← back to all models ← RevenueBench home
minimax

minimax/minimax-m2.1

Mean 0.942 · 31/40 perfect tests · $0.67 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-auditPASS 1.00
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 $0.0302 · 94s · in 41,221 / out 14,149 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:**
- **DS2+** = Deals in stages DS2, DS3, DS4, DS5
- **Substantive why_buys** = 15+ characters OR LONG(n) format (truncated but present)
- **Non-compliant placeholders** = tbd, n/a, see notes, tbc, na, none, '-', empty string
- **Regression** = Earlier-stage timestamp is later than a later-stage timestamp

---

### Why-Buys Compliance (DS2+ Deals)
| Metric | Value |
|--------|-------|
| Total DS2+ deals | 124 |
| Non-compliant | 101 |
| **Non-compliant %** | **81.5%** |

### By Owner
| Owner | Non-Compliant | Total | % Non-Compliant |
|-------|---------------|-------|------------------|
| Bryce Harmon | 15 | 18 | 83.3% |
| Dana Mercer | 18 | 22 | 81.8% |
| Alex Franklin | 48 | 61 | 78.7% |
| Cole Ingram | 19 | 22 | 86.4% |
| Farid Osman | 1 | 1 | 100.0% |

### Other Issues
- **Deals with no DS1 timestamp:** 0
- **Deals with stage regression:** 0

---

### Three Process Fixes
1. **Mandatory why_buys field validation at stage transition** — Block progression from DS2+ until why_buys contains 15+ characters. Use CRM automation (validation rule) to enforce.

2. **Quarterly why_buys audit + owner coaching** — Alex Franklin has the most DS2+ volume (61 deals) and highest absolute non-compliance (48). Target top-of-funnel coaching for owners above 80% non-compliance.

3. **Replace placeholder defaults in CRM** — Default empty why_buys to a prompt ("Describe the customer's business problem and why they need this solution") rather than leaving blank. This reduces accidental empty submissions at stage entry.
owner-resolutionPASS 1.00
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 $0.0132 · 35s · in 24,781 / out 4,007 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
**RESULTS**

**Number of deals resolved:** 156 (all deals resolved)

**Deals whose owner_id has no match in the owners table:** None

**Owner ids that map to archived/deactivated owners:** None

**Total pipeline amount per resolved owner:**

| Owner | Pipeline | # Deals |
|-------|----------|---------|
| Bryce Harmon | $1,054,144.00 | 35 |
| Dana Mercer | $341,195.00 | 24 |
| Alex Franklin | $624,310.00 | 67 |
| Cole Ingram | $288,161.43 | 22 |
| Farid Osman | $4,134.00 | 7 |
| Elena Sinclair | $2,100.00 | 1 |
| **TOTAL** | **$2,314,044.43** | **156** |

All 156 open deals were successfully resolved to active owners in the owners table. The archived owner_ids (1520255671 - Gavin Porter; 77260721 - Hugo Lindqvist) do not appear in any deal records.
gong-hubspot-join-integrityPASS 1.00
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 $0.0351 · 106s · in 47,178 / out 16,667 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
## Call-to-Deal Mapping Integrity Report

### (a) Orphan Gong Rows
**Count:** 30 rows in gong_calls_by_deal_90d.csv whose deal ID has NO match in deals_open

**Sample orphans:**
| deal_alias | hs_deal_id | calls_90d |
|------------|------------|-----------|
| Deal-8FA85D | 60251290957 | 46 |
| Deal-8FC3F9 | 60251649055 | 24 |
| Deal-3B7945 | 60251639682 | 21 |
| Deal-42B265 | 61227242540 | 21 |
| Deal-9CCC42 | 61430316324 | 17 |

These are deals that have been logged in Gong but are no longer in the current open pipeline (likely closed-won, closed-lost, or archived).

---

### (b) Duplicate Conversation Keys
**Count:** 0

All 67 rows in the gong table have `calls_90d` <= `distinct_conversation_keys`. No duplicate conversation keys detected — each call is mapped to a unique conversation key.

---

### (c) DS3+ Deals with Calls
- **Total open deals at DS3 or later:** 85
- **DS3+ deals with at least one logged call:** 25
- **Share with calls:** 29.4%

**Arithmetic:** 25 / 85 = 0.2941 → 29.4%

**Sample DS3+ deals WITH calls:** Deal-547B2B, Deal-215CCA, Deal-A2B47C, Deal-D9A12F, Deal-C6FE92

---

**Key insight:** 70.6% of advanced-pipeline deals (DS3+) have zero logged calls in the past 90 days — a potential engagement gap.
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 $0.0028 · 22s · in 1,208 / out 1,640 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
Looking at this catalog, I need to identify the right tables and apply the documented rules.

**Analysis:**
- `COMPANY_COHORT_SUMMARY` has `M1_GIVES` (recognition count) and `M1_REDEMPTIONS` for first calendar month
- `REDEMPTION_RECORDS_V2` has individual redemption events (`STATE = 'succeeded'`)
- No raw recognition/giving table with giver IDs is visible in the catalog — only the aggregated `M1_GIVES` column
- The catalog explicitly notes that a "filter excluding deleted givers" exists in "some giving queries" and "must NOT be applied to historical giving counts"

Since there's no source table for individual recognitions with giver identifiers in this catalog, I cannot compute unique givers from raw data. I'll use the cohort summary which provides first-month aggregated metrics.

```sql
-- Query: First calendar month metrics per customer company
-- Uses COMPANY_COHORT_SUMMARY for M1 (first calendar month) metrics
-- M1_GIVES = recognition count; M1_REDEMPTIONS = successful redemption count
-- Note: No raw recognition table with giver IDs available in catalog to count unique givers

SELECT
    -- Company identifier from cohort table
    cs.COMPANY_ID,
    
    -- First calendar month as customer (based on FIRST_SUB_PAYMENT_DATE)
    DATE_TRUNC('MONTH', cs.FIRST_SUB_PAYMENT_DATE) AS first_customer_month,
    
    -- Recognition count in first month (M1_GIVES from cohort summary)
    cs.M1_GIVES AS recognition_count,
    
    -- Successful redemption count in first month (M1_REDEMPTIONS from cohort summary)
    cs.M1_REDEMPTIONS AS redemption_count,
    
    -- Unique givers: NOT AVAILABLE
    -- The catalog does not include a recognition-level table with giver identifiers
    -- Only aggregated M1_GIVES exists; cannot derive unique count from it
    NULL AS unique_givers
    
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY cs

WHERE 
    -- Only companies with a subscription (active customers)
    cs.FIRST_SUB_PAYMENT_DATE IS NOT NULL
    
    -- Exclude stale/sandbox companies if any exist in this table
    AND cs.CURRENT_STATUS != 'sandbox'

ORDER BY first_customer_month, cs.COMPANY_ID;
```

**Limitations:**
- **Unique givers**: Cannot be computed — the catalog provides no recognition-level table with giver/contact IDs. The `M1_GIVES` column is pre-aggregated and doesn't expose individual givers.
- **Redemption count**: Uses the `M1_REDEMPTIONS` aggregated column rather than counting from `REDEMPTION_RECORDS_V2` directly (which would require a join on company ID, not shown in the catalog).
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 $0.0381 · 147s · in 26,000 / out 19,561 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 Audit Report

## Completeness Percentages

| Entity | Field | Filled | Total | % |
|--------|-------|--------|-------|---|
| Company | industry | 34 | 34 | 100.0% |
| Company | employee_count | 25 | 34 | 73.5% |
| Company | hq_country | 28 | 34 | 82.4% |
| Contact | email | 52 | 52 | 100.0% |
| Contact | title | 39 | 52 | 75.0% |
| Contact | persona | 37 | 52 | 71.2% |
| Deal | owner | - | - | **MISSING FILE** |
| Deal | stage | - | - | **MISSING FILE** |
| Deal | amount | - | - | **MISSING FILE** |
| Deal | close_date | - | - | **MISSING FILE** |
| Deal | why_buys | - | - | **MISSING FILE** |

---

## Duplicate Company Clusters

### Cluster 1: acme-corp.com
- C-0A092931: industry=Technology, employee_count=500, hq_country=US
- C-0A092932: industry=tech, employee_count=510, hq_country=USA
- **Survivor: C-0A092931** (most complete record)

### Cluster 2: globex.io
- C-0A092933: industry=SaaS, employee_count=200, hq_country=US
- C-0A092934: industry=Technology, employee_count=200, hq_country=US
- **Survivor: C-0A092933** (most complete record)

---

## Invalid Emails (4 records)

| contact_key | company_alias | email | Issue |
|-------------|---------------|-------|-------|
| CT-0010 | C-66D1FC | user0@ | Missing domain |
| CT-0080 | C-92D97D | user0@ | Missing domain |
| CT-0081 | C-92D97D | user1@ | Missing domain |
| CT-0192 | C-425E2A | user2@ | Missing domain |

---

## Domain Mismatches (5 records)

| contact_key | company_alias | email | contact_domain | Issue |
|-------------|---------------|-------|----------------|-------|
| CT-0010 | C-66D1FC | user0@ | 66d1fc.com | Empty email |
| CT-0011 | C-66D1FC | user1@other-domain.com | 66d1fc.com | **Wrong domain** |
| CT-0080 | C-92D97D | user0@ | 92d97d.com | Empty email |
| CT-0081 | C-92D97D | user1@ | 92d97d.com | Empty email |
| CT-0192 | C-425E2A | user2@ | 425e2a.com | Empty email |

---

## Fill Missing Company Fields from Enrichment

Only fill where CRM is empty AND enrichment has a matching row:

| company_alias | domain | field | CRM value | ZI value | Action |
|---------------|--------|-------|-----------|----------|--------|
| C-EC3025 | ec3025.com | employee_count | (empty) | 400 | Fill from ZI |
| C-96039F | 96039f.com | employee_count | (empty) | 400 | Fill from ZI |
| C-44EA29 | 44ea29.com | employee_count | (empty) | 400 | Fill from ZI |
| C-D04904 | d04904.com | employee_count | (empty) | 400 | Fill from ZI |
| C-B23205 | b23205.com | employee_count | (empty) | 400 | Fill from ZI |
| C-60C75F | 60c75f.com | employee_count | (empty) | 400 | Fill from ZI |
| C-7BBDFA | 7bbdfa.com | employee_count | (empty) | 400 | Fill from ZI |
| C-50D386 | 50d386.com | employee_count | (empty) | 400 | Fill from ZI |

---

## CRM vs Enrichment Disagreements

Where both CRM and enrichment have values but they differ:

| company_alias | domain | field | CRM | ZI | Recommendation |
|---------------|--------|-------|-----|----|----------------|
| C-66D1FC | 66d1fc.com | hq_country | US | United States | Standardize to ZI |
| C-950043 | 950043.com | hq_country | US | United States | Standardize to ZI |
| C-EC3025 | ec3025.com | hq_country | USA | United States | Standardize to ZI |
| C-96039F | 96039f.com | hq_country | USA | United States | Standardize to ZI |
| C-77A95A | 77a95a.com | hq_country | US | United States | Standardize to ZI |
| C-B23205 | b23205.com | hq_country | US | United States | Standardize to ZI |
| C-E51FB7 | e51fb7.com | hq_country | USA | United States | Standardize to ZI |
| C-D0662E | d0662e.com | hq_country | US | United States | Standardize to ZI |
| C-425E2A | 425e2a.com | hq_country | USA | United States | Standardize to ZI |
| C-2D7423 | 2d7423.com | hq_country | USA | United States | Standardize to ZI |

**Recommendation:** Standardize all country values to "United States" (ZI standard format). US/USA/USA are the same country — the disagreement is purely formatting.

---

## 10 Fixes with Most Pipeline at Stake

**CANNOT CALCULATE** — No deals.csv file was provided. Pipeline analysis requires deal amounts, owners, and stages to prioritize fixes by revenue impact.

**Workaround:** Once deals.csv is available, join deals to companies via company_alias and sum amount by:
- Companies with missing fields that can be filled from enrichment
- Duplicate company clusters that need merging
- Contacts with invalid emails that block outreach

---

## Summary of Available Fixes

| Fix Type | Count | Priority |
|----------|-------|----------|
| Fill employee_count from ZI | 8 | High |
| Standardize hq_country to "United States" | 10 | Medium |
| Fix invalid emails (empty domains) | 4 | High |
| Fix domain mismatch (CT-0011) | 1 | High |
| Merge duplicate company clusters | 2 | Medium |
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 $0.0131 · 69s · in 4,765 / out 9,366 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
I'll analyze this CSV data systematically, classifying each deal by primary category and side.

## Classification by Deal

| Deal Alias | Tag | Free-Text Reason | Primary Category | Side |
|------------|-----|------------------|-------------------|------|
| Deal-DB0AAC | Lost- Timing | paused | TIMING | BUYER |
| Deal-F7F635 | Competitor | another direction | COMPETITOR | BUYER |
| Deal-AC944F | MIA | unresponsive | NO DECISION | UNKNOWN |
| Deal-214060 | MIA | unresponsive | NO DECISION | UNKNOWN |
| Deal-91A056 | Lost- Timing | reconnect 2027 | TIMING | BUYER |
| Deal-29326C | Lost- Timing | Timing | TIMING | BUYER |
| Deal-5DB9B0 | Lost- Does not fit ICP | Spam | OTHER | UNKNOWN |
| Deal-831B7B | Lost- Timing | new year | TIMING | BUYER |
| Deal-F97C37 | Competitor | other vendor diversified | COMPETITOR | BUYER |
| Deal-13E9CF | Doing nothing | deprioritized | NO DECISION | BUYER |
| Deal-39E25C | Lost- Timing | reconnect next year | TIMING | BUYER |
| Deal-7ED004 | Lost- Budget/Price | no budget approval | PRICING | BUYER |
| Deal-21B045 | MIA | MIA | NO DECISION | UNKNOWN |
| Deal-B3ABED | Lost- Timing | Q2 next year | TIMING | BUYER |
| Deal-422BA6 | Competitor | ADP partner | COMPETITOR | BUYER |
| Deal-ED9AE7 | Lost DM | timing, budget, authority | TIMING | BUYER |
| Deal-988493 | MIA | mia | NO DECISION | UNKNOWN |
| Deal-381C8C | Competitor | not moving forward | COMPETITOR | BUYER |
| Deal-F308CA | MIA | No contact since April | NO DECISION | UNKNOWN |
| Deal-F1E8A6 | Competitor | not moving forward | COMPETITOR | BUYER |
| Deal-B6AC09 | Lost- Timing | revisiting 2027 | TIMING | BUYER |
| Deal-70F704 | Lost DM | MIA, anniversary only | NO DECISION | BUYER |
| Deal-E6E80A | Lost- Timing | pushed to 2027 | TIMING | BUYER |
| Deal-B038F0 | Lost- Timing | pushed back 2027 | TIMING | BUYER |
| Deal-4664E1 | MIA | No contact after intro | NO DECISION | UNKNOWN |
| Deal-175756 | Lost- Timing | on hold until 2027 | TIMING | BUYER |
| Deal-E74A73 | Doing nothing | test manually first | NO DECISION | BUYER |
| Deal-DDAB52 | Competitor | Rippl more features | COMPETITOR | BUYER |
| Deal-ACE061 | Competitor | went with HeyTaco | COMPETITOR | BUYER |
| Deal-BB78F3 | Lost- Timing | action items first | TIMING | BUYER |
| Deal-D48E0B | MIA | MIA | NO DECISION | UNKNOWN |
| Deal-15DA99 | Lost- Timing | early 2027 | TIMING | BUYER |
| Deal-F4AF5D | Lost- Timing | early next year | TIMING | BUYER |
| Deal-79B7A1 | Lost- Timing | Timing | TIMING | BUYER |
| Deal-583ADB | MIA | MIA | NO DECISION | UNKNOWN |
| Deal-8E27DA | Feature Request | swag only, no R&R | PRODUCT GAP | BUYER |
| Deal-2D2F8D | Competitor | different direction | COMPETITOR | BUYER |
| Deal-E0441F | MIA | stale, no contact | NO DECISION | UNKNOWN |
| Deal-7CB44D | MIA | no contact since demo | NO DECISION | UNKNOWN |
| Deal-0F96AA | Competitor | not advancing to finalist | COMPETITOR | BUYER |
| Deal-1BCA50 | Competitor | budget + other vendor | COMPETITOR | BUYER |
| Deal-7CC678 | Competitor | nothing specific | COMPETITOR | UNKNOWN |
| Deal-FAC17C | Lost DM | no approval from IT Director | CHAMPION LEFT | BUYER |
| Deal-242273 | Competitor | points currency spend | COMPETITOR | BUYER |
| Deal-50E5D8 | Doing nothing | pause for now | NO DECISION | BUYER |
| Deal-A2C349 | Competitor | stick with Awardco | COMPETITOR | BUYER |
| Deal-9F176A | Lost- Timing | pause until end year | TIMING | BUYER |
| Deal-7B2236 | Doing nothing | budget + simpler option | NO DECISION | BUYER |
| Deal-AFA56C | MIA | unresponsive | NO DECISION | UNKNOWN |
| Deal-C7156E | Competitor | selected another vendor | COMPETITOR | BUYER |
| Deal-C33D91 | Lost- Budget/Price | budget cuts | PRICING | BUYER |
| Deal-9048EB | MIA | no contact, feature gaps | PRODUCT GAP | BUYER |
| Deal-5E64CE | Doing nothing | Nectar exit fee high | PRICING | BUYER |
| Deal-8A0992 | Competitor | Canadian provider | COMPETITOR | BUYER |
| Deal-D0C698 | Competitor | past Kudos user | COMPETITOR | BUYER |
| Deal-69CF3D | Lost- Timing | On Hold | TIMING | BUYER |
| Deal-ECBF89 | Lost- Timing | On Hold for now | TIMING | BUYER |
| Deal-3618CC | Lost DM | Wanted Surveys | PRODUCT GAP | BUYER |
| Deal-EECC02 | Competitor | another direction | COMPETITOR | BUYER |
| Deal-5AD03E | Competitor | budget access | COMPETITOR | BUYER |
| Deal-D1A623 | Lost- Timing | timing | TIMING | BUYER |
| Deal-413C56 | Doing nothing | CEO not ready | NO DECISION | BUYER |
| Deal-47F1A1 | Competitor | staying with WorkTango | COMPETITOR | BUYER |
| Deal-BF2A98 | Competitor | deployed HiThrive | COMPETITOR | BUYER |
| Deal-2A292B | Doing nothing | build internally | NO DECISION | BUYER |
| Deal-D1AABF | MIA | No response | NO DECISION | UNKNOWN |
| Deal-FEDBCB | Doing nothing | reconnect end year | TIMING | BUYER |
| Deal-1E7DA9 | Competitor | selected another platform | COMPETITOR | BUYER |
| Deal-2BBA21 | MIA | No contact since intro | NO DECISION | UNKNOWN |
| Deal-286F9C | Competitor | go with another platform | COMPETITOR | BUYER |
| Deal-7FBAC6 | Doing nothing | Leadership pause | NO DECISION | BUYER |
| Deal-369281 | Competitor | went with Paylocity | COMPETITOR | BUYER |
| Deal-386F6E | MIA | No response | NO DECISION | UNKNOWN |
| Deal-9FCD0D | Competitor | Canadian company | COMPETITOR | BUYER |
| Deal-55867E | Lost- Timing | not moving forward now | TIMING | BUYER |
| Deal-DAFB82 | Lost- Budget/Price | other priorities, 2028 | PRICING | BUYER |
| Deal-2FEDDB | Doing nothing | unsure on timing | NO DECISION | BUYER |
| Deal-64B19A | Competitor | stayed with Motivosity | COMPETITOR | BUYER |
| Deal-3F86A0 | MIA | unresponsive | NO DECISION | UNKNOWN |
| Deal-096750 | MIA | no contact after intro | NO DECISION | UNKNOWN |
| Deal-F325A5 | Lost DM | Layoffs, leadership change | CHAMPION LEFT | BUYER |
| Deal-ABD14C | Doing nothing | Not interested | NO DECISION | BUYER |
| Deal-79E61A | MIA | Unresponsive | NO DECISION | UNKNOWN |
| Deal-8A119B | Lost- Budget/Price | Didn't get approval | PRICING | BUYER |
| Deal-AE7C4E | MIA | Unresponsive | NO DECISION | UNKNOWN |
| Deal-DAB4F1 | MIA | Unresponsive | NO DECISION | UNKNOWN |
| Deal-B4B50F | MIA | Unresponsive | NO DECISION | UNKNOWN |
| Deal-981AD4 | Feature Request | UI and UK focus | PRODUCT GAP | BUYER |
| Deal-DC77FE | Competitor | more customization | COMPETITOR | BUYER |
| Deal-5885B9 | MIA | MIA | NO DECISION | UNKNOWN |

## Summary

### Category Counts (90 deals total)
- **COMPETITOR**: 25 deals (28%)
- **TIMING**: 20 deals (22%)
- **NO DECISION**: 31 deals (34%)
- **PRICING**: 5 deals (6%)
- **PRODUCT GAP**: 3 deals (3%)
- **CHAMPION LEFT**: 2 deals (2%)
- **OTHER**: 1 deal (1%)

### Side Split
- **BUYER**: 59 deals (66%)
- **UNKNOWN**: 30 deals (33%)
- **BONUSLY**: 0 deals (0%)

### Tag vs. Free-Text Discrepancies (10 deals)

| Deal | Tag | Reason | Discrepancy |
|------|-----|--------|--------------|
| Deal-5DB9B0 | Does not fit ICP | "Spam" | Tag says ICP mismatch; reason says spam — OTHER |
| Deal-70F704 | Lost DM | MIA, anniversary only | Tag says champion left; reason is NO DECISION (unresponsive) |
| Deal-E0441F | MIA | stale from departed rep | Tag says no decision; actually CHAMPION LEFT (rep churn) |
| Deal-7CC678 | Competitor | nothing specific | Tag says competitor; no competitor named — UNKNOWN |
| Deal-3618CC | Lost DM | Wanted Surveys | Tag says champion left; actual issue is PRODUCT GAP |
| Deal-5E64CE | Doing nothing | Nectar exit fee | Tag says not priority; actual issue is PRICING (switching cost) |
| Deal-FEDBCB | Doing nothing | reconnect end year | Tag says not priority; actual issue is TIMING |
| Deal-2FEDDB | Doing nothing | unsure on timing | Tag says not priority; actual issue is TIMING |
| Deal-9048EB | MIA | feature gaps | Tag says no decision; actual issue is PRODUCT GAP |
| Deal-ABD14C | Doing nothing | Not interested | Could be OTHER (genuine no fit) |

**Count: 10 deals where tag disagrees with free-text**

### Two Patterns Most Worth Acting On

**1. Competitor Loss Rate (25 deals, 28%)**
The highest single category. Recurring competitor names: ADP TotalSource partner (Deal-422BA6), Rippl, HeyTaco, Awardco, WorkTango, Motivosity, Kudos, Paylocity, HiThrive. Common differentiators mentioned: "more diversified offerings," "more customization," "Canadian provider preferred," "digitize points currency," "staying with existing vendor." 
→ *Action: Develop competitive battle cards for top 5 competitors; emphasize Bonusly's differentiation on customization and integration depth.*

**2. No-Decision / MIA Rate (31 deals, 34%)**
The largest category. Deals marked MIA or unresponsive with reasons like "No contact since intro," "unresponsive," "stale." This indicates failure in follow-up cadence or early engagement.
→ *Action: Implement automated multi-touch sequences with ADR escalation triggers at 14 days of no contact; require deal health scoring at opportunity creation.*

**Honorable mention: Timing (20 deals, 22%)** — Nearly all reference 2027 reconnection. These are salvageable if CRM workflows trigger outreach at the stated reconnect date. Current system appears to lack date-based re-engagement automation.
pipeline-tieringPASS 1.00
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 $0.0903 · 301s · in 83,753 / out 50,517 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
{
  "tier_counts": {
    "LOCK": 7,
    "ACTION": 33,
    "BUILD": 107,
    "WATCH": 7,
    "RISKY": 2,
    "REVIVE": 0
  },
  "tier_examples": {
    "LOCK": ["Deal-944310", "Deal-C26D20", "Deal-D348E1"],
    "ACTION": ["Deal-62D607", "Deal-7599B8", "Deal-9AAE5F"],
    "BUILD": ["Deal-3795AD", "Deal-927338", "Deal-117863"],
    "WATCH": ["Deal-C6FE92", "Deal-DAF1D9", "Deal-46988D"],
    "RISKY": ["Deal-4A13AD", "Deal-7BBDFA"],
    "REVIVE": []
  },
  "risky_deals": ["Deal-4A13AD", "Deal-7BBDFA"],
  "lock_violations": 0,
  "pipeline_shape": "Pipeline is heavily weighted toward BUILD (107 deals, 69%) representing early-stage PIPELINE opportunities. Strong COMMIT/BEST_CASE deals cluster in LOCK (7) or ACTION (33), with only 2 deals flagged as RISKY (high forecast but stale engagement). WATCH tier (7) captures high-engagement BEST_CASE deals in mid-stages. No REVIVE deals exist, indicating no deals have completely stalled engagement."
}
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 $0.0041 · 22s · in 2,016 / out 2,543 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
[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "why_buys": [
      "The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually."
    ],
    "pain_points": [
      "HR team of three cannot keep up manually with anniversary and birthday awards",
      "Track everything in a spreadsheet, and people slip through the cracks"
    ],
    "stakeholders": ["VP People", "HR Admin"],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "Ideally we would have this live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "Security review on September 12",
    "objections": ["Need SSO and audit logs for IT to sign off"],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "We want to tie recognition to retention for our hourly workforce — regretted turnover there is over 30%."
    ],
    "pain_points": [
      "Regretted turnover over 30% for hourly workforce"
    ],
    "stakeholders": ["Head of Total Rewards", "CFO"],
    "budget_signal": "$25k pilot budget for this quarter",
    "timeline_signal": "We want a decision by end of September",
    "competitor_mentioned": null,
    "next_step": "Send pilot agreement and route to legal this week",
    "objections": ["Integration with Workday has to be rock solid — that's my one condition"],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "We need to make recognition visible across our 12 retail locations.",
      "Store managers have zero budget autonomy for on-the-spot recognition today."
    ],
    "pain_points": [
      "No visibility across 12 retail locations",
      "Store managers have zero budget autonomy for on-the-spot recognition"
    ],
    "stakeholders": ["People Ops Manager", "CEO"],
    "budget_signal": null,
    "timeline_signal": "Honestly there's no rush on our side until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "Schedule a call with CEO — prospect will send two times",
    "objections": [],
    "confidence": "moderate"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": [
      "We want to consolidate three separate recognition tools into one.",
      "We're paying for three tools and none of them talk to our HRIS."
    ],
    "pain_points": [
      "Using three separate recognition tools",
      "Current tools don't integrate with HRIS"
    ],
    "stakeholders": ["VP People", "IT Security Lead"],
    "budget_signal": "If it's under $15k annually, I can approve it without going to the board",
    "timeline_signal": "Our procurement cycle runs six to eight weeks minimum",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "The security review took three months for our last vendor — that's my hesitation"
    ],
    "confidence": "low"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "Two things: automate service milestones, and give us analytics on recognition equity across departments.",
      "Our night-shift teams feel invisible — their engagement scores run 20 points lower."
    ],
    "pain_points": [
      "Need to automate service milestones",
      "Need analytics on recognition equity across departments",
      "Night-shift teams feel invisible — engagement scores 20 points lower",
      "Exec team is skeptical after a failed rollout two years ago"
    ],
    "stakeholders": ["HR Director", "People Ops Coordinator", "Exec Team"],
    "budget_signal": "$12k approved under our engagement line",
    "timeline_signal": "Need this running before our January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "Present to exec team on October 2",
    "objections": [
      "Exec team is skeptical after a failed rollout two years ago"
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "I'd love to cut the admin time on service awards.",
      "I personally spend five hours a month ordering and shipping plaques."
    ],
    "pain_points": [
      "Spends five hours a month ordering and shipping plaques manually",
      "COO usually prefers building things in-house"
    ],
    "stakeholders": ["HR Manager", "COO"],
    "budget_signal": null,
    "timeline_signal": "Fiscal year resets in February, so a Q1 start is realistic",
    "competitor_mentioned": null,
    "next_step": "Send one-page overview and forward to COO this week",
    "objections": [
      "COO usually prefers building things in-house"
    ],
    "confidence": "moderate"
  }
]
```
next-to-close0.75
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 $0.0018 · 10s · in 1,346 / out 817 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
**Top 3 Deals Closest to Signature**

**1. Deal-547B2B — $11,200**
- **Why close:** Slack explicitly states "signing page is out" and "VP People told me they are signing tomorrow." This is signature-imminent.
- **What left:** Just need the signature. Clean redlines already returned.

**2. Deal-403845 — $9,000**
- **Why close:** Order form is with their finance team (Slack, Dana Mercer). DS5/COMMIT in CRM with close date 2026-09-11.
- **What left:** Awaiting finance approval on the order form.

**3. Deal-A2B47C — $6,360**
- **Why close:** DS5/COMMIT, close date 2026-09-11. Slack notes "warm, just normal legal-review pace" — no blockers mentioned.
- **What left:** Legal review wrapping up, typical pace but no delays flagged.

*Note: Deal-2465CE ($5,400) shows DS5/COMMIT in CRM with 2026-09-10 close, but Slack confirms the champion left and procurement froze the deal — it's now blocked, not close.*
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 $0.0020 · 14s · in 935 / out 1,055 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
## Analysis of Prospect-Raised Product Gaps

| Transcript | Prospect Quote | Classification | Deal | Amount |
|---|---|---|---|---|
| TG-001 | "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." | **REAL GAP** — SCIM user provisioning not listed as supported in product docs | Deal-EC3025 | *[not provided]* |
| TG-002 | "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us." | **REAL GAP** — ADP Workforce Now integration not listed as supported in product docs | Deal-D0D6B5 | *[not provided]* |
| TG-003 | "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?" | **Plan Gate** — Custom report builder exists but requires Enterprise tier (docs: "Custom report builder — yes" for Enterprise only) | Deal-CFE7F4 | *[not provided]* |
| TG-004 | "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." | **Rollout/Enablement Issue** — Slack integration is available on all plans; issue is training, not product capability | Deal-84DBA6 | *[not provided]* |
| TG-005 | (Prospect response to Alex's disclosure about no mobile app) "Good to know. The web version should be fine for our office staff for now." | **Not a gap** — Prospect accepted limitation; also, the gap came from Alex, not prospect | Deal-36C33F | *[not provided]* |

---

## Summary: Real Product Gaps

1. **SCIM User Provisioning** — cited by Deal-EC3025 prospect
   - Prospect quote: *"We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."*

2. **ADP Workforce Now Integration** — cited by Deal-D0D6B5 prospect
   - 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."*

---

**Note:** Deal amounts were not provided in the source data.
rep-performance 5 tests
stale-pipeline-by-repPASS 1.00
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 $0.0481 · 124s · in 56,409 / out 13,068 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
The analysis is complete. Here's the final report:

---

**STALE DEALS REPORT**  
*Snapshot: 2026-09-05 | Cutoff: 2026-08-29 (7 days ago)*  

A deal is stale if it has no logged email, call, or meeting since 2026-08-29.  
"999 days" = no engagement record ever (no email/call/meeting in 90-day window).

---

### Bryce Harmon
**13 stale deals | $626,243.00 total**

| Deal Alias | Stage | Amount | Days Since Contact |
|------------|-------|--------|-------------------|
| Deal-2D1F1B | DS1 | $240,000.00 | 81 |
| Deal-66D1FC | DS1 | $99,000.00 | 16 |
| Deal-950043 | DS1 | $70,000.00 | 19 |
| Deal-B23205 | DS1 | $45,000.00 | 16 |
| Deal-7BBDFA | DS3 | $37,440.00 | 46 |
| Deal-332637 | DS2 | $36,000.00 | 9 |
| Deal-1BEEBF | DS1 | $31,500.00 | 19 |
| Deal-C5658B | DS1 | $23,400.00 | 16 |
| Deal-40522D | DS3 | $21,000.00 | 19 |
| Deal-F0EBBB | DS3 | $11,400.00 | 24 |
| Deal-E25A09 | DS1 | $6,000.00 | 9 |
| Deal-C9C286 | DS2 | $5,502.00 | 9 |
| Deal-012CB1 | DS1 | $1.00 | 23 |

---

### Dana Mercer
**14 stale deals | $261,645.00 total**

| Deal Alias | Stage | Amount | Days Since Contact |
|------------|-------|--------|-------------------|
| Deal-44EA29 | DS2 | $60,000.00 | 10 |
| Deal-E51FB7 | DS2 | $43,875.00 | 12 |
| Deal-B42F46 | DS1 | $27,000.00 | 19 |
| Deal-BA3DDC | DS3 | $23,400.00 | 15 |
| Deal-9DDE86 | DS2 | $20,000.00 | 15 |
| Deal-215CCA | DS3 | $18,900.00 | 17 |
| Deal-5EED42 | DS3 | $16,250.00 | 11 |
| Deal-57887A | DS2 | $15,000.00 | 8 |
| Deal-B7EBD1 | DS5 | $9,000.00 | 16 |
| Deal-3974EB | DS4 | $9,000.00 | 8 |
| Deal-F40F04 | DS2 | $8,100.00 | 15 |
| Deal-87DDD1 | DS1 | $5,000.00 | 19 |
| Deal-F336B6 | DS3 | $4,200.00 | 15 |
| Deal-0660B4 | DS4 | $1,920.00 | 16 |

---

### Alex Franklin
**19 stale deals | $109,536.00 total**

| Deal Alias | Stage | Amount | Days Since Contact |
|------------|-------|--------|-------------------|
| Deal-CC08D1 | DS1 | $24,000.00 | 16 |
| Deal-E73427 | DS3 | $18,000.00 | 10 |
| Deal-885F45 | DS2 | $9,300.00 | 12 |
| Deal-C2FF3C | DS1 | $8,316.00 | 10 |
| Deal-3EED2C | DS2 | $7,200.00 | 999 (no contact) |
| Deal-0D2F7A | DS3 | $5,100.00 | 12 |
| Deal-6C60D4 | DS3 | $4,800.00 | 12 |
| Deal-13FEBD | DS2 | $4,680.00 | 12 |
| Deal-9D0060 | DS3 | $3,840.00 | 12 |
| Deal-690476 | DS2 | $3,600.00 | 18 |
| Deal-C6D97A | DS4 | $3,240.00 | 8 |
| Deal-EE195F | DS3 | $3,120.00 | 8 |
| Deal-278DEC | DS3 | $2,700.00 | 8 |
| Deal-635B8E | DS3 | $2,600.00 | 18 |
| Deal-6883F3 | DS1 | $2,400.00 | 16 |
| Deal-4A13AD | DS3 | $2,160.00 | 26 |
| Deal-F67D31 | DS2 | $1,800.00 | 8 |
| Deal-5FDCE4 | DS3 | $1,600.00 | 12 |
| Deal-BA571A | DS4 | $1,080.00 | 18 |

---

### Cole Ingram
**18 stale deals | $252,905.03 total**

| Deal Alias | Stage | Amount | Days Since Contact |
|------------|-------|--------|-------------------|
| Deal-D04904 | DS2 | $58,529.25 | 11 |
| Deal-B25F40 | DS3 | $40,000.00 | 8 |
| Deal-813836 | DS2 | $32,175.00 | 11 |
| Deal-1BA595 | DS2 | $31,750.00 | 11 |
| Deal-CFE1E8 | DS3 | $18,000.00 | 11 |
| Deal-CD47A6 | DS2 | $12,168.00 | 11 |
| Deal-627646 | DS3 | $11,193.00 | 11 |
| Deal-FF809F | DS2 | $7,781.20 | 11 |
| Deal-AF932D | DS2 | $7,225.40 | 11 |
| Deal-A71728 | DS2 | $6,947.50 | 11 |
| Deal-8BC9F5 | DS2 | $5,616.00 | 10 |
| Deal-175395 | DS3 | $4,779.88 | 11 |
| Deal-481E24 | DS3 | $4,140.00 | 10 |
| Deal-C7F9BF | DS2 | $3,360.00 | 11 |
| Deal-2F3A66 | DS3 | $3,334.80 | 11 |
| Deal-342E96 | DS2 | $2,700.00 | 24 |
| Deal-E568D5 | DS3 | $1,875.00 | 11 |
| Deal-FD9F4E | DS5 | $1,330.00 | 10 |

---

### Elena Sinclair
**1 stale deal | $2,100.00 total**

| Deal Alias | Stage | Amount | Days Since Contact |
|------------|-------|--------|-------------------|
| Deal-57FF13 | DS1 | $2,100.00 | 999 (no contact) |

---

**SUMMARY BY OWNER**

| Owner | Stale Deals | Total Stale Amount |
|-------|------------|-------------------|
| Bryce Harmon | 13 | $626,243.00 |
| Dana Mercer | 14 | $261,645.00 |
| Alex Franklin | 19 | $109,536.00 |
| Cole Ingram | 18 | $252,905.03 |
| Elena Sinclair | 1 | $2,100.00 |
| **TOTAL** | **65** | **$1,252,429.03** |
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 $0.0491 · 178s · in 52,714 / out 25,643 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
**SALES REP ACTIVITY ANALYSIS (Last 30 Days vs DS2 Entries)**
Snapshot: 2026-09-05 | Lookback: 2026-08-06 to 2026-09-05

---

**Rank 1: Dana Mercer**
- DS2 entries (last 30d): 1
- Activities: 5 total (3 emails, 0 calls, 2 meetings)
- Activity mix: 60.0% email / 0.0% call / 40.0% meeting
- Activities per DS2 entry: **5.0**

**Rank 2: Alex Franklin**
- DS2 entries (last 30d): 17
- Activities: 102 total (80 emails, 0 calls, 22 meetings)
- Activity mix: 78.4% email / 0.0% call / 21.6% meeting
- Activities per DS2 entry: **6.0**

**Rank 3: Cole Ingram**
- DS2 entries (last 30d): 2
- Activities: 15 total (14 emails, 0 calls, 1 meeting)
- Activity mix: 93.3% email / 0.0% call / 6.7% meeting
- Activities per DS2 entry: **7.5**

**Rank 4: Bryce Harmon**
- DS2 entries (last 30d): 4
- Activities: 49 total (40 emails, 0 calls, 9 meetings)
- Activity mix: 81.6% email / 0.0% call / 18.4% meeting
- Activities per DS2 entry: **12.2**

**Rank 5: Farid Osman**
- DS2 entries (last 30d): 1
- Activities: 21 total (17 emails, 0 calls, 4 meetings)
- Activity mix: 81.0% email / 0.0% call / 19.0% meeting
- Activities per DS2 entry: **21.0**

---

**SUMMARY**

- **Most efficient** (lowest activities per DS2 entry): **Dana Mercer** (5.0 activities per DS2 entry)
- **Highest volume** (total activities): **Alex Franklin** (102 activities)
- **Same person?** No

*Note: Elena Sinclair (owner_id 701163055) had no deals entering DS2 in the last 30 days and is excluded from the ranking.*
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 $0.0058 · 25s · in 8,785 / out 2,297 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
I'll analyze the data to build the QTD scorecard for Alex Franklin as of 2026-09-05.

**1. BOOKINGS VS QUOTA**

Q3 2026 runs July 1 - September 30. QTD through Sept 5 includes closed-won deals dated July 1 or later.

Closed-won deals in QTD:
- Deal-A1C3E5: $40,000 (2026-07-15)
- Deal-F2C7D8: $20,000 (2026-07-24)
- Deal-B7D2F4: $35,000 (2026-07-31)
- Deal-C9E1A6: $21,000 (2026-08-12)
- Deal-A8B4D6: $12,000 (2026-08-19)
- Deal-D4B8C2: $11,000 (2026-08-21)
- Deal-E6F3A9: $6,500 (2026-09-02)
- Deal-C5D9E2: $4,500 (2026-09-03)

Note: Deal-B3E6F1 ($24,000, close_date=2026-06-20) is EXCLUDED - dated before Q3.

**Total QTD Bookings: $149,500**
**Quota: $200,000**
**Attainment: 149,500 / 200,000 = 74.75%**

---

**2. NEW VS EXPANSION SPLIT**

| Type | Deals | Amount |
|------|-------|--------|
| New | 6 | $127,500 |
| Expansion | 2 | $22,000 |

Expansion deals: Deal-F2C7D8 ($20,000), Deal-A8B4D6 ($12,000)

---

**3. ACTIVE PIPELINE BY STAGE (Open deals only)**

| Stage | Count | Total Amount |
|-------|-------|--------------|
| DS1 | 11 | $159,521 |
| DS2 | 13 | $136,940 |
| DS3 | 43 | $346,004 |
| DS4 | 3 | $6,804 |
| DS5 | 4 | $27,770 |

**Total Open Pipeline: 74 deals, $677,039**

---

**4. ROLLING 90-DAY DS2-TO-WON RATE**

90-day window: 2026-06-07 through 2026-09-05

Deals that entered DS2 in window:
| Deal | entered_ds2 | is_won |
|------|-------------|--------|
| Deal-5AD03E | 2026-07-12 | false |
| Deal-69CF3D | 2026-07-05 | false |
| Deal-ECBF89 | 2026-06-27 | false |
| Deal-15DA99 | 2026-07-15 | false |
| Deal-583ADB | 2026-07-16 | false |
| Deal-79B7A1 | 2026-07-29 | false |
| Deal-8E27DA | 2026-07-19 | false |
| Deal-D48E0B | 2026-07-26 | false |
| Deal-F4AF5D | 2026-07-27 | false |
| Deal-F2C7D8 | 2026-06-29 | true |
| Deal-A8B4D6 | 2026-07-09 | true |
| Deal-E6F3A9 | 2026-08-05 | true |

**DS2-to-Won Rate: 3 wins / 12 deals = 25.0%**

---

**5. WIN AND LOSS COUNTS WITH TOP LOSS REASON**

**Wins (QTD):** 8 deals, $149,500

**Losses (QTD):** 26 deals, $247,031 total

Loss reasons:
| Reason | Count |
|--------|-------|
| Lost- Timing (1 year or more) | 10 |
| Competitor | 6 |
| MIA | 5 |
| Lost DM | 2 |
| Feature Request | 1 |
| Lost- Does not fit ICP | 1 |
| *(empty)* | 1 |

**Top Loss Reason: Lost- Timing (1 year or more)** - 10 deals (38.5% of losses)

---

**6. ACTIVITY VOLUME (LAST 30 DAYS)**

Sum of ae_engagements across all deals:
| Activity Type | Total |
|--------------|-------|
| Emails | 5,314 |
| Calls | 127 |
| Meetings | 98 |
| Notes | 118 |

---

**7. COACHING OBSERVATIONS**

1. **Win rate from DS2 is critically low at 25%.** Over the past 90 days, 9 of 12 deals that reached DS2 are still open or lost - only 3 converted. Focus on DS2->DS3 progression: Are deals getting sufficient qualification before advancing? Is there a qualification gap at DS2?

2. **Timing is the #1 loss reason (38.5% of losses).** Ten deals lost to "1 year or more" timing. This signals weak ICP fit or premature prospecting. Review lead scoring criteria and ensure deals meet true readiness thresholds before entering active sales stages.

3. **Pipeline coverage is thin at 3.4x quota ($677K / $200K).** With QTD at 75% attainment and only 3.4x remaining quota in pipeline, Alex needs ~$50K more bookings in QTD. At the current 25% DS2 conversion rate, the existing pipeline won't close the gap. Recommend: (a) accelerate high-value DS3 deals, (b) add net-new pipeline immediately, or (c) request quota adjustment.
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 $0.0089 · 39s · in 7,121 / out 4,844 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 Complete

**Note:** The provided data does NOT include deal amounts or stages — these fields are not available in deal_contacts.csv.

### Summary

- **Total deals analyzed:** 14
- **Flagged deals:** 11 at-risk
- **Properly threaded (3+ active contacts, diverse personas):** 3 deals

---

### At-Risk Deals (flagged for threading issues)

| Deal Alias | Company | Flag | Active Contacts | Personas Present | Unengaged on File |
|------------|---------|------|-----------------|------------------|-------------------|
| Deal-EC3025 | C-FDD0C7 | SINGLE-THREADED (1) | 1 | champion | ✓ Chief People Officer (CT-6827DB) |
| Deal-92D97D | C-E23238 | SINGLE-THREADED (1) | 1 | HR admin | None |
| Deal-36C33F | C-077A0E | SINGLE-THREADED (1) | 1 | IT security | ✓ Chief People Officer (CT-1DB73E) |
| Deal-FCBE5B | C-737030 | SINGLE-THREADED (1) | 1 | champion | None |
| Deal-F9A08A | C-0D15DF | SINGLE-THREADED (1) | 1 | champion | ✓ Chief People Officer (CT-697541) |
| Deal-50D386 | C-EB10E4 | UNDER-THREADED (2) | 2 | champion, HR admin | ✓ Chief People Officer (CT-A1C4B3) |
| Deal-5BFE3B | C-535D36 | UNDER-THREADED (all same) | 2 | champion (both) | None |
| Deal-885F45 | C-5E8EFB | UNDER-THREADED (2) | 2 | champion, economic buyer | ✓ IT Security Lead (CT-B3F25D) |
| Deal-5408B0 | C-2AE3AA | UNDER-THREADED (2) | 2 | champion, HR admin | ✓ Chief People Officer (CT-07FA76) |
| Deal-D0D6B5 | C-32918E | UNDER-THREADED (all same) | 3 | champion (all 3) | ✓ Chief People Officer (CT-1FA4DB) |
| Deal-C6D97A | C-5A8FC2 | UNDER-THREADED (all same) | 3 | champion (all 3) | None |

---

### Recommended Actions by Deal

**Deal-EC3025** — Add economic buyer (CT-6827DB available)  
**Deal-92D97D** — Add economic buyer (none on file)  
**Deal-36C33F** — Add economic buyer (CT-1DB73E available)  
**Deal-FCBE5B** — Add economic buyer (none on file)  
**Deal-F9A08A** — Add economic buyer (CT-697541 available)  
**Deal-50D386** — Add economic buyer (CT-A1C4B3 available)  
**Deal-5BFE3B** — Add economic buyer or finance (none on file)  
**Deal-885F45** — Add finance or IT security (IT security available)  
**Deal-5408B0** — Add economic buyer (CT-07FA76 available)  
**Deal-D0D6B5** — Add economic buyer (CT-1FA4DB available)  
**Deal-C6D97A** — Add economic buyer or finance (none on file)

---

### Properly Threaded Deals (NOT at risk)

| Deal Alias | Company | Active Contacts | Personas |
|------------|---------|-----------------|----------|
| Deal-84DBA6 | C-BE9BE0 | 3 | champion, economic buyer, IT security |
| Deal-4B0BEB | C-1BDFE0 | 4 | champion, economic buyer, HR admin, finance |
| Deal-D348E1 | C-804F29 | 5 | champion, economic buyer, IT security, finance, HR admin |

These 3 deals meet the threshold of 3+ active contacts with diverse personas.
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 $0.0029 · 17s · in 2,821 / out 1,347 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
## Call Analysis: Alex Franklin (10 calls)

### Lead (first 5 minutes)

| Call | Lead |
|------|------|
| 8 calls | "Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards..." |
| TT-004 | "I put together a short agenda — security review first, then pricing." |
| TT-009 | "You asked for straight pricing last time, so let's start there." |

**Finding:** 8/10 calls open with the same retailer case study. Two calls deviate (agenda-driven, pricing-driven).

---

### Three Most Common Objections & Responses

**Objection 1 — Budget locked until next fiscal year** (4 calls: TT-001, TT-003, TT-006, TT-010)
> "Honestly, budget is locked until next fiscal year — I can't add a new line item right now."

**Response:**
> "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."

---

**Objection 2 — Revisit next quarter / open enrollment** (3 calls: TT-002, TT-005, TT-008)
> "This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater."

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

---

**Objection 3 — Already using spreadsheets/gift cards** (3 calls: TT-004, TT-007, TT-009)
> "We already do recognition with a spreadsheet and quarterly gift cards — why would we change?"

**Response:**
> "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."

---

### Next Step Agreement Rate

| Call | Next Step Locked? |
|------|-------------------|
| TT-001 | Yes |
| TT-002 | Yes |
| TT-003 | Yes |
| TT-004 | No (Alex withdrew) |
| TT-005 | Yes |
| TT-006 | Yes |
| TT-007 | No ("I need to think about it") |
| TT-008 | Yes |
| TT-009 | Yes |
| TT-010 | No ("I can't commit to anything today") |

**Rate: 7/10 = 70%**

---

### Competitors Raised by Prospects

| Deal | Competitor |
|------|------------|
| TT-003 (Deal-547B2B) | Awardco |
| TT-007 (Deal-EDC141) | Kudos |

---

### Two Coaching Notes

1. **Objection handling is consistent but reactive.** Alex has solid pivots (turnover savings, pilot scope, analytics value), yet he never pushes back on the "budget locked" objection with a direct question — e.g., "What's one thing that would make this worth exploring before fiscal year?" This could surface buying signals or real constraints.

2. **Lead variety is low.** 80% of calls open with identical case study language. TT-009 proves he can adapt ("You asked for straight pricing"), yet defaults to the retailer story. Testing two or three opening angles based on prospect signals (prior conversation, company size, industry) would likely increase engagement.
reporting-analytics 5 tests
q3-weighted-forecastPASS 1.00
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 $0.0163 · 59s · in 22,431 / out 7,532 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
# Q3 2026 Forecast Report

## Forecast Summary

| Metric | Value | Deal Count |
|--------|-------|-------------|
| **COMMIT Total** | $44,729.00 | 7 |
| **BEST_CASE Total** | $203,565.00 | 24 |
| **Weighted Forecast** | **$115,976.75** | — |

## Weighted Forecast Arithmetic

```
COMMIT:    $44,729.00 × 100% = $44,729.00
BEST_CASE: $203,565.00 × 35% = $71,247.75
                              ─────────────
                              $115,976.75
```

## Deals by Category Inside Q3 2026

**COMMIT (7 deals):**
- 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

**PIPELINE (23 deals):** Weighted at 0% = $0.00

## Top 5 BEST_CASE Deals by Amount

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

## Excluded Deals (Outside Q3)

- **Count:** 32 deals
- **Total Amount:** $227,575

*(These have close dates in October 2026, outside Q3 which ends 2026-09-30)*

---

## Data Quality

Four data quality problems in this extract would make an unattended forecast unreliable: (1) 82 of 90 deals have empty `owner` fields (only Deal-C9C286 has "Bryce Harmon"), preventing territory or rep-level analysis; (2) all `why_buys_chars` values are 0 despite many deals having non-zero character counts in the source, suggesting buyer motivation data was not exported; (3) one amount (Deal-42326B) is a decimal float ($2,480.40) mixed with integers, indicating inconsistent data types; (4) there is no `created_date` or `last_modified_date` column, so deal freshness or staleness cannot be assessed for pipeline health.
aha-moment-2x20.83
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 $0.0213 · 79s · in 20,636 / out 11,566 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
## Activation Hypothesis Test Results

### 2x2 Cohort Matrix

| Category | Cohort Size | Retained @ 24mo | Retention Rate |
|----------|-------------|------------------|----------------|
| **Both Signals** (m1_users >= 5 AND m1_redemptions >= 1) | 47 | 31 | **66.0%** |
| **Givers-Only** (m1_users >= 5, m1_redemptions = 0) | 49 | 23 | **46.9%** |
| **Redemption-Only** (m1_users < 5, m1_redemptions >= 1) | 29 | 9 | **31.0%** |
| **Neither Signal** (m1_users < 5, m1_redemptions = 0) | 95 | 38 | **40.0%** |
| **TOTAL** | 220 | 101 | 45.9% |

### Exclusions from Denominator
**3 companies excluded** with `current_status = 'non_renewing'`:

| Company | signup_month | m1_users | m1_redemptions | Why excluded |
|---------|--------------|----------|----------------|--------------|
| C-0B2078FB | 2023-01 | 1 | 0 | status = 'non_renewing' |
| C-0A96134F | 2023-01 | 3 | 3 | status = 'non_renewing' |
| C-0BEAF685 | 2023-06 | 3 | 2 | status = 'non_renewing' |

These are excluded because "retained at 24 months" = `current_status = 'active'` only. Non-renewing is a third state that doesn't map to the binary retained/churned framework.

### Single Signal with Largest Retention Lift

**Baseline** (neither signal): 40.0%

| Signal | Absolute Lift vs Baseline |
|--------|---------------------------|
| Givers-only | **+6.9%** |
| Redemptions-only | **-9.0%** |

**Givers is the single signal with the largest retention lift** (+6.9 percentage points).

### What This Does and Does Not Prove

**Does prove:**
- A strong correlation exists between having both activation signals (5+ users + 1+ redemptions in month 1) and 24-month retention (66% vs 40% baseline = +26pp)
- Companies with the givers signal alone perform better than baseline (47% vs 40%)
- The redemption-only cohort actually underperforms baseline (31% vs 40%)

**Does NOT prove:**
- **Causation** — other confounding factors (company size, industry, product tier, geographic region) may drive both the activation signals AND retention
- That triggering these behaviors in a non-activating company would improve retention — this is observational data only
- That the relationship holds for cohorts younger than 25 months (all companies in this extract are 25+ months old)
arr-rollforward-reconciliation0.80
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 $0.0137 · 68s · in 8,426 / out 7,500 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 — As of 2026-09-05

### TOTALS

| Source | ARR |
|--------|-----|
| **Billing (Active Subscriptions)** | $604,739.28 |
| **HubSpot (CRM)** | $603,581.76 |
| **Variance** | **$1,157.52** |

---

### VARIANCE DECOMPOSITION

| Bucket | Amount | Notes |
|--------|--------|-------|
| Status Mismatch | $0.00 | None found |
| Rounding (<$10) | $0.00 | None found |
| Missing in Billing (HubSpot has, no active sub) | -$29,655.72 | 3 accounts with cancelled/inactive subs in HubSpot |
| Missing in HubSpot (Billing has, not in HubSpot) | $28,449.24 | 1 account in billing but not in HubSpot |
| Other (diff >= $10) | $2,364.00 | 3 accounts with data discrepancies |
| **TOTAL** | **$1,157.52** | ✓ Matches variance |

---

### MISMATCHED ACCOUNTS

**Missing in HubSpot (in Billing only):**
| Company Alias | Billing ARR | Suggested Owner |
|---------------|-------------|-----------------|
| C-21629AA4 | $28,449.24 | Sales Ops |

**Missing in Billing (in HubSpot, no active subscription):**
| Company Alias | HubSpot ARR | Suggested Owner |
|---------------|-------------|-----------------|
| C-0C8323BF | $4,905.24 | Account Mgmt |
| C-0D5BBE3A | $16,497.24 | Sales Ops |
| C-0DC4FB8C | $8,253.24 | Account Mgmt |

**Other Significant Discrepancies (≥$10):**
| Company Alias | Billing ARR | HubSpot ARR | Diff | Suggested Owner |
|---------------|-------------|-------------|------|-----------------|
| C-0D66DF9E | $23,184.00 | $23,200.00 | -$16.00 | Revenue Ops |
| C-0F7269D7 | $26,796.00 | $24,396.00 | +$2,400.00 | Sales Ops |
| C-14D70CE0 | $18,180.00 | $18,200.00 | -$20.00 | Revenue Ops |

---

### BUSINESS RULE VIOLATIONS

**Rule:** Any subscription with term ≠ 12 months must have `cf_agreement_end_date` populated.

| Status |
|--------|
| ✓ No violations — all non-12-month terms have end dates populated |

Non-12-month subscriptions verified:
- SUB-0002 (C-1794A52C): 24 months — end date: 2027-11-30
- SUB-000C (C-0DB48281): 24 months — end date: 2027-11-30
- SUB-0019 (C-22170CA1): 36 months — end date: 2027-11-30
- SUB-001A (C-0FC4DBB8): 36 months — end date: 2027-11-30
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 $0.0074 · 31s · in 6,257 / out 3,744 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
**KVM Report: 2026-08 vs 2026-07**

All four metrics show minimal movement except pulse engagement, which dropped sharply.

| KVM | Jul-26 | Aug-26 | Δ Absolute | Δ Relative | Direction |
|-----|--------|--------|------------|------------|-----------|
| Giving Rate | 0.6023 | 0.6027 | +0.0004 | +0.07% | ↑ |
| Redemptions per User | 1.7300 | 1.7302 | +0.0002 | +0.01% | ↑ |
| 1:1 Engagement | 0.4469 | 0.4472 | +0.0003 | +0.06% | ↑ |
| Pulse Engagement | 0.6006 | 0.5086 | -0.0920 | **-15.31%** | ↓ |

**Largest Relative Move:** Pulse engagement (-15.31% decline). The data shows this is driven by the **enterprise** segment, where pulse engagement fell from 0.5500 to 0.2743 (a -50.13% drop). SMB and mid-market showed negligible change (+/- 0.2%). All enterprise accounts (10 companies) contributed to this drop, with August values clustered around 0.27-0.29.
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 $0.0990 · 316s · in 83,327 / out 48,775 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
Last completed month: **August 2026**

---

**REDEMPTION METRICS (through 2026-08)**
- Redemption count: **48**
- Total spend: **$3,536.00**
- Unique redeemers: **46**
- Redemptions per redeemer: **1.04** (48 ÷ 46)

---

**PROVIDER MIX (% of spend)**
| Provider   | % of Spend |
|------------|------------|
| Tremendous | 32.4%      |
| custom     | 29.9%      |
| Snappy     | 23.6%      |
| TangoCard  | 14.1%      |
| **TOTAL**  | **100.0%** |

---

**TOP 5 COUNTRIES BY REDEMPTIONS**
| Country | Redemptions |
|---------|-------------|
| US      | 31          |
| CA      | 7           |
| GB      | 2           |
| SG      | 2           |
| AU      | 2           |

---

**Arithmetic check:**
- 48 redemptions ÷ 46 unique users = 1.04 redemptions per user
- $1,145 + $1,057 + $834 + $500 = $3,536 total
- 32.4% + 29.9% + 23.6% + 14.1% = 100.0%
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 $0.0032 · 20s · in 1,452 / out 1,921 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

### Eligibility Rules Applied
- **R1**: Health score < 60 (at-risk)
- **R2**: Churn-save eligible amount > $0
- **R3**: Renewal within 120 days of 2026-09-05 → renewal date ≤ 2027-01-03

### Qualifying Accounts (8 accounts)

| Account | Health | Eligible Amount | Renewal Date | Days to Renewal | Play | Signal |
|---------|--------|-----------------|--------------|------------------|------|--------|
| C-0F6C0F34 | 51 | $49,707 | 2026-10-03 | 58 | Executive touch | champion_active=false |
| C-0B827671 | 56 | $25,365 | 2026-11-14 | 70 | Usage revival | usage_trend_3m=declining |
| C-0B360C78 | 57 | $35,748 | 2026-10-28 | 53 | Commercial concession | champion_active=true |
| C-0B0F1BAB | 38 | $5,494 | 2026-09-23 | 18 | Executive touch | champion_active=false |
| C-0CA21961 | 58 | $16,829 | 2026-12-28 | 114 | Commercial concession | champion_active=true |
| C-0E9C27D1 | 39 | $41,235 | 2026-09-24 | 19 | Commercial concession | champion_active=true |
| C-0CEF69FD | 53 | $32,621 | 2026-11-21 | 77 | Executive touch | champion_active=false |
| C-0D3278C7 | 54 | $17,602 | 2026-11-12 | 68 | Usage revival | usage_trend_3m=declining |

**Total at stake: $224,601**

### At-Risk But Not Eligible (7 accounts)

| Account | Health | Eligible Amount | Renewal Date | Why Ineligible |
|---------|--------|-----------------|--------------|----------------|
| C-0BC71BDD | 55 | $0 | 2026-10-27 | R2 failed: eligible amount = $0 |
| C-0F6694C3 | 43 | $0 | 2027-03-21 | R2 failed: eligible amount = $0 |
| C-0BE96399 | 54 | $0 | 2026-10-29 | R2 failed: eligible amount = $0 |
| C-0F876796 | 47 | $19,958 | 2027-02-06 | R3 failed: 153 days out (exceeds 120) |
| C-0FCCD2DF | 43 | $0 | 2027-04-23 | R2 failed: eligible amount = $0 |
| C-10A56B0F | 54 | $0 | 2026-12-12 | R2 failed: eligible amount = $0 |
| C-0BA71F12 | 52 | $6,824 | 2027-04-11 | R3 failed: 217 days out (exceeds 120) |

Note: C-0F876796 and C-0BA71F12 both have eligible amounts > 0 and health < 60, but their renewals fall outside the 120-day window.
expansion-kit0.83
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 $0.0018 · 14s · in 616 / out 987 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: **37.5%** (150 ÷ 400)

### Usage Health (2 lines)
1. **Usage growth trajectory:** Users climbed from 88 (Mar 2026) to 126 (Aug 2026) — a 43% increase over 6 months, showing consistent adoption momentum.
2. **Utilization rate:** 126 active users against 150 licensed seats = **84% utilization** — nearing license capacity.

### Headroom
- **Seats:** 150 licensed − 126 active = **24 unused seats**
- **Per-seat rate:** $9,000 ARR ÷ 150 seats = **$60/seat**
- **ARR headroom:** 24 seats × $60 = **$1,440 potential ARR expansion** at current rate

### Who Replied & Buying Authority
- **Replier:** Maria S., People Operations Coordinator
- **Can buy:** No — she explicitly states: *"I'm not the right person for purchasing decisions... Budget and seat expansion sit with Dana R."*

### Right Buyer
- **Dana R.**, VP People (last engaged 2026-05-18)

---

### Reply Email (138 words)

> Hi Maria,
>
> Thanks so much for the thoughtful response — it's great to hear the team genuinely loves Bonusly, and that growth in monthly active users is visible on your end. That 43% usage increase since March speaks for itself.
>
> I'd love to connect with Dana R. on seat expansion. With 126 users now active against 150 licensed seats, we're getting close to capacity, and I'd hate to hit a wall mid-momentum.
>
> Would you be able to make that intro? Happy to come with a quick usage summary and expansion proposal tailored to where your team is headed.
>
> Thanks again for being the bridge — really appreciate it.
>
> Best,
> Cole

---

**Summary:** The account has 24 seats and ~$1.4K ARR of headroom at current pricing. Usage is healthy and growing. Maria is an advocate but not the buyer — the path to revenue is through Dana R. with an intro facilitated by Maria.
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 $0.0014 · 11s · in 578 / out 642 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 Call Prep: C-0D284E42

### What's Complete

| Milestone | Date | Status |
|-----------|------|--------|
| Integration — Slack | 2026-08-12 | ✓ Complete (integration_slack field) |
| Allowance Set | 2026-08-13 | ✓ Complete (allowance_set field) |
| Admins Added | — | ✓ Complete (admins_added = 2) |
| First Recognition Given | 2026-08-15 14:22 | ✓ Complete (first_recognition_at field) |
| Integration — HRIS | — | ✗ Not Complete (empty field) |
| First Redemption | — | ✗ Not Complete (empty field) |

---

### Early Engagement Signals

**Active givers trend (2026-08-11 → 2026-09-04):**
- Started: 3 active givers
- Current: 15 active givers
- Growth: +400% over ~3 weeks
- Pattern: Consistent upward trend with minor fluctuations; hit double-digits on 2026-08-21 and has held there

**Recognition activity:**
- First recognition given within 4 days of signup (2026-08-15)
- Recognition cadence appears active given the growing giver base

**Signal:** Strong early adoption. Growing engaged user base without yet having completed HRIS integration or any redemptions.

---

### Three Things to Cover on the Call

1. **HRIS Integration** — Not yet connected. Ask: What's blocking? Offer technical support to unblock.

2. **Redemption Awareness** — Zero redemptions to date despite allowance set and growing engagement. Confirm users understand how to redeem and that rewards are appealing. Ask: "Have team members tried to redeem? Any confusion?"

3. **Health Check / Expansion Signal** — 15 active givers is strong mid-onboarding traction. Use this momentum to: (a) confirm admin team is comfortable, (b) identify any champions or power users to involve in advocacy, (c) discuss next steps for driving program participation company-wide.
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 $0.0310 · 124s · in 23,883 / out 18,211 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
**Period: September 6, 2026 – December 5, 2026**

---

## RENEWALS BY ACCOUNT

### SEPTEMBER 2026

**C-0B7D2C30** | CSM: Dana Mercer | ARR: $65,901  
Renewal: **2026-09-15** (Chargebee — multi-year; ChurnZero dates unreliable)  
⚠️ DISAGREEMENT: ChurnZero=2026-09-10, Chargebee=2026-09-15  
Seat Utilization: **57.6%** (274/476) | 3-Month Trend: **Declining**  
**Risk: HIGH** — Usage declined 13 users over 3 months (97→84) with only 57.6% seat utilization.

---

**C-0BCDB8C2** | CSM: Cole Ingram | ARR: $54,427  
Renewal: **2026-09-18** (Chargebee — multi-year; ChurnZero dates unreliable)  
⚠️ DISAGREEMENT: ChurnZero=2027-09-18, Chargebee=2026-09-18  
Seat Utilization: **54.7%** (232/424) | 3-Month Trend: **Declining**  
**Risk: HIGH** — Usage declined 17 users over 3 months (127→110) with only 54.7% seat utilization.

---

**C-0D2AB865** | CSM: Elena Sinclair | ARR: $38,022  
Renewal: **2026-09-22** (Chargebee — multi-year; ChurnZero dates unreliable)  
⚠️ DISAGREEMENT: ChurnZero=2026-09-10, Chargebee=2026-09-22  
Seat Utilization: **61.4%** (250/407) | 3-Month Trend: **Declining**  
**Risk: MEDIUM** — Usage declined from 125 to 109 over 3 months; moderate utilization at 61.4%.

---

**C-0BBE3E60** | CSM: Dana Mercer | ARR: $30,993  
Renewal: **2026-09-26** (Chargebee — multi-year; ChurnZero dates unreliable)  
⚠️ DISAGREEMENT: ChurnZero=2027-09-26, Chargebee=2026-09-26  
Seat Utilization: **64.9%** (74/114) | 3-Month Trend: **Declining**  
**Risk: MEDIUM** — Usage declined from 39 to 33 over 3 months; moderate utilization at 64.9%.

---

**C-0F5D2323** | CSM: Cole Ingram | ARR: $90,647  
Renewal: **2026-09-29** (Chargebee — multi-year; ChurnZero dates unreliable)  
⚠️ DISAGREEMENT: ChurnZero=2026-09-10, Chargebee=2026-09-29  
Seat Utilization: **28.5%** (111/390) | 3-Month Trend: **Stable**  
**Risk: MEDIUM** — Low seat utilization (28.5%) despite stable usage trend.

---

### OCTOBER 2026

**C-0EC6999D** | CSM: Elena Sinclair | ARR: $79,419  
Renewal: **2026-10-03** (Both systems agree)  
Seat Utilization: **27.7%** (31/112) | 3-Month Trend: **Declining**  
**Risk: HIGH** — Usage declined 2 users over 3 months (17→15) with only 27.7% seat utilization.

---

**C-0B20DB64** | CSM: Dana Mercer | ARR: $21,770  
Renewal: **2026-10-07** (Both systems agree)  
Seat Utilization: **56.6%** (214/378) | 3-Month Trend: **Stable**  
**Risk: MEDIUM** — Stable usage with 56.6% utilization — monitor for expansion opportunity.

---

**C-0BBC4E7A** | CSM: Cole Ingram | ARR: $56,374  
Renewal: **2026-10-10** (Both systems agree)  
Seat Utilization: **67.7%** (228/337) | 3-Month Trend: **Declining**  
**Risk: MEDIUM** — Usage declined from 142 to 139 over 3 months; moderate utilization at 67.7%.

---

**C-0FD551AB** | CSM: Elena Sinclair | ARR: $48,815  
Renewal: **2026-10-14** (Both systems agree)  
Seat Utilization: **55.9%** (210/376) | 3-Month Trend: **Stable**  
**Risk: MEDIUM** — Stable usage with 55.9% utilization — monitor for expansion opportunity.

---

**C-0F9F8F13** | CSM: Dana Mercer | ARR: $46,230  
Renewal: **2026-10-18** (Both systems agree)  
Seat Utilization: **56.5%** (199/352) | 3-Month Trend: **Stable**  
**Risk: MEDIUM** — Stable usage with 56.5% utilization — monitor for expansion opportunity.

---

**C-0BC34584** | CSM: Cole Ingram | ARR: $16,740  
Renewal: **2026-10-22** (Both systems agree)  
Seat Utilization: **66.2%** (327/494) | 3-Month Trend: **Stable**  
**Risk: LOW** — Usage stable (104→106) with healthy 66.2% utilization.

---

**C-0B7A7546** | CSM: Elena Sinclair | ARR: $35,062  
Renewal: **2026-10-25** (Both systems agree)  
Seat Utilization: **88.8%** (182/205) | 3-Month Trend: **Stable**  
**Risk: LOW** — Usage stable (64→63) with healthy 88.8% utilization.

---

**C-0B369871** | CSM: Dana Mercer | ARR: $85,128  
Renewal: **2026-10-29** (Both systems agree)  
Seat Utilization: **75.1%** (317/422) | 3-Month Trend: **Growing**  
**Risk: LOW** — Usage growing (326→333) with healthy 75.1% utilization.

---

### NOVEMBER 2026

**C-0B144C78** | CSM: Cole Ingram | ARR: $30,899  
Renewal: **2026-11-02** (Both systems agree)  
Seat Utilization: **75.4%** (169/224) | 3-Month Trend: **Stable**  
**Risk: LOW** — Usage stable (101→106) with healthy 75.4% utilization.

---

**C-0FC4DBB8** | CSM: Elena Sinclair | ARR: $94,732  
Renewal: **2026-11-05** (Both systems agree)  
Seat Utilization: **76.7%** (356/464) | 3-Month Trend: **Growing**  
**Risk: LOW** — Usage growing (189→193) with healthy 76.7% utilization.

---

**C-0D5BBE3A** | CSM: Dana Mercer | ARR: $39,740  
Renewal: **2026-11-09** (Both systems agree)  
Seat Utilization: **83.3%** (85/102) | 3-Month Trend: **Growing**  
**Risk: LOW** — Usage growing (88→91) with healthy 83.3% utilization.

---

**C-0FB9D5AF** | CSM: Cole Ingram | ARR: $63,158  
Renewal: **2026-11-13** (Both systems agree)  
Seat Utilization: **72.4%** (144/199) | 3-Month Trend: **Stable**  
**Risk: LOW** — Usage stable (173→176) with healthy 72.4% utilization.

---

**C-0B344485** | CSM: Elena Sinclair | ARR: $64,384  
Renewal: **2026-11-16** (Both systems agree)  
Seat Utilization: **78.0%** (224/287) | 3-Month Trend: **Growing**  
**Risk: LOW** — Usage growing (238→244) with healthy 78.0% utilization.

---

**C-0CB2C1B4** | CSM: Dana Mercer | ARR: $40,628  
Renewal: **2026-11-20** (Both systems agree)  
Seat Utilization: **81.6%** (386/473) | 3-Month Trend: **Growing**  
**Risk: LOW** — Usage growing (47→49) with healthy 81.6% utilization.

---

**C-22170CA1** | CSM: Cole Ingram | ARR: $45,646  
Renewal: **2026-11-24** (Both systems agree)  
Seat Utilization: **85.4%** (251/294) | 3-Month Trend: **Stable**  
**Risk: LOW** — Usage stable (143→146) with healthy 85.4% utilization.

---

## SUMMARY

| Metric | Value |
|--------|-------|
| **Total accounts renewing (90-day window)** | 20 |
| **Total ARR renewing** | **$1,048,715** |
| **ARR at HIGH risk** | $199,747 |
| **ARR at MEDIUM risk** | $332,851 |
| **ARR at LOW risk** | $516,117 |
| **TOTAL ARR AT RISK (HIGH + MEDIUM)** | **$532,598** |

---

## DISAGREEMENTS FLAGGED (5 accounts)

All disagreements involve multi-year contracts where ChurnZero dates are known to be unreliable:

| Account | ChurnZero Date | Chargebee Date | Reason |
|---------|----------------|----------------|--------|
| C-0B7D2C30 | 2026-09-10 | 2026-09-15 | Multi-year |
| C-0BCDB8C2 | 2027-09-18 | 2026-09-18 | Multi-year (CZ off by +1 year) |
| C-0D2AB865 | 2026-09-10 | 2026-09-22 | Multi-year |
| C-0BBE3E60 | 2027-09-26 | 2026-09-26 | Multi-year (CZ off by +1 year) |
| C-0F5D2323 | 2026-09-10 | 2026-09-29 | Multi-year |

**Decision rule applied:** For multi-year contracts (is_multi_year=true in Chargebee), Chargebee dates are trusted as the authoritative source. ChurnZero is known to incorrectly project multi-year renewal dates forward by the full term length rather than showing the current renewal milestone.
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 $0.0414 · 169s · in 29,029 / out 23,363 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
Now I have the clear picture. Let me synthesize this properly, separating single-account noise from broad patterns:

**QUARTERLY SUPPORT TICKET THEMES (Q2-Q3 2026)**

Total: 80 tickets | 24 accounts | $1,598,900 ARR portfolio

---

**1. HRIS/PROVISIONING FAILURES**
- **Count:** 12 (15% of tickets)
- **Share:** 15.0%
- **Distinct accounts:** 3
- **ARR affected:** $114,000 (7.1% of portfolio)
- **Ticket IDs:** IC-460059, IC-460055
- **Accounts:** C-0B2213A9 ($36K), C-0F6C0F34 ($30K), C-0DDFC9A7 ($48K)
- **Recommendation:** Investigate HRIS sync logic — three mid-market accounts blocked on new hire provisioning for 3+ months. Risk of churn escalation.

---

**2. GIFT CARD/REDEMPTION FAILURES**
- **Count:** 14 (17.5%)
- **Share:** 17.5%
- **Distinct accounts:** 7
- **ARR affected:** $68,800 (4.3%)
- **Ticket IDs:** IC-460024, IC-460035
- **Recommendation:** Audit gift card fulfillment pipeline — points deducted but rewards never delivered across 7 accounts.

---

**3. POINTS NOT POSTING**
- **Count:** 20 (25%)
- **Share:** 25.0%
- **Distinct accounts:** 9
- **ARR affected:** $31,100 (1.9%)
- **Ticket IDs:** IC-460004, IC-460016
- **Recommendation:** Root-cause recognition credit timing issue — affects 9 accounts broadly.

---

**4. SLACK INTEGRATION FAILURES**
- **Count:** 14 (17.5%)
- **Share:** 17.5%
- **Distinct accounts:** 4
- **ARR affected:** $18,900 (1.2%)
- **Ticket IDs:** IC-460041, IC-460047
- **Recommendation:** Fix Slack sync toggle persistence — auth token refresh or permission drift.

---

**5. BILLING/INVOICE ERRORS — SINGLE-ACCOUNT NOISE**
- **Count:** 16 (20%)
- **Share:** 20.0%
- **Distinct accounts:** 1 (C-0E9C27D1)
- **ARR affected:** $52,000 (3.3%)
- **Ticket IDs:** IC-460071, IC-460069
- **Recommendation:** Account-specific billing config issue — C-0E9C27D1 has recurring seat count mismatch (charged 200 vs. licensed 150). Escalate to accounting for manual reconciliation.

---

**SUMMARY:**

| Theme | Count | Accounts | ARR | Type |
|-------|-------|----------|-----|------|
| HRIS/Provisioning | 12 | 3 | $114K | Broad pattern |
| Gift Card Failures | 14 | 7 | $68.8K | Broad pattern |
| Points Not Posting | 20 | 9 | $31.1K | Broad pattern |
| Slack Integration | 14 | 4 | $18.9K | Broad pattern |
| Billing/Invoice | 16 | 1 | $52K | Single-account noise |

Broad patterns (4 themes, 50 tickets, $232.8K ARR) ranked by ARR exposure. Billing/Invoice isolated as single-account noise — 16 tickets from C-0E9C27D1 alone represent 20% of volume but only 3.3% of portfolio ARR.
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 $0.0021 · 13s · in 874 / out 1,169 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
## Top 3 Similar Customers with Case Studies

| Rank | Company | Match Fields | Score |
|------|---------|-------------|-------|
| 1 | C-64171065 | Industry✓ Size✓ Use Case✓ | 3/4 |
| 2 | C-11C31562 | Size✓ Use Case✓ Region✓ | 3/4 |
| 3 | C-A13C193D | Industry✓ Size✓ Region✓ | 3/4 |

### Rationale

**Prospect (C-82AF3719):** Technology | Mid-Market | employee_recognition | NA-West

**1. C-64171065 (Score: 3/4)**
- Matching: Technology, Mid-Market, employee_recognition
- Gap: Region (NA-East vs NA-West)

**2. C-11C31562 (Score: 3/4)**
- Matching: Mid-Market, employee_recognition, NA-West
- Gap: Industry (Manufacturing vs Technology)

**3. C-A13C193D (Score: 3/4)**
- Matching: Technology, Mid-Market, NA-West
- Gap: Use Case (retention vs employee_recognition)

All three tied at 3 of 4 fields. Ranked by industry relevance (Technology closest to prospect's core domain), then use case alignment. C-9D69D147 and C-CD4829A7 both scored 2/4 and were excluded.
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 $0.0209 · 98s · in 14,714 / out 12,297 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 Analysis — Trailing 6 Months (Mar–Aug 2026)

### FLAGGED: SQO Precedes SQM
- **CT-000044** (linkedin_ads): SQM=2026-07-23, SQO=2026-07-18 (SQO 5 days before SQM)
- **CT-000041** (linkedin_ads): SQM=2026-06-14, SQO=2026-06-09 (SQO 5 days before SQM)

These 2 LinkedIn records have data quality issues —SQOs logged before SQMs. Flag for CRM hygiene review.

---

### PAID CHANNELS

| Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO% | Pipeline | Pipeline/$ |
|---------|-------|------|------|----------|----------|----------|----------|------------|
| paid_search | $36,000 | 40 | 18 | $900 | $2,000 | 45.0% | $720,000 | **$20.00** |
| linkedin_ads | $24,000 | 25 | 8 | $960 | $3,000 | 32.0% | $96,000 | $4.00 |
| **paid_social** | $18,000 | 0 | 0 | *undef* | *undef* | N/A | $0 | $0.00 |
| webinars | $9,000 | 12 | 5 | $750 | $1,800 | 41.7% | $60,000 | $6.67 |

**Paid social is undefined** — $18,000 spent with zero attributed SQMs. Either the tracking is broken or the creative is generating awareness but not capture.

---

### ORGANIC CHANNELS

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

---

### Reallocation Recommendation

**Cut:** paid_social ($18,000/mo → $0)
- Zero SQMs in 6 months despite spend. No attributable pipeline. Either disable or reallocate to a channel with proven returns.

**Scale:** paid_search (maintain or test increment)
- Best efficiency: $20 pipeline per dollar, 45% SQM→SQO rate, lowest cost per SQO ($2,000).
- With 18 SQOs over 6 months, signal is strong. Consider +10–15% budget to test scalability.

**Hold:** linkedin_ads, webinars
- LinkedIn: $4/$ is weak vs paid_search. Review audience/creative. 32% SQM→SQO suggests qualified reach but conversion friction.
- Webinars: Best cost per SQM ($750) but modest volume. Hold at current spend, optimize follow-up sequence.

**Grow:** referral
- Highest SQO rate (40%) and $3,200 pipeline per SQO (48,000/15). No spend required — double down on incentive program.

**Confidence:**
- paid_search: **High** (40 SQMs, 18 SQOs)
- linkedin_ads: **Medium** (25 SQMs, 8 SQOs) — flagged data quality reduces confidence
- paid_social: **None** (0 SQMs) — cannot evaluate
- webinars: **Medium** (12 SQMs, 5 SQOs)
- organic_search: **Medium** (30 SQMs, 10 SQOs)
- referral: **Low–Medium** (15 SQMs, 6 SQOs) — small sample but strong rate
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 $0.0037 · 27s · in 1,521 / out 2,289 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
I'll analyze all the data and create an updated battlecard.

**Pricing Analysis:**
- S17 (2026-08-12): $7/user/month - NEWEST PRICING SOURCE
- S18 (2026-08-14): $7/user/mo list, 15% discount for 3-year
- S13 (2026-06-02): $6.50/user/mo quoted to 500-seat prospect (older, different deal)

**Win/Loss Calculation:**
From deals_with_competitor.csv:
- Losses (7): Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F
- Wins (13): Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392
- **Record: 13W-7L (65% win rate)**

**Old card issues:**
- "Lacks Slack integration" - CONTRADICTED by S04: "Slack integration worked out of the box"
- "Acquired by WorkHuman in 2025" - NO SOURCE in snippets, marked unverified
- Pricing from Jan 2026 - outdated, now $7 as of Aug 2026

---

# Battlecard: Rivally (Updated)

## One-Line Positioning
Points-based employee recognition platform with strong mid-market traction and accelerating EU enterprise push. [S01, S12]

---

## Pricing
**Current list price:** $7 per user/month, annual billing required (Recognition Starter tier) [S17, 2026-08-12]

**Recent deal quotes:**
- $6.50/user/month to 500-seat prospect (Jun 2026) [S13]
- $7/user/month list + 15% discount for 3-year term (Aug 2026) [S18]

*Conflict noted: Pricing has increased from $5 (Jan 2026) to $7 (Aug 2026). Enterprise deals may see discounting ($6.50 quoted, 15% off for multi-year).*

---

## Where They Win

| Factor | Evidence |
|--------|----------|
| Fast implementation | Setup took under a week for mid-market [S04] |
| Slack integration | Works out of the box [S04] |
| EU data residency | Generally available as of Jul 2026 [S15]; pitched to prospects [S05] |
| Distributed EU teams | Multi-language support praised [S12] |
| Recognition feed engagement | Points-based feed is engaging and praised [S02, S16] |
| Support responsiveness | Response time under 4 hours [S22] |
| Teams integration | Microsoft Teams app v2 in public preview [S19] |

---

## Where We Win

| Factor | Evidence |
|--------|----------|
| Analytics depth | "reporting dashboards are basic compared to enterprise tools" [S07]; prospect cited analytics depth as reason for choosing Bonusly [S25] |
| SCIM provisioning | "Rivally lacks SCIM provisioning; manual user management is painful" [S10] |
| Bulk operations | "lacks bulk recognition editing" [S24] |
| Rewards catalog (EMEA) | "rewards catalog in EMEA is thinner than US catalog" [S14] |
| Migration/export | "migration off Rivally was hard because analytics exports are CSV-only" [S20] |
| Admin tooling | "admin tooling lags peers" [S16] |

---

## Objections and Responses

**"Their pricing is cheaper"**
- Response: Rivally's list price is now $7/user/month [S17], comparable to mid-market pricing. Recent deals show $6.50-$7 range with discounts for volume [S13, S18]. Emphasize total cost of ownership: their lack of SCIM forces manual management at scale [S10], and limited analytics may require add-on purchases or workaround tools.

**"They have EU data residency now"**
- Response: True—EU data residency is generally available [S15]. However, their EMEA rewards catalog is thinner than the US [S14]. If global reward consistency matters, we win on catalog depth.

**"Their recognition feed looks engaging"**
- Response: The feed is well-designed [S16], but admin tooling lags [S16, S24]. For ops teams managing recognition programs at scale, the lack of bulk editing [S24] and basic reporting [S07] create ongoing friction.

**"They set up fast"**
- Response: Under a week setup [S04] is table stakes. But enterprise buyers report pain with manual user management [S10]. At 500+ seats, SCIM matters—highlight our automated provisioning.

**"Support is responsive"**
- Response: Under 4-hour response is solid [S22]. But for complex reporting needs, we provide self-service analytics they need to chase support for [S07, S25].

---

## Recent Changes

| Date | Change | Source |
|------|--------|--------|
| Nov 2025 | Series C: $40M led by Northgate Ventures | S01 |
| Mar 2026 | Rivally Pulse engagement survey add-on launches | S06 |
| Jun 2026 | Ex-Workday VP EMEA hired for European expansion | S11 |
| Jul 2026 | Dublin office opened; EU data residency GA | S15 |
| Aug 2026 | Pricing increased: Recognition Starter now $7/user/mo | S17 |
| Aug 2026 | Microsoft Teams app v2 enters public preview | S19 |
| Sep 2026 | Rivally Pulse exits beta, priced as add-on | S23 |

---

## 12-Month Win/Loss Record Against Rivally

| Period | Wins | Losses | Win Rate |
|--------|------|--------|----------|
| Sep 2025 - Aug 2026 | 13 | 7 | **65%** |

**Deal breakdown:**
- Wins: Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392
- Losses: Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F

---

## Unverified (Removed from Previous Card)

| Claim | Status |
|-------|--------|
| Rivally lacks Slack integration | **REMOVED** - Contradicted by S04: "Slack integration worked out of the box" |
| Rivally was acquired by WorkHuman in 2025 | **UNVERIFIED** - No source in current data |
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 $0.0128 · 33s · in 27,943 / out 2,995 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
## Sequence Review

**New Logo Nurture**
- Step 1: 42% 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: Step 3 (meeting rate 1.4%)

**Expansion Nurture**
- Step 1: 43.3% open, 7.3% reply, 1.7% meeting
- Step 2: 113.3% open (ERROR), 8.3% reply, 1.3% meeting
- Step 3: 34.5% open, 4.4% reply, 1.1% meeting
- Weakest step: Step 3 (meeting 1.1%)

**Cold Outbound - HR Leaders**
- Step 1: 40% open, 0.8% reply, 0% meeting
- Step 2: 29.4% open, 0.3% reply, 0% meeting
- Step 3: 22% open, 0.2% reply, 0% meeting
- Weakest step: Step 1 (reply 0.8% — all steps under 2%)
- Failure mode: cold outbound to non-permissioned HR leaders = near-zero response

**Cold Outbound - People Ops**
- Step 1: 37.5% open, 3.5% reply, 0.8% meeting
- Step 2: 28.5% open, 2.3% reply, 0.5% meeting
- Step 3: 21.2% open, 1.6% reply, 0.3% meeting
- Weakest step: Step 3 (reply 1.6%)

---

**Tracking Error:** Expansion Nurture Step 2 — opened (340) > sent (300). Data integrity issue; investigate duplicate contacts or cross-sequence contamination.

**Audience Overlap:** None. Contacts are siloed by sequence.

---

**Priority Fix:** Cold Outbound - HR Leaders. Reply rates 0.8%, 0.3%, 0.2% — all well under 2%, yielding zero meetings across all steps. This is a catastrophic failure mode: the audience is either non-qualified or the messaging is misaligned. **Change:** Pause or fundamentally re-segment this sequence; test with a permissioned ICP (e.g., warm introductions, LinkedIn engagement) before resuming.

**Second priority:** Cold Outbound - People Ops Step 3 reply rate (1.6%) just crossed below 2%. Consider shortening to 2 steps or testing new copy at Step 3.
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 $0.0022 · 12s · in 498 / out 1,303 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
## Q3-2026 Weekly Marketing Goals Update

### Performance Summary

| Metric | QTD Actual | Target | Delta | Pace |
|--------|-----------|--------|-------|------|
| SQMs | 230 | 300 | -70 | **Ahead** (230 vs 215 expected) |
| SQOs | 84 | 120 | -36 | Behind (84 vs 86 expected) |
| DS2s | 40 | 75 | -35 | Behind (40 vs 54 expected) |
| Closed-Lost MIA Rate | 20% | 10% | +10pp | Behind (20% vs 10% expected) |
| Same-Quarter Closes | 10 | 20 | -10 | Behind (10 vs 14 expected) |
| Active Pipeline | $3.0M | $4.0M | -$1.0M | **Ahead** ($3.0M vs $2.87M expected) |

### Calculations

**Pace formula:** Expected = Target × (Days Elapsed ÷ Days in Quarter) = Target × (66 ÷ 92) = Target × 0.7174

- **SQMs:** 230 actual vs 215 expected → **AHEAD** by 15 units
- **SQOs:** 84 actual vs 86 expected → **BEHIND** by 2 units  
- **DS2s:** 40 actual vs 54 expected → **BEHIND** by 14 units
- **MIA Rate:** (5 MIA ÷ 25 total closed-lost) = 20% vs 10% target → **BEHIND** (lower is better)
- **Same-Quarter Closes:** 10 actual vs 14 expected → **BEHIND** by 4 units
- **Active Pipeline:** $3.0M actual vs $2.87M expected → **AHEAD** by ~$130K

### What Moved This Week

I cannot determine week-over-week movement from the provided data — the files contain only quarter-to-date totals, not prior week snapshots. To report what moved this week, I would need either a previous week's snapshot or a delta field tracking week-over-week changes.
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 $0.0016 · 11s · in 772 / out 781 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 forecast is $115,976 (54 deals: $44,729 committed + 35% of $203,565 best case). But 32 deals ($227,575) are excluded for October closes, and 71 of 86 deals lack why-buy—forecast precision is illusory without re-qualification.
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 $0.0010 · 8s · in 566 / out 343 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: Quick check-in on the 150-seat pricing we sent

Hi there,

Following up on the pricing I sent after our July 28 demo — I wanted to see if you had a chance to review it for your 150-seat team?

The VP People typically owns the recognition budget, so I wanted to make sure this landed with the right person. Let me know if you'd like to hop on a quick call to walk through any questions or discuss next steps.

Thanks,
Alex
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 $0.0015 · 12s · in 420 / out 794 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
# Weekly GTM Digest

**Marketing** delivered 46 SQMs this week against a target of 52, reaching 88% of goal. The webinar channel contributed 18 of those SQMs, proving that virtual events remain a powerful demand driver. Paid search kept cost efficiency steady at $150 per SQM.

**Sales** converted 14 SQOs and set 9 DS2 meetings while generating $310,000 in new pipeline. The team closed 3 deals within the same quarter to keep revenue flowing.

**CS** saved 2 renewals this week, maintaining strong customer relationships. Team NPS climbed to 61, reflecting continued satisfaction. There are 3 open red-flag accounts requiring attention heading into next week.

**PLG** added 412 new signups with activation holding at 31%. Thirty-eight companies reached the aha moment—defined as 10 recognition gives—demonstrating the viral power of a recognition-first culture.
partner-digest-sectionPASS 1.00
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 $0.0016 · 9s · in 608 / out 760 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**
- Sourced Pipeline: 2 deals | $275,000
- Activity: 2 sourced opps in DS1. Co-webinar locked for 09-15. Deals: Deal-DDAAF2 ($180,000), Deal-2CF33E ($95,000)

**HRCloud Partners**
- Sourced Pipeline: 1 deal | $140,000
- Activity: 1 sourced opp moved to DS2 (security review closed). Deal: Deal-F1CDA5 ($140,000)

**CultureBridge**
- Sourced Pipeline: 2 deals | $135,000
- Activity: Lunch-and-learn produced 2 sourced opps (early stage). Deals: Deal-096E1D ($60,000), Deal-067213 ($75,000)

**WorkWell Group**
- Sourced Pipeline: 0 deals | $0
- Activity: No sourced deals this period. Planning call booked for 09-09. Wants to restart joint playbook in Q4.

## Quiet Partners

- Recogniq — no activity this period
- KudosWave — no activity this period
- PeopleFirst Advisors — no activity this period
- TotalPerk — no activity this period

---

**Partner-Sourced Pipeline Total:** 5 deals | $550,000
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 $0.0018 · 10s · in 646 / out 962 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
# Security & Compliance Questionnaire Responses

**Q1: Is customer data encrypted at rest? If so, how?**
Yes. All customer data is encrypted at rest using AES-256. [P1]

**Q2: Is data encrypted in transit?**
Yes. Data in transit is encrypted with TLS 1.2 or higher. [P1]

**Q3: Do you hold a SOC 2 report, and can we obtain a copy?**
Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. The report is available to prospects under NDA. [P2]

**Q4: How long is customer data retained after contract termination?**
Customer data is retained for the duration of the contract plus 90 days, after which it is deleted from production systems. [P3]

**Q5: Where is your subprocessor list published and how are changes notified?**
The subprocessor list is maintained at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. [P4]

**Q6: Do you support SAML single sign-on?**
Yes. SAML 2.0 single sign-on is available on the Enterprise plan. [P5]

**Q7: What is your production backup schedule and retention?**
Production databases are backed up daily. Backups are encrypted and retained for 35 days. [P6]

**Q8: Do you hold an ISO 27001 certification?**
Unanswerable from the provided excerpts. This information would be found in the formal certification portfolio or trust documentation.

**Q9: What is your contractual uptime SLA percentage?**
Unanswerable from the provided excerpts. This information would be found in the Master Services Agreement (MSA) or SLA documentation.

**Q10: Will you sign a HIPAA Business Associate Agreement?**
Unanswerable from the provided excerpts. This information would be found in the security compliance matrix or contractual terms.
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 $0.0254 · 26s · in 75,486 / out 2,155 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
I'll systematically analyze the skill manifest and files against the seven reconciliation criteria.

## Reconciliation Report

### (1) OVERLAPPING ALWAYS-TRIGGER PHRASES — WARNING

**Found 2 overlapping trigger groups:**

| Trigger Phrase | Skills |
|---|---|
| "write me an email", "draft a follow-up" | `comms-drafter`, `email-drafter` |
| "run the pipeline report", "pipeline review" | `pipeline-intelligence-report`, `weekly-pipeline-report` |

**Action:** TRIM_DESC — consolidate email skills; clarify pipeline report triggers with specificity.

---

### (2) CIRCULAR DELEGATION CHAIN — CRITICAL

**Circular chain detected:**

```
deal-strategy-coach → email-drafter (line: "use the `email-drafter` skill")
email-drafter → deal-strategy-coach (line: "use deal-strategy-coach instead")
```

Both skills reference each other as alternatives, creating a delegation loop.

**Action:** MERGE — combine these into one skill with conditional logic, or delete one and redirect triggers.

---

### (3) DANGLING DELEGATION TARGET — WARNING

| Delegating Skill | Missing Target |
|---|---|
| `email-drafter` | `prospect-research-multithreading` (referenced, not in manifest) |
| `deal-strategy-coach` | `prospect-research-multithreading` (referenced, not in manifest) |

**Action:** UPDATE_BODY — either add missing skill to manifest or remove references.

---

### (4) VERSION CONFLICTS — INFO

Multiple skills declare versions in frontmatter but no conflicts exist between same skills. Version declarations:
- `analysis-validator`: v3.6 (described in body as v3.6)
- `pipeline-intelligence-report`: v6 (in description)
- `sales-forecast`: v1.1 (in changelog)
- `signalforge-feedback`: v1.0 (in changelog)

No duplicate skill names with conflicting versions found.

**Action:** REVIEW — no action required; versions are consistent.

---

### (5) DESCRIPTIONS EXCEEDING 1,024 CHARACTERS — WARNING

| Skill | Description Chars | Status |
|---|---|---|
| `partner-digest` | 1,004 | OK |
| `pipeline-intelligence-report` | 1,006 | **EXCEEDS** |
| `signalforge-claim-compressor` | 1,006 | **EXCEEDS** |

**Count: 2** manifest descriptions exceed 1,024 characters.

**Action:** TRIM_DESC — shorten descriptions for pipeline-intelligence-report and signalforge-claim-compressor.

---

### (6) HARDCODED IDS, DATES, PERSONS — CRITICAL

**Hardcoded page IDs:**
- `partner-digest`: `2286616609` (Partnerships Digest folder), `1958248479` (Space ID)
- `signalforge-feedback`: `2295136266` (Feedback Log page)

**Hardcoded dates:**
- `analysis-validator`: "April 26, 2026", "May 9, 2026" (multiple)
- `closed-lost-analysis`: "May 2026" (multiple references)
- `deal-strategy-coach`: "May 2026" (Playbook date)
- `model-selection`: "2026-05-19" (last_checked)
- `partner-digest`: "May 16, 2026" (reference issue)
- `pipeline-intelligence-report`: "May 2026" (version)
- `stale-pipeline-report`: "2026-06-10"
- `weekly-pipeline-report`: "April 1 – June 30, 2026"

**Hardcoded person names (in bodies):**
- `analysis-validator`: Manish, Amani (Finance escalation)
- `deal-strategy-coach`: Alaina Loori (VP Sales), full AE roster with IDs
- `partner-digest`: Amani Phipps (RevOps/Partnerships), Kelli, Jen Lee, Hani, Bryce, Sara
- `pipeline-intelligence-report`: Alaina Loori, full AE names
- `stale-pipeline-report`: Ben Lavin (Demand Gen)

**Action:** UPDATE_BODY — replace hardcoded dates with dynamic references; externalize person names to roster skills.

---

### (7) MANIFEST DRIFT — INFO

**Files with no manifest row:** NONE — all 14 files have corresponding rows.

**Manifest rows with no file:** NONE — all 14 rows have corresponding files.

**Status: CLEAN** — no drift detected in either direction.

---

## Summary of Findings

| Severity | Finding | Count | Action |
|---|---|---|---|
| CRITICAL | Circular delegation (deal-strategy-coach ↔ email-drafter) | 1 | MERGE |
| CRITICAL | Hardcoded page IDs in bodies | 2 | UPDATE_BODY |
| CRITICAL | Hardcoded person names in bodies | 5 skills | UPDATE_BODY |
| WARNING | Overlapping ALWAYS-trigger phrases | 2 groups | TRIM_DESC |
| WARNING | Dangling delegation target | 1 target | UPDATE_BODY |
| WARNING | Descriptions >1,024 chars | 2 | TRIM_DESC |
| INFO | Version conflicts | 0 | REVIEW |
| INFO | Manifest drift | 0 | — |

**Total actionable items: 7**
runbook-from-slack-thread0.75
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 $0.0018 · 10s · in 774 / out 892 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
# Runbook: reward-worker Queue Backup — incident-2026-08-29-reward-queue

## Step 1: Diagnose Queue Depth
- **Command/Action:** `bundle exec rake sidekiq:queue_depth`
- **Who:** Farid Osman [M02]
- **Verification:** Returned 48,213 pending jobs (normal: <500)
- **Rollback:** None (read-only diagnostic)

## Step 2: Check Dead Letter Queue
- **Command/Action:** Inspected dead set in console
- **Who:** Farid Osman [M03]
- **Verification:** Found 112 jobs, all Redis::TimeoutError from ~13:58
- **Rollback:** None (read-only diagnostic)

## Step 3: Pause Job Enqueue (Stop the Bleed)
- **Command/Action:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
- **Who:** Farid Osman [M04]
- **Verification:** Queue depth stopped growing
- **Rollback:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` [M04]

## Step 4: Clear Dead Letter Queue
- **Command/Action:** Cleared dead set from Rails console
- **Who:** Elena Sinclair [M05]
- **Verification:** Dead set emptied
- **Rollback:** None (cannot restore deleted jobs; re-process from source if needed — not documented in thread)

## Step 5: Scale Up Workers
- **Command/Action:** `kubectl scale deployment/reward-worker --replicas=6`
- **Who:** Bryce Harmon [M06]
- **Verification:** Queue depth dropped ~1,200/min [M07]
- **Rollback:** `kubectl scale deployment/reward-worker --replicas=3` [M06]

## Step 6: Verify Queue Drained
- **Command/Action:** `bundle exec rake sidekiq:queue_depth`
- **Who:** Cole Ingram [M08]
- **Verification:** Returned 0; Datadog error rate back to baseline
- **Rollback:** None (read-only diagnostic)

## Step 7: Re-enable Job Enqueue
- **Command/Action:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
- **Who:** Bryce Harmon [M09]
- **Verification:** 40 new jobs processed cleanly in 3 minutes
- **Rollback:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` (inverse of step 3)

## Step 8: Scale Workers Back to Normal
- **Command/Action:** `kubectl scale deployment/reward-worker --replicas=3`
- **Who:** Bryce Harmon [M10]
- **Verification:** Queue stable at 0
- **Rollback:** `kubectl scale deployment/reward-worker --replicas=6` (inverse of step 5)
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 $0.0031 · 18s · in 1,842 / out 1,746 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
## Failure Sequence Analysis

### First Error
**Timestamp:** 2026-09-03T14:01:12Z  
**Message:** `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`  
**Service:** reward-service

### Cascade in Order (14:01:12 → 14:06:52)

| Time | Service | Event |
|------|---------|-------|
| 14:01:12 | reward-service | Redis connection timeout (FIRST ERROR) |
| 14:01:20 | reward-service | retry exhausted for RewardGiveJob |
| 14:01:30 | reward-service | retry exhausted for RewardGiveJob |
| 14:01:40 | reward-service | retry exhausted for RewardGiveJob |
| 14:01:40 | sidekiq | RewardGiveJob failed; retrying in 60s |
| 14:02:28 | sidekiq | RewardGiveJob failed; retrying |
| 14:02:30 | sidekiq | Queue depth > 10,000 (WARN) |
| 14:03:05 | api-gateway | 502 upstream timeout → reward-service /gives |
| 14:03:30 | web-app | Give form submission failed: upstream 502 |
| 14:03:31 | sidekiq | RewardGiveJob failed; retrying |
| 14:03:48 | api-gateway | 502 upstream timeout → reward-service |
| 14:04:13 | api-gateway | 502 upstream timeout → reward-service |
| 14:04:22 | sidekiq | RewardGiveJob failed; retrying |
| 14:04:45 | web-app | Give form submission failed: upstream 502 |
| 14:05:16 | api-gateway | 502 upstream timeout → reward-service |
| 14:05:26 | sidekiq | RewardGiveJob failed; retrying |
| 14:05:42 | web-app | Give form submission failed: upstream 502 |
| 14:06:47 | sidekiq | RewardGiveJob failed; retrying |
| 14:06:49 | web-app | Give form submission failed: upstream 502 |
| 14:06:52 | api-gateway | 502 upstream timeout → reward-service |

### Service & Job
- **Primary service:** reward-service
- **Downstream services:** sidekiq (job processing), api-gateway (routing), web-app (UI)
- **Affected job:** RewardGiveJob (also RecognitionDigestJob per sidekiq_jobs.csv)

### Datadog Query for First Error
```
service:reward-service level:error "Redis::TimeoutError" "redis-primary:6379"
```
Or equivalently:
```
service:reward-service level:error message:"*Redis*Timeout*"
```

### What the Logs Do NOT Show

1. **Root cause of Redis timeout** — No network blips, OOM kills, or Redis server logs
2. **Redis-side metrics** — No memory usage, CPU, connection pool saturation, or slowlog entries
3. **Recovery trigger** — Logs show restoration at 14:22:10 but not what fixed it (manual intervention? self-heal? timeout adjustment?)
4. **Impact scope** — No data on how many reward requests failed, user impact count, or financial/revenue impact
5. **Pre-failure indicators** — No gradual degradation, queue growth warnings, or Redis latency spikes leading up to 14:01:12
6. **Related job data** — No payload details for the failed RewardGiveJobs to understand what operations were in flight
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 $0.0019 · 12s · in 670 / out 1,036 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

### 1. recognition_streaks_v2
- **What it controls**: Tracks recognition streaks when a "give" occurs via `StreakTracker.record(give)`
- **State**: ON
- **Targeting**: segment:beta_companies
- **Company count**: 42

### 2. points_budget_guardrails
- **What it controls**: Enforces budget limits on points via `BudgetService.new(company).enforce!(giver, points)`
- **State**: ON
- **Targeting**: all_companies
- **Company count**: 220

### 3. slack_dm_nudges
- **What it controls**: Sends Slack DM nudges via `SlackDm.send_nudge(user)`
- **State**: ON
- **Targeting**: segment:region_na
- **Company count**: 87

### 4. redeem_flow_redesign
- **What it controls**: Renders new redeem UI (`RedeemV2Component`) vs legacy (`RedeemV1Component`)
- **State**: OFF
- **Targeting**: targeted_list
- **Company count**: 12

### 5. analytics_dashboard_v3
- **What it controls**: Loads the v3 analytics dashboard via `AnalyticsV3.new(company)`
- **State**: ON
- **Targeting**: segment:tier_three
- **Company count**: 65

### 6. ms_teams_app_v2
- **What it controls**: Installs Microsoft Teams app V2 via `TeamsAppV2.install(company)`
- **State**: OFF
- **Targeting**: targeted_list
- **Company count**: 9

---

## Flags with No Code Reference

| Flag | State | Targeting | Company Count |
|------|-------|-----------|---------------|
| legacy_give_modal | OFF | segment:legacy_plan | 14 |
| survey_boosters_q3 | ON | segment:legacy_plan | 7 |
| paused_offboard_cleanup | OFF | *(none)* | 0 |

---

## Flags with No Targets

| Flag | State | Targeting | Company Count |
|------|-------|-----------|---------------|
| paused_offboard_cleanup | OFF | *(empty)* | 0 |

This flag has an empty targeting_rules field and 0 companies — appears to be a cleanup flag with no active rollout.
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 $0.0017 · 13s · in 721 / out 837 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 Results

---

### NDA-1 — [PARTY A] and Bonusly

**Classification: GREEN**

Reasoning: Standard mutual NDA with no concerning provisions. Term (2 years), survival (3 years), carve-outs (publicly available, prior knowledge, third-party receipt, independent development, legal disclosure), and Delaware law are all market-standard. No non-solicit, non-compete, or exclusivity clauses.

---

### NDA-2 — [PARTY B] and Bonusly

**Classification: YELLOW**

Reasoning: Adds a **non-solicitation clause** (clause 4): *"neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement"* — 18 months post-term. The clause is narrowly scoped to employees "contacted under this Agreement," which limits risk, but the clause itself is a departure from standard NDAs and warrants counsel review to confirm the scope is acceptable.

---

### NDA-3 — [PARTY C] and Bonusly

**Classification: RED**

Reasoning: **Four critical issues:**

1. **One-way confidentiality** (clause 1) — Bonusly receives no protection; only Party C's information is protected.

2. **Non-compete clause** (clause 2): *"Recipient shall not...engage in or provide services to any business that competes with Discloser's business"* — 3-year term with **no geographic limitation**, **no customer/prospect restriction**, and **no carve-out for general employment**. This is overly broad and potentially enforceable against Bonusly employees.

3. **Missing carve-outs** — Clause 3 explicitly states *"No confidentiality carve-outs are stated in this Agreement."* This means information that becomes public, is independently developed, or received from third parties may still be deemed confidential — a significant liability.

4. **Governing law** (clause 3): Irish law — unusual for a US company (Bonusly), introduces unfamiliar legal jurisdiction and potential enforcement complexity.

This NDA requires full legal review before execution.