY / DELETE_SKILL / MERGE / REVIEW actions covering every rule violation found.
The answer in one line: files_audited = 14; rank = CRITICAL / WARNING / INFO with one fix action per issue
files_audited14
findingsduplicate trigger, circular delegation chain, dangling file reference, oversized description, hardcoded values, manifest drift
rankCRITICAL / WARNING / INFO with one fix action per issue
graded checksduplicate_trigger_found · circular_chain · dangling_target · oversized_desc_count · hardcoded_values · manifest_drift · severity_and_action

Model's actual answer

show the model's full answer
# Skill Set Reconciliation Report

## (1) Overlapping/Duplicate ALWAYS-Trigger Phrases — CRITICAL — MERGE

**Skills:** `comms-drafter` and `email-drafter`

These two skills share 6+ identical trigger phrases in their descriptions:

| Trigger phrase | comms-drafter | email-drafter |
|---|---|---|
| "write me an email" | ✓ | ✓ |
| "draft a follow-up" | ✓ | ✓ |
| "help me reply" | ✓ | ✓ |
| "what should I say" | ✓ | ✓ |
| "bump email" | ✓ | ✓ |
| "contract nudge" | ✓ | ✓ |

Both claim to handle email drafting for AEs, SDRs, and CSMs. `comms-drafter` claims broader scope ("any external communication"); `email-drafter` claims email-only scope. A user saying "draft a follow-up" or "bump email" would hit both with no routing rule to distinguish them. Neither has a lane marker pointing to the other.

**Proposal:** Merge `email-drafter` into `comms-drafter`. `comms-drafter` is the superset skill (covers email + Intercom + partner + all external comms). `email-drafter`'s Gmail signature extraction and HubSpot context pull procedures consolidate into `comms-drafter`. Delete `email-drafter` after merge.

---

## (2) Circular Delegation Chain — NONE FOUND

No circular delegation chain exists among the 14 manifest skills. The only delegation chain is linear:
- `next-to-close` → `pipeline-intelligence-report` → `closed-lost-analysis`

No skill in that chain delegates back to an earlier link.

---

## (3) Dangling Delegation Targets — CRITICAL — REVIEW

Ten delegation targets referenced in skill bodies do not exist in the manifest:

| Referenced skill | Referenced by | Times referenced |
|---|---|---|
| `bonusly-data-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-product-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-business-reporting-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-rewards-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-ppp-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-feature-flag-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-deal-desk-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-datadog-questions` | `analysis-validator` §12.4 | 1 |
| `bonusly-brand` | `comms-drafter` Step 0, `email-drafter` Core Rules, `sales-forecast` quality gates | 3 |
| `prospect-research-multithreading` | `deal-strategy-coach` cross-skill handoff, `comms-drafter` unknown-recipient section, `email-drafter` unknown-recipient section | 3 |

`signalforge-reports` is also referenced by `pipeline-intelligence-report` (Phase 5) and `weekly-pipeline-report` (Step 4) — it's an org skill path (`/mnt/skills/organization/signalforge-reports/`), not a manifest skill. This is a structural reference, not a delegation target, so excluded from the dangling count.

**Proposal:** REVIEW — determine which of these are intended to be in the manifest vs. org skills vs. deprecated. `bonusly-brand` and `prospect-research-multithreading` are the most critical (3 references each, actively invoked in workflows). The eight `bonusly-*` specialist skills (data-questions, product-questions, etc.) are referenced only by `analysis-validator` §12.4 as validation delegation targets — if they exist outside the manifest, document their location.

---

## (4) Version Conflict — CRITICAL — MERGE

**Skills:** `comms-drafter` and `email-drafter`

Neither has an explicit version number, but they are competing implementations of the same capability. Both:
- Claim to draft emails for AEs, SDRs, and CSMs
- Reference `deal-strategy-coach` as the strategy handoff target
- Reference `bonusly-brand` for brand voice
- Reference `prospect-research-multithreading` for contact lookup
- Share 6+ overlapping trigger phrases

`comms-drafter` is the superset (email + Intercom + partner + support), has richer routing logic (Step 3 routes by communication type), and includes a review/rating workflow. `email-drafter` has Gmail signature extraction that `comms-drafter` lacks.

**Proposal:** `comms-drafter` should survive. Fold `email-drafter`'s signature extraction and HubSpot personalization procedures into `comms-drafter`. Delete `email-drafter`.

---

## (5) Manifest Descriptions Exceeding 1,024 Characters — 0

| Skill | Description chars | Exceeds 1,024? |
|---|---|---|
| analysis-validator | 656 | NO |
| closed-lost-analysis | 897 | NO |
| comms-drafter | 996 | NO |
| deal-strategy-coach | 792 | NO |
| email-drafter | 965 | NO |
| model-selection | 676 | NO |
| next-to-close | 945 | NO |
| partner-digest | 1,004 | NO |
| pipeline-intelligence-report | 1,006 | NO |
| sales-forecast | 962 | NO |
| signalforge-claim-compressor | 1,006 | NO |
| signalforge-feedback | 708 | NO |
| stale-pipeline-report | 762 | NO |
| weekly-pipeline-report | 656 | NO |

None exceed 1,024 characters. The longest is `pipeline-intelligence-report` and `signalforge-claim-compressor` at 1,006.

---

## (6) Hardcoded Page IDs, Dates, and Person Names — WARNING — REVIEW

Hardcoded values found in skill bodies (representative sample; not exhaustive):

**Person names (dated — will stale):**
- `analysis-validator` §12.3: "Bryce Harmon", "Dana Mercer", "Cole Ingram", "Alex Franklin", "Gavin Porter", "Alaina Loori", "Shealagh Coughlin", "Colleen Perry", "Ellie Barton", "Ashley Reyer", "Megan Franz", "Elena Sinclair", "Youssef Elkhateeb", "Amanda Czenkus", "Ben Castelli", "Amani Phipps", "John Thomas", "Yasmin Wahid" — labeled "Updated May 4, 2026" (4 months stale)
- `deal-strategy-coach`: same roster + "Farid" (India routing), "Perseus" (India routing partner)

**Hardcoded dates:**
- `analysis-validator` changelog: "May 9, 2026" (6 entries), "May 4, 2026" (2 entries), "April 26, 2026"
- `deal-strategy-coach`: "May 4, 2026" (GTM roster), "April 2026" (pricing table header), "May 2026" (system constants)
- `partner-digest`: "May 16, 2026" (reference issue), "2026-05-17" (changelog)
- `pipeline-intelligence-report`: "May 2026" (version label)
- `weekly-pipeline-report`: "Q2 2026" (April 1 – June 30, 2026), Q1 2026 static context ($365,152, 77%, $2,490,532, 76%)
- `sales-forecast`: Q2 hardcoded in references

**Hardcoded IDs:**
- `analysis-validator` §12.2: Deal stage IDs (150582536, 150582537, 150582538, 150582539, 1175632767)
- `analysis-validator` §12.3: HubSpot owner IDs (119337721, 77260721, 83155923, etc.)
- `analysis-validator` §12.4: Confluence page IDs (none here — those are in other skills)
- `pipeline-intelligence-report`: Same stage IDs, same owner IDs, HubSpot org ID "1973303"
- `partner-digest`: cloudId "73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f", spaceId "1958248479", folder ID "2286616609"
- `sales-forecast`: spaceId "2232811524", cloudId "73fe98de-a4a3-4869-9f8a-bb1eeed4cf7f", parent page ID "2232582148"
- `signalforge-feedback`: page ID "2295136266", spaceId "2232811524", parent "2234417154"
- `weekly-pipeline-report`: spreadsheet IDs "1CLZeOsElVDF_LF0ZG_t2nfwvhnZ6bpwqM_nX3WEYzcw", "1ENuaEcCuLjdKhMvp8FK3Ys1ek5Aw9ZuOZhsHJJFoB_k"
- `stale-pipeline-report`: channel ID "C0561C1JCPJ"

**Proposal:** TRIM_DESC for descriptions (none over 1,024 — no action needed). UPDATE_BODY for all skills containing hardcoded values: replace inline dates with `[LIVE QUERY]` tags, replace person names with roster-lookup procedures, and mark IDs with source-of-truth references. The GTM roster in `analysis-validator` §12.3 and `deal-strategy-coach` is the most time-sensitive (4 months stale). The Confluence/Spreadsheet IDs are stable but should be centralized rather than duplicated across 5+ skills.

---

## (7) Manifest Drift (Both Directions) — NONE

**Files with no manifest row:** 0 of 14 files are unregistered. Every `.SKILL.md` file has a matching row in `skill_manifest.csv`.

**Manifest rows with no file:** 0 of 14 rows are orphaned. Every manifest `file` value has a corresponding `.SKILL.md` file.

The manifest is fully reconciled with the on-disk skill set. No drift in either direction.

---

## Summary Table

| # | Finding | Severity | Action | Skills Affected |
|---|---|---|---|---|
| 1 | Overlapping ALWAYS-trigger phrases (6+ phrases shared) | CRITICAL | MERGE | `comms-drafter` ← `email-drafter` |
| 2 | Circular delegation chain | NONE | — | — |
| 3 | Dangling delegation targets (10 skills referenced, not in manifest) | CRITICAL | REVIEW | `analysis-validator`, `comms-drafter`, `email-drafter`, `deal-strategy-coach`, `sales-forecast` |
| 4 | Version conflict (competing implementations) | CRITICAL | MERGE | `comms-drafter` should survive |
| 5 | Descriptions exceeding 1,024 chars | 0 | — | — |
| 6 | Hardcoded page IDs, dates, person names | WARNING | UPDATE_BODY | 9 of 14 skills affected |
| 7 | Manifest drift (files ↔ manifest) | NONE | — | — |
runbook-from-slack-threadPASS 1.00
Plain-English question: Turn the resolved incident thread into a runbook.
Prompt — what the model saw
Turn the resolved incident thread into a runbook: ordered steps with the exact command or action taken, who ran it, how success was verified, and rollback for each step that changed state. Every step must trace to a message in the thread. Add nothing the thread does not contain; where the thread is unclear, mark the step as needing confirmation.
cost $0.0056 · 33s · in 0 / out 1,813 tok
✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

Plain-English answer: Ordered steps from the thread, each with the exact command, who ran it, how success was verified, and rollback where state changed.
The answer in one line: rule = no steps beyond what the thread contains; every step traces to a message id.
ambiguous_steporder=4; msg=M05; mark=needs-confirmation
ruleno steps beyond what the thread contains; every step traces to a message id.
stepsorder=1; msg=M01; actor=Bryce Harmon; action=acknowledge PagerDuty alert, take IC; state_change=False; verified_by=M01 | order=2; msg=M02; actor=Farid Osman; action=check queue depth; command=bundle exec rake sidekiq:queue_depth; state_change=False; verified_by=M02 (48,213 pending) | order=3; msg=M04; actor=Farid Osman; action=pause auto-enqueue via feature flag; command=bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'; state_change=True; rollback=bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'; verified_by=M07 (queue depth falling) | order=4; msg=M05; actor=Elena Sinclair; action=cleared the dead set; state_change=True; needs_confirmation=thread gives no command or method for clearing the dead set | order=5; msg=M06; actor=Bryce Harmon; action=scale reward-worker to 6 replicas; command=kubectl scale deployment/reward-worker --replicas=6; state_change=True; rollback=kubectl scale deployment/reward-worker --replicas=3; verified_by=M07/M08 | order=6; msg=M08; actor=Cole Ingram; action=verify queue drained and error rate baseline; command=bundle exec rake sidekiq:queue_depth; state_change=False; verified_by=M08 (returns 0) … (+2 more)
graded checksordered_steps · rollback · needs_confirmation · sourced

Model's actual answer

show the model's full answer
# Incident Runbook: reward-worker queue backlog (#incident-2026-08-29-reward-queue)

---

## Step 1 — Assess queue depth

| Field | Detail |
|---|---|
| **Action** | `bundle exec rake sidekiq:queue_depth` |
| **Run by** | Farid Osman (M02) |
| **Result** | 48,213 pending jobs (normal baseline: <500) |
| **Verification** | Command output itself — no further check needed |
| **Rollback** | N/A — read-only |

---

## Step 2 — Inspect dead job set

| Field | Detail |
|---|---|
| **Action** | **UNKNOWN** — the thread says "Dead set has 112 jobs, all Redis::TimeoutError from around 13:58" (M03) but does not record the exact command. **Needs confirmation.** |
| **Run by** | Farid Osman (M03) |
| **Result** | 112 dead jobs, error type `Redis::TimeoutError`, timestamp ~13:58Z |
| **Verification** | Dead-set count visible in Sidekiq UI or console output |
| **Rollback** | N/A — read-only |

---

## Step 3 — Pause auto-recognition enqueue (stop the bleed)

| Field | Detail |
|---|---|
| **Action** | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` |
| **Run by** | Farid Osman (M04) |
| **Verification** | Implicit — no new jobs were enqueued while workers drained the backlog |
| **Rollback** | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |

---

## Step 4 — Clear dead set

| Field | Detail |
|---|---|
| **Action** | **UNKNOWN** — Elena says "cleared out the dead set" from the Rails console (M05) but the exact command is not recorded. Likely `Sidekiq::DeadSet.new.clear` or a UI action. **Needs confirmation.** |
| **Run by** | Elena Sinclair (M05) |
| **Verification** | Implicit — dead-set count returned to 0 |
| **Rollback** | **Not specified in thread.** Dead jobs cannot be restored once cleared. |

---

## Step 5 — Scale workers up

| Field | Detail |
|---|---|
| **Action** | `kubectl scale deployment/reward-worker --replicas=6` |
| **Run by** | Bryce Harmon (M06) |
| **Previous state** | 3 replicas |
| **Verification** | Queue depth dropped from 48,213 → 9,400 at ~1,200/min (M07) |
| **Rollback** | `kubectl scale deployment/reward-worker --replicas=3` |

---

## Step 6 — Monitor queue drain (observation only)

| Field | Detail |
|---|---|
| **Action** | None — Farid read the queue depth and computed the drain rate (M07) |
| **Run by** | Farid Osman (M07) |
| **Result** | Queue at 9,400, falling ~1,200/min |
| **Rollback** | N/A — observation only |

---

## Step 7 — Verify full drain and baseline error rate

| Field | Detail |
|---|---|
| **Action** | `bundle exec rake sidekiq:queue_depth` + Datadog error-rate check |
| **Run by** | Cole Ingram (M08) |
| **Result** | Queue depth: 0. Datadog error rate: back to baseline. |
| **Verification** | Command output + Datadog dashboard |
| **Rollback** | N/A — read-only |

---

## Step 8 — Re-enable enqueue

| Field | Detail |
|---|---|
| **Action** | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |
| **Run by** | Bryce Harmon (M09) |
| **Verification** | 40 new jobs processed cleanly in the following 3 minutes (M09) |
| **Rollback** | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` |

---

## Step 9 — Scale workers back to normal

| Field | Detail |
|---|---|
| **Action** | `kubectl scale deployment/reward-worker --replicas=3` |
| **Run by** | Bryce Harmon (M10) |
| **Verification** | Queue stable at 0 (M10) |
| **Rollback** | `kubectl scale deployment/reward-worker --replicas=6` |

---

### Items needing confirmation

1. **Step 2** — Exact command used to inspect the dead set (e.g. `Sidekiq::DeadSet.new.check` or a rake task).
2. **Step 4** — Exact command used to clear the dead set (e.g. `Sidekiq::DeadSet.new.clear` or the Sidekiq Web UI "Delete All" button).

### Root cause (from thread)

The incident was triggered by a spike of `Redis::TimeoutError` failures around 13:58Z that filled the dead set with 112 jobs while the live queue swelled to 48,213. The thread does not identify *what* caused the Redis timeouts. That investigation is outside the scope of this runbook.
datadog-incident-timelinePASS 1.00
Plain-English question: Sequence the failure from the logs — what broke first, and what cascaded after it?
Prompt — what the model saw
From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.
cost $0.0060 · 32s · in 907 / out 1,649 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

Plain-English answer: A reward-service Redis timeout at 14:01:12Z cascades to sidekiq at 14:01:40 and api-gateway at 14:03:05, with the Datadog query that confirms the first error.
The answer in one line: job_class_involved = RewardGiveJob
cascade_orderservice=reward-service; first_error_at=2026-09-03T14:01:12Z | service=sidekiq; first_error_at=2026-09-03T14:01:40Z | service=api-gateway; first_error_at=2026-09-03T14:03:05Z | service=web-app; first_error_at=2026-09-03T14:03:30Z
clean_servicespostgres
confirming_queryservice:reward-service status:error "@message:Redis::TimeoutError"
first_errortimestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
job_class_involvedRewardGiveJob
logs_do_not_showroot cause of the Redis timeout, redis-server's own logs, infrastructure metrics (CPU/memory/network), any deploy or config change preceding the incident, who applied mitigation or when (no actor fields in logs)
graded checksfirst_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

show the model's full answer
**First error:** `2026-09-03T14:01:12Z` — **reward-service** ERROR `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`

**Cascade in order:**

1. **14:01:12Z** — reward-service ERROR: Redis connection to `redis-primary:6379` times out after 5s
2. **14:01:20Z** — reward-service ERROR: retry exhausted for `RewardGiveJob`
3. **14:01:30Z** — reward-service ERROR: retry exhausted for `RewardGiveJob`
4. **14:01:40Z** — reward-service ERROR: retry exhausted for `RewardGiveJob`
5. **14:01:40Z** — sidekiq ERROR: `RewardGiveJob` failed (Redis::TimeoutError); retrying in 60s
6. **14:02:28Z** — sidekiq ERROR: `RewardGiveJob` failed; retrying
7. **14:02:30Z** — sidekiq WARN: Queue `reward` depth above 10,000
8. **14:03:05Z** — api-gateway ERROR: 502 upstream timeout calling `reward-service /gives`
9. **14:03:30Z** — web-app ERROR: Give form submission failed: upstream 502 from api-gateway
10. *Continues* — sidekiq retries, api-gateway 502s, web-app failures repeat through ~14:06:52Z
11. **14:10:56Z+** — postgres checkpoints run normally (unaffected)
12. **14:22:10Z** — reward-service INFO: Redis connection restored; resuming job processing
13. **14:24:45Z** — sidekiq INFO: Queue depth below 500

**Jobs involved:** `RewardGiveJob` (primary, 12 jobs in `sidekiq_jobs.csv`), `RecognitionDigestJob` (secondary, 4 jobs in `sidekiq_jobs.csv`). All failed with `Redis::TimeoutError`.

**Datadog query to confirm the first error:**
```
service:reward-service level:error "Redis::TimeoutError" "Connection to Redis at redis-primary:6379 timed out after 5s"
```
Or, broader to catch the onset:
```
service:reward-service level:error "Redis::TimeoutError" "redis-primary:6379"
```

**What the logs do not show:**
- Any log from the Redis service itself — no `redis` service entries exist in the slice
- Why Redis was unreachable (network partition, Redis process crash, OOM kill, DNS resolution failure, resource exhaustion)
- Any infrastructure or platform-layer events (container restart, host failure, load balancer state)
- The total number of Give form submission failures or affected users
- The peak queue depth beyond the single "above 10,000" warning
- The exact retry count per job before exhaustion
- Any alert or paging event triggered by the incident
feature-flag-state-summaryPASS 1.00
Plain-English question: Summarize what each feature flag controls and who has it on.
Prompt — what the model saw
Summarize the state of the listed feature flags: what each controls per the code excerpt, which companies or segments have it on, targeting rules, and any flag with no code reference or no targets. Company counts must match the export. Do not describe a flag that is not in the export.
cost $0.0048 · 25s · in 0 / out 1,259 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

Plain-English answer: Recognition streaks on for 42 beta companies, redeem-flow redesign off with a 12-company target list, plus any flag with no code reference or no targets.
The answer in one line: rule = company counts must equal the export; do not describe flags absent from the export.
code_referencedrecognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2
flags{"recognition_streaks_v2": {"state": "on", "targeting_rules": "segment:beta_companies", "company_count": 42}, "points_budget_guardrails": {"state": "on", "targeting_rules": "all_companies", "company_c
flags_with_no_targetspaused_offboard_cleanup
orphan_flags_no_code_referencelegacy_give_modal, survey_boosters_q3, paused_offboard_cleanup
rulecompany counts must equal the export; do not describe flags absent from the export.
graded checksorphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer
## Feature Flag Summary

### Flags with Code References

| Flag | State | What It Controls (per code) | Targeting | Companies |
|---|---|---|---|---|
| **recognition_streaks_v2** | **on** | After a give, calls `StreakTracker.record(give)` in `app/models/recognition.rb` | `segment:beta_companies` | 42 |
| **points_budget_guardrails** | **on** | Calls `BudgetService.new(company).enforce!(giver, points)` in `app/services/budget_service.rb` | `all_companies` | 220 |
| **slack_dm_nudges** | **on** | Sends `SlackDm.send_nudge(user)` in `app/jobs/nudge_job.rb` | `segment:region_na` | 87 |
| **redeem_flow_redesign** | **off** | Renders `RedeemV2Component` when on, `RedeemV1Component` when off (in `app/controllers/redeem_controller.rb`) | `targeted_list` | 12 |
| **analytics_dashboard_v3** | **on** | Instantiates `AnalyticsV3` dashboard in `app/controllers/analytics_controller.rb` | `segment:tier_three` | 65 |
| **ms_teams_app_v2** | **off** | Calls `TeamsAppV2.install(company)` in `app/services/teams_installer.rb` | `targeted_list` | 9 |

### Flags with No Code Reference

| Flag | State | Targeting | Companies |
|---|---|---|---|
| **legacy_give_modal** | off | `segment:legacy_plan` | 14 |
| **survey_boosters_q3** | on | `segment:legacy_plan` | 7 |
| **paused_offboard_cleanup** | off | *(empty)* | 0 |

### Summary

- **6 of 9 flags** have a matching code reference in the excerpt.
- **3 flags have no code reference** — `legacy_give_modal` (off, 14 companies on `legacy_plan`), `survey_boosters_q3` (on, 7 companies on `legacy_plan`), and `paused_offboard_cleanup` (off, 0 companies, empty targeting). These may be deprecated, pending removal, or defined elsewhere.
- **1 flag has no targets**: `paused_offboard_cleanup` — empty targeting rules and 0 companies.
- **Company counts** match the export exactly (no arithmetic needed beyond reading the CSV).
nda-triagePASS 1.00
Plain-English question: Triage each NDA green, yellow, or red — and quote the clause that decides it.
Prompt — what the model saw
Triage each NDA as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Cite the clause that drives each classification by quoting it. Catch embedded non-solicits, non-competes, missing carve-outs, and unusual term or governing law. One line of reasoning per NDA.
cost $0.0046 · 23s · in 0 / out 1,130 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

Plain-English answer: NDA-1 green (standard mutual terms), NDA-2 yellow for an embedded 18-month non-solicit, with the driving clause quoted for every classification.
The answer in one line: non_solicit_in = NDA-2
checksclassification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked
classifications{"NDA-1": {"classification": "GREEN", "driving_reason": "standard mutual NDA: 2-year term, standard carve-outs, Delaware law", "driving_clause_quote": "Carve-outs: information that (a) is or becomes p
non_solicit_inNDA-2
graded checksgreen · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer
## NDA Triage

---

### NDA-1 — [PARTY A] and Bonusly → **GREEN**

**Reasoning:** Standard mutual NDA. Term (2 years) and survival (3 years) are conventional. All five standard carve-outs are present (public domain, prior knowledge, third-party receipt, independent development, legal compulsion). No non-solicit, no non-compete, no exclusivity. Delaware governing law is unobjectionable for a US-based company.

**Clause that drives classification:** Carve-outs §3 — all five standard exceptions are explicitly listed, no gaps.

**Flagged items:** None.

---

### NDA-2 — [PARTY B] and Bonusly → **YELLOW**

**Reasoning:** Mutual NDA with standard carve-outs and Delaware law — all baseline GREEN. However, §4 contains an **embedded non-solicitation clause:**

> *"During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement."*

This is mutual and scoped to employees actually contacted during the evaluation, which is reasonable but imposes a contractual obligation beyond pure confidentiality. Counsel should confirm the 18-month tail and the "hire" restriction (which goes beyond solicitation) are acceptable given the potential partnership scope. Not a RED because it is mutual and scoped, but warrants counsel sign-off.

**Clause that drives classification:** §4 non-solicitation (quoted above).

**Flagged items:** Non-solicit with 18-month tail; "hire" language broader than "solicit."

---

### NDA-3 — [PARTY C] and Bonusly → **RED**

**Reasoning:** Three material red flags:

1. **Non-compete clause** (§2):
   > *"For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business."*

   This is a **broad, indefinite-scope non-compete** — no limitation to the evaluation purpose, no defined competitive business, and a 3-year duration starting from the *Effective Date* (not post-termination). It would restrict Bonusly's ability to work with any competitor of [PARTY C] for three full years.

2. **Missing carve-outs** (§4): No standard confidentiality exceptions are stated — no public domain, prior knowledge, independent development, or legal-compulsion carve-out. This means even obviously non-confidential information would be technically covered.

3. **Governing law: Republic of Ireland** (§3):
   > *"This Agreement shall be governed by and construed in accordance with the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts."*

   Exclusive Irish jurisdiction imposes significant cost and logistical burden on Bonusly for any dispute resolution.

**Clause that drives classification:** §2 non-compete (quoted above) — this is a deal-stopper without negotiation.

**Flagged items:** Non-compete (broad, 3-year), missing confidentiality carve-outs, 5-year term, Irish exclusive jurisdiction.