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google/gemma-4-31b-it

Mean 0.889 · 24/40 perfect tests · $0.16 total · modalities in: text, image, video · out: text · each card: the prompt → the correct answer (gold) → this model's actual answer

What do the modalities mean?

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

data-crm 5 tests
stage-hygiene-audit0.83
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 $0.0088 · 329s · in 56,647 / out 8,334 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
Definitions:
- Non-compliant why-buys: Deals at DS2 or later lacking a substantive why_buys value (defined as 15+ characters and not a placeholder).
- No DS1 timestamp: Deals missing a value in the t_ds1 field.
- Regressed: Deals where a timestamp for a later stage is earlier than a timestamp for an earlier stage.

Hygiene Metrics:
- Why-buys non-compliant overall: 81.45% (101/124)
- Why-buys non-compliant by owner:
    - Bryce Harmon: 83.33% (15/18)
    - Alex Franklin: 78.69% (48/61)
    - Dana Mercer: 81.82% (18/22)
    - Cole Ingram: 86.36% (19/22)
    - Farid Osman: 100.00% (1/1)
- Deals with no DS1 timestamp: 1
- Deals that regressed: 0

Process Fixes:
1. Implement a CRM validation rule that blocks progression to DS2 unless the why_buys field contains 15+ characters.
2. Configure an automated weekly exception report to flag deals missing DS1 timestamps for immediate owner remediation.
3. Establish a mandatory "Deal Review" gate at DS2 where a manager verifies the substance of why-buys before the deal is officially accepted into the pipeline.
owner-resolution0.33
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 $0.0052 · 157s · in 19,455 / out 7,662 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

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- Number of deals resolved: 154
- 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:
- Bryce Harmon: 931,564 (24,000 + 19,656 + 13,500 + 7,000 + 2,520 + 240,000 + 99,000 + 72,000 + 70,000 + 63,600 + 45,000 + 1 + 21,000 + 23,400 + 13,680 + 5,502 + 8,160 + 1 + 11,400 + 1 + 36,000 + 31,500 + 6,000 + 10,800 + 30,275 + 17,400 + 12,600 + 18,000 + 37,440 + 18,828 + 2,880 + 36,000 + 20,880 + 10,920 + 25,200)
- Alex Franklin: 559,160 (14,850 + 13,770 + 11,200 + 9,000 + 6,360 + 5,400 + 3,240 + 2,484 + 1,920 + 1,080 + 7,200 + 19,000 + 2,880 + 1,400 + 4,800 + 1,632 + 10,000 + 9,300 + 2,700 + 2,160 + 1,800 + 3,600 + 3,840 + 15,000 + 1,968 + 4,000 + 3,600 + 4,800 + 3,120 + 2,520 + 9,000 + 2,400 + 62,000 + 5,400 + 5,100 + 16,700 + 4,400 + 1,620 + 2,600 + 7,200 + 18,000 + 17,000 + 8,316 + 8,100 + 18,000 + 12,600 + 24,000 + 15,000 + 9,000 + 7,200 + 3,780 + 16,200 + 7,200 + 4,680 + 1,800 + 18,000 + 2,730 + 2,400 + 3,060 + 18,000 + 12,000 + 1,800 + 4,400 + 31,200 + 7,200 + 1,600 + 60,000)
- Dana Mercer: 323,775 (11,250 + 10,500 + 9,000 + 9,000 + 5,400 + 4,800 + 4,600 + 15,000 + 4,200 + 18,900 + 27,000 + 43,875 + 20,000 + 60,000 + 8,100 + 16,250 + 3,150 + 5,000 + 2,100 + 23,400 + 5,400 + 7,350 + 25,000)
- Cole Ingram: 284,181.43 (2,760 + 1,330 + 7,225.4 + 3,360 + 5,616 + 2,700 + 7,781.2 + 6,947.5 + 40,000 + 12,168 + 2,480.4 + 11,116 + 11,193 + 1,875 + 31,750 + 32,175 + 4,779.88 + 3,334.8 + 58,529.25 + 4,140 + 18,000 + 18,900)
- Farid Osman: 4,134 (1,249 + 1 + 1 + 1 + 2,880 + 1 + 1)
- Elena Sinclair: 2,100 (2,100)
gong-hubspot-join-integrity0.50
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 $0.0079 · 214s · in 40,953 / out 12,517 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

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(a) Orphans: 35
Arithmetic: 67 total Gong rows - 32 matching deals in deals_open = 35 orphans
Sample aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945

(b) Duplicate conversation keys: 0
Arithmetic: 0 rows where calls_90d > distinct_conversation_keys

(c) Share of DS3+ deals with calls: 32.47%
Arithmetic: 25 deals with calls / 77 total deals at stage DS3 or later = 32.47%
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 $0.0037 · 102s · in 1,117 / out 8,131 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

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```sql
SELECT
    -- Assuming COMPANY_ID exists as the identifier for the company in this table
    COMPANY_ID, 
    -- Data for 'unique givers' is not explicitly provided as a distinct count in the catalog; 
    -- M1_USERS is used as the only available proxy for unique participants in the first month.
    M1_USERS AS unique_givers,
    -- Pre-calculated recognition count for the first calendar month (M1)
    M1_GIVES AS recognition_count,
    -- Pre-calculated redemption count for the first calendar month (M1)
    M1_REDEMPTIONS AS successful_redemption_count
-- Using COMPANY_COHORT_SUMMARY as it is the only provided source containing both giving and redemption 
-- metrics aggregated for the first calendar month (M1) of the customer lifecycle.
FROM PRODUCTION.PLG.COMPANY_COHORT_SUMMARY;
```
crm-completeness-enrichment0.80
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 $0.0026 · 17s · in 3,577 / out 4,341 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

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### Completeness Audit

**Deals** (No data provided)
- Owner: 0%
- Stage: 0%
- Amount: 0%
- Close Date: 0%
- Why-Buys: 0%

**Companies** (35 total)
- Industry: 100% (35/35)
- Employee Count: 80.0% (28/35)
- HQ Country: 85.7% (30/35)

**Contacts** (62 total)
- Email: 100% (62/62)
- Title: 80.6% (50/62)
- Persona: 77.4% (48/62)

---

### Duplicate Company Clusters
- **Cluster 1**: C-0A092931, C-0A092932 (acme-corp.com) $\rightarrow$ Survivor: C-0A092931
- **Cluster 2**: C-0A092933, C-0A092934 (globex.io) $\rightarrow$ Survivor: C-0A092933

---

### Email and Domain Validation
**Invalid Emails**
- CT-0010: user0@
- CT-0080: user0@
- CT-0081: user1@
- CT-0192: user2@

**Domain Mismatches**
- CT-0011: user1@other-domain.com (Expected: 66d1fc.com)

---

### Company Field Fixes

**Missing Values Filled via Enrichment**
- C-EC3025: Employee Count $\rightarrow$ 400
- C-96039F: Employee Count $\rightarrow$ 400
- C-44EA29: Employee Count $\rightarrow$ 400
- C-D04904: Employee Count $\rightarrow$ 400
- C-B23205: Employee Count $\rightarrow$ 400
- C-60C75F: Employee Count $\rightarrow$ 400
- C-7BBDFA: Employee Count $\rightarrow$ 400
- C-50D386: Employee Count $\rightarrow$ 400

**CRM vs Enrichment Disagreements**
| Company | Field | CRM Value | Enrichment Value | Recommendation |
| :--- | :--- | :--- | :--- | :--- |
| C-66D1FC | HQ Country | US | United States | Enrichment |
| C-950043 | HQ Country | US | United States | Enrichment |
| C-EC3025 | HQ Country | USA | United States | Enrichment |
| C-96039F | HQ Country | USA | United States | Enrichment |
| C-B23205 | HQ Country | US | United States | Enrichment |
| C-E51FB7 | HQ Country | USA | United States | Enrichment |
| C-425E2A | HQ Country | USA | United States | Enrichment |
| C-2D7423 | HQ Country | USA | United States | Enrichment |
| C-BA969B | HQ Country | US | United States | Enrichment |

---

### Top 10 Fixes by Pipeline at Stake
Data missing. No deal amounts or pipeline values were provided in the extract.
deal-intelligence 5 tests
closed-lost-classificationPASS 1.00
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 $0.0040 · 77s · in 6,507 / out 7,586 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

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### Deal Classifications

| Deal Alias | Category | Side |
| :--- | :--- | :--- |
| Deal-DB0AAC | timing | buyer |
| Deal-F7F635 | competitor | buyer |
| Deal-AC944F | no decision | buyer |
| Deal-214060 | no decision | buyer |
| Deal-91A056 | timing | buyer |
| Deal-29326C | timing | buyer |
| Deal-5DB9B0 | other | buyer |
| Deal-831B7B | timing | buyer |
| Deal-F97C37 | competitor | buyer |
| Deal-13E9CF | timing | buyer |
| Deal-39E25C | timing | buyer |
| Deal-7ED004 | pricing | buyer |
| Deal-21B045 | no decision | buyer |
| Deal-B3ABED | timing | buyer |
| Deal-422BA6 | competitor | buyer |
| Deal-ED9AE7 | timing | buyer |
| Deal-988493 | no decision | buyer |
| Deal-381C8C | competitor | buyer |
| Deal-F308CA | no decision | buyer |
| Deal-F1E8A6 | competitor | buyer |
| Deal-B6AC09 | timing | buyer |
| Deal-70F704 | no decision | buyer |
| Deal-E6E80A | timing | buyer |
| Deal-B038F0 | timing | buyer |
| Deal-4664E1 | no decision | buyer |
| Deal-175756 | timing | buyer |
| Deal-E74A73 | no decision | buyer |
| Deal-DDAB52 | competitor | buyer |
| Deal-ACE061 | competitor | buyer |
| Deal-BB78F3 | timing | buyer |
| Deal-D48E0B | no decision | buyer |
| Deal-15DA99 | timing | buyer |
| Deal-F4AF5D | timing | buyer |
| Deal-79B7A1 | timing | buyer |
| Deal-583ADB | no decision | buyer |
| Deal-8E27DA | no decision | buyer |
| Deal-2D2F8D | competitor | buyer |
| Deal-E0441F | no decision | buyer |
| Deal-7CB44D | no decision | buyer |
| Deal-0F96AA | competitor | buyer |
| Deal-1BCA50 | competitor | buyer |
| Deal-7CC678 | competitor | buyer |
| Deal-FAC17C | other | buyer |
| Deal-242273 | product gap | Bonusly |
| Deal-50E5D8 | timing | buyer |
| Deal-A2C349 | competitor | buyer |
| Deal-9F176A | timing | buyer |
| Deal-7B2236 | pricing | buyer |
| Deal-AFA56C | no decision | buyer |
| Deal-C7156E | competitor | buyer |
| Deal-C33D91 | pricing | buyer |
| Deal-9048EB | product gap | Bonusly |
| Deal-5E64CE | timing | buyer |
| Deal-8A0992 | competitor | buyer |
| Deal-D0C698 | competitor | buyer |
| Deal-69CF3D | timing | buyer |
| Deal-ECBF89 | timing | buyer |
| Deal-3618CC | product gap | buyer |
| Deal-EECC02 | competitor | buyer |
| Deal-5AD03E | pricing | buyer |
| Deal-D1A623 | timing | buyer |
| Deal-413C56 | timing | buyer |
| Deal-47F1A1 | competitor | buyer |
| Deal-BF2A98 | competitor | buyer |
| Deal-2A292B | no decision | buyer |
| Deal-D1AABF | no decision | buyer |
| Deal-FEDBCB | timing | buyer |
| Deal-1E7DA9 | competitor | buyer |
| Deal-2BBA21 | no decision | buyer |
| Deal-286F9C | competitor | buyer |
| Deal-7FBAC6 | timing | buyer |
| Deal-369281 | competitor | buyer |
| Deal-386F6E | no decision | buyer |
| Deal-9FCD0D | competitor | buyer |
| Deal-55867E | timing | buyer |
| Deal-DAFB82 | timing | buyer |
| Deal-2FEDDB | timing | buyer |
| Deal-64B19A | competitor | buyer |
| Deal-3F86A0 | no decision | buyer |
| Deal-096750 | no decision | buyer |
| Deal-F325A5 | champion left | buyer |
| Deal-ABD14C | no decision | buyer |
| Deal-79E61A | no decision | buyer |
| Deal-8A119B | pricing | buyer |
| Deal-AE7C4E | no decision | buyer |
| Deal-DAB4F1 | no decision | buyer |
| Deal-B4B50F | no decision | buyer |
| Deal-981AD4 | product gap | Bonusly |
| Deal-DC77FE | product gap | Bonusly |
| Deal-5885B9 | no decision | buyer |

### Summary

**Category Counts**
- timing: 28
- no decision: 26
- competitor: 23
- pricing: 5
- product gap: 5
- other: 2
- champion left: 1
(Total: 90)

**Side Split**
- buyer: 86
- Bonusly: 4
- unknown: 0
(Arithmetic: 90 total - 4 Bonusly = 86 buyer)

**Tag vs Text Disagreements**
Count: 4
(Deal-13E9CF: Cost vs Priority; Deal-8E27DA: Feature Request vs No R&R; Deal-DAFB82: Budget vs Priority; Deal-5AD03E: Competitor vs Budget Access)

**Top Actionable Patterns**
1. **Deferral Cluster**: 31.1% of deals (28/90) are specifically deferred to 2027 or next year, representing a high-volume nurture opportunity.
2. **Engagement Gap**: 28.9% of deals (26/90) resulted in "no decision" due to being unresponsive (MIA), suggesting a need to review top-of-funnel lead quality.
pipeline-tiering0.80
Plain-English question: Score all 156 open deals into exactly one of LOCK / ACTION / BUILD / REVIVE / WATCH / RISKY.
Prompt — what the model saw
Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.0068 · 317s · in 26,977 / out 10,483 tok
✓ json_returned✓ tier_vocabulary✗ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
```json
{
  "tier_counts": {
    "LOCK": 29,
    "ACTION": 76,
    "BUILD": 13,
    "REVIVE": 7,
    "WATCH": 0,
    "RISKY": 12
  },
  "tier_examples": {
    "LOCK": ["Deal-25F752", "Deal-D348E1", "Deal-C26D20"],
    "ACTION": ["Deal-E53952", "Deal-5408B0", "Deal-9AAE5F"],
    "BUILD": ["Deal-66D1FC", "Deal-B23205", "Deal-012CB1"],
    "REVIVE": ["Deal-0660B4", "Deal-BA571A", "Deal-2D1F1B"],
    "WATCH": [],
    "RISKY": ["Deal-547B2B", "Deal-B7EBD1", "Deal-A2B47C"]
  },
  "risky_deals": [
    "Deal-547B2B", "Deal-B7EBD1", "Deal-A2B47C", "Deal-2465CE", "Deal-584EE5", 
    "Deal-FD9F4E", "Deal-499BF6", "Deal-A5E80A", "Deal-7BBDFA", "Deal-4A13AD", 
    "Deal-9D0060", "Deal-635B8E"
  ],
  "lock_violations": 0,
  "pipeline_shape": "The pipeline is heavily weighted toward the ACTION tier (55% of total volume), showing high general engagement but a failure to convert that activity into the verified meeting frequency required for LOCK status. There is a concerning lack of hygiene in the COMMIT category, where nearly 10% of the total pipeline is tiered RISKY due to a disconnect between forecast confidence and actual meeting evidence."
}
```
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 $0.0026 · 13s · in 17,174 / out 2,830 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": [
      "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 with manual awards",
      "tracking everything in a spreadsheet",
      "people slip through the cracks"
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "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": [
      "tie recognition to retention for our hourly workforce"
    ],
    "pain_points": [
      "regretted turnover for hourly workforce is over 30%"
    ],
    "stakeholders": [
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "budget_signal": "$25k pilot budget for this quarter",
    "timeline_signal": "decision by end of September",
    "competitor_mentioned": null,
    "next_step": "send the pilot agreement and we'll route it to legal this week",
    "objections": [
      "Integration with Workday has to be rock solid"
    ],
    "confidence": "High"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why-buys": [
      "make recognition visible across our 12 retail locations"
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition today"
    ],
    "stakeholders": [
      "Prospect (People Ops Manager)",
      "CEO"
    ],
    "budget_signal": null,
    "timeline_signal": "no rush on our side until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "schedule a call with our CEO",
    "objections": [
      "The CEO has to be sold first — she decides anything people-related"
    ],
    "confidence": "Medium"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why-buys": [
      "consolidate three separate recognition tools into one"
    ],
    "pain_points": [
      "paying for three tools and none of them talk to our HRIS"
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "under $15k annually, I can approve it without going to the board",
    "timeline_signal": "procurement cycle runs six to eight weeks minimum",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "security review took three months for our last vendor"
    ],
    "confidence": "Low"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why-buys": [
      "automate service milestones",
      "analytics on recognition equity across departments"
    ],
    "pain_points": [
      "night-shift teams feel invisible",
      "engagement scores run 20 points lower for night-shift"
    ],
    "stakeholders": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "$12k approved under our engagement line",
    "timeline_signal": "running before our January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "present to our exec team on October 2",
    "objections": [
      "exec team is skeptical after a failed rollout two years ago"
    ],
    "confidence": "Medium"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why-buys": [
      "cut the admin time on service awards"
    ],
    "pain_points": [
      "personally spend five hours a month ordering and shipping plaques"
    ],
    "stakeholders": [
      "Prospect (HR Manager)",
      "COO"
    ],
    "budget_signal": "Budget isn't the issue — time is",
    "timeline_signal": "Q1 start is realistic",
    "competitor_mentioned": "doing it internally",
    "next_step": "send the one-page overview and I'll forward it to our COO this week",
    "objections": [
      "COO usually prefers building things in-house"
    ],
    "confidence": "Medium"
  }
]
```
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 $0.0014 · 17s · in 1,607 / out 1,157 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
1. Deal: Deal-547B2B
Amount: $11,200
Why: Redlines are clean, the signing page is out, and the VP People expects to sign tomorrow.
What is left: Final signature.

2. Deal: Deal-403845
Amount: $9,000
Why: The order form is currently with the customer's finance team.
What is left: Finance approval and signature.

3. Deal: Deal-A2B47C
Amount: $6,360
Why: CRM stage is DS5/COMMIT and Slack confirms the deal is "still warm."
What is left: Completion of legal review.
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 $0.0021 · 99s · in 17,265 / out 1,518 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
**Candidate Analysis**
- Quote: "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management." | Class: real gap | Deal: Deal-EC3025 | Amount: Missing
- 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." | Class: real gap | Deal: Deal-D0D6B5 | Amount: Missing
- Quote: "I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?" | Class: plan gate | Deal: Deal-CFE7F4 | Amount: Missing
- 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." | Class: rollout/enablement issue | Deal: Deal-84DBA6 | Amount: Missing

**Product Gap Summary**
- Deal-EC3025: SCIM user provisioning
- Deal-D0D6B5: ADP Workforce Now integration
rep-performance 5 tests
stale-pipeline-by-rep0.83
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 $0.0142 · 323s · in 88,348 / out 18,286 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
Bryce Harmon:
- Deal-2D1F1B, DS1, $240,000.00, 81 days
- Deal-66D1FC, DS1, $99,000.00, 16 days
- Deal-950043, DS1, $70,000.00, 19 days
- Deal-B23205, DS1, $45,000.00, 16 days
- Deal-7BBDFA, DS3, $37,440.00, 46 days
- Deal-332637, DS2, $36,000.00, 9 days
- Deal-1BEEBF, DS1, $31,500.00, 19 days
- Deal-C5658B, DS1, $23,400.00, 16 days
- Deal-40522D, DS3, $21,000.00, 19 days
- Deal-F0EBBB, DS3, $11,400.00, 24 days
- Deal-E25A09, DS1, $6,000.00, 9 days
- Deal-C9C286, DS2, $5,502.00, 9 days
- Deal-012CB1, DS1, $1.00, 23 days
Stale Deals: 13, Total Stale Amount: $626,243.00

Cole Ingram:
- Deal-D04904, DS2, $58,529.25, 11 days
- Deal-B25F40, DS3, $40,000.00, 8 days
- Deal-813836, DS2, $32,175.00, 11 days
- Deal-1BA595, DS2, $31,750.00, 11 days
- Deal-CFE1E8, DS3, $18,000.00, 11 days
- Deal-CD47A6, DS2, $12,168.00, 11 days
- Deal-627646, DS3, $11,193.00, 11 days
- Deal-FF809F, DS2, $7,781.20, 11 days
- Deal-AF932D, DS2, $7,225.40, 11 days
- Deal-A71728, DS2, $6,947.50, 11 days
- Deal-8BC9F5, DS2, $5,616.00, 10 days
- Deal-175395, DS3, $4,779.88, 11 days
- Deal-481E24, DS3, $4,140.00, 10 days
- Deal-C7F9BF, DS2, $3,360.00, 11 days
- Deal-2F3A66, DS3, $3,334.80, 11 days
- Deal-342E96, DS2, $2,700.00, 24 days
- Deal-E568D5, DS3, $1,875.00, 11 days
- Deal-FD9F4E, DS5, $1,330.00, 10 days
Stale Deals: 18, Total Stale Amount: $252,905.03

Dana Mercer:
- Deal-44EA29, DS2, $60,000.00, 10 days
- Deal-E51FB7, DS2, $43,875.00, 12 days
- Deal-B42F46, DS1, $27,000.00, 19 days
- Deal-BA3DDC, DS3, $23,400.00, 15 days
- Deal-9DDE86, DS2, $20,000.00, 15 days
- Deal-215CCA, DS3, $18,900.00, 17 days
- Deal-5EED42, DS3, $16,250.00, 11 days
- Deal-57887A, DS2, $15,000.00, 8 days
- Deal-B7EBD1, DS5, $9,000.00, 16 days
- Deal-3974EB, DS4, $9,000.00, 8 days
- Deal-F40F04, DS2, $8,100.00, 15 days
- Deal-87DDD1, DS1, $5,000.00, 19 days
- Deal-F336B6, DS3, $4,200.00, 15 days
- Deal-0660B4, DS4, $1,920.00, 16 days
Stale Deals: 14, Total Stale Amount: $261,645.00

Alex Franklin:
- Deal-CC08D1, DS1, $24,000.00, 16 days
- Deal-E73427, DS3, $18,000.00, 10 days
- Deal-885F45, DS2, $9,300.00, 12 days
- Deal-C2FF3C, DS1, $8,316.00, 10 days
- Deal-3EED2C, DS2, $7,200.00, N/A days
- Deal-0D2F7A, DS3, $5,100.00, 12 days
- Deal-6C60D4, DS3, $4,800.00, 12 days
- Deal-13FEBD, DS2, $4,680.00, 12 days
- Deal-9D0060, DS3, $3,840.00, 12 days
- Deal-690476, DS2, $3,600.00, 18 days
- Deal-C6D97A, DS4, $3,240.00, 8 days
- Deal-EE195F, DS3, $3,120.00, 8 days
- Deal-278DEC, DS3, $2,700.00, 8 days
- Deal-635B8E, DS3, $2,600.00, 18 days
- Deal-6883F3, DS1, $2,400.00, 16 days
- Deal-4A13AD, DS3, $2,160.00, 26 days
- Deal-F67D31, DS2, $1,800.00, 8 days
- Deal-5FDCE4, DS3, $1,600.00, 12 days
- Deal-BA571A, DS4, $1,080.00, 18 days
Stale Deals: 19, Total Stale Amount: $109,536.00

Elena Sinclair:
- Deal-57FF13, DS1, $2,100.00, N/A days
Stale Deals: 1, Total Stale Amount: $2,100.00
activity-mix-vs-outcome0.80
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 $0.0025 · 1987s · in 2,077 / out 683 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
**Owner: Bryce Harmon (119337721)**
- Activities: Emails: 107, Calls: 0, Meetings: 17
- Activity Mix: Emails 86.2%, Calls 0%, Meetings 13.8%
- DS2 Entries: 1 (Deal-D73B89)
- Activities per DS2: 124.0 / 1 = 124.0

**Owner: Dana Mercer (83155923)**
- Activities: Emails: 57, Calls: 16, Meetings: 8
- Activity Mix: Emails 70.4%, Calls 19.8%, Meetings 9.8%
- DS2 Entries: 1 (Deal-57887A)
- Activities per DS2: 81.0 / 1 = 81.0

**Owner: Alex Franklin (84342457)**
- Activities: Emails: 151, Calls: 25, Meetings: 13
- Activity Mix: Emails 79.8%, Calls 13.2%, Meetings 7.0%
- DS2 Entries: 3 (Deal-403845, Deal-3EED2C, Deal-1FC049)
- Activities per DS2: 189.0 / 3 = 63.0

**Owner: Cole Ingram (83155924)**
- Activities: Emails: 67, Calls: 8, Meetings: 4
- Activity Mix: Emails 84.8%, Calls 10.1%, Meetings 5.1%
- DS2 Entries: 0
- Activities per DS2: 79.0 / 0 = N/A

**Owner: Farid Osman (716654662)**
- Activities: Emails: 22, Calls: 0, Meetings: 16
- Activity Mix: Emails 57.9%, Calls 0%, Meetings 42.1%
- DS2 Entries: 1 (Deal-499BF6)
- Activities per DS2: 38.0 / 1 = 38.0

**Owner: Elena Sinclair (701163055)**
- Activities: Emails: 3, Calls: 0, Meetings: 0
- Activity Mix: Emails 100%, Calls 0%, Meetings 0%
- DS2 Entries: 0
- Activities per DS2: 3.0 / 0 = N/A

**Efficiency Ranking**
1. Farid Osman (38.0)
2. Alex Franklin (63.0)
3. Dana Mercer (81.0)
4. Bryce Harmon (124.0)

**Summary**
- Most Efficient Rep: Farid Osman
- Highest-Volume Rep: Alex Franklin (189 total activities)
- The most efficient and highest-volume reps differ.
ae-qtd-scorecardPASS 1.00
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 $0.0104 · 1744s · in 29,199 / out 22,896 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
**QTD Scorecard: Alex Franklin (as of 2026-09-05)**

**Bookings vs Quota**
* Bookings: $150,000 (40,000 + 20,000 + 35,000 + 21,000 + 12,000 + 11,000 + 6,500 + 4,500)
* Quota: $200,000
* Attainment: 75% ($150,000 / $200,000)

**Bookings Split**
* New: $113,500 (40,000 + 35,000 + 21,000 + 11,000 + 6,500)
* Expansion: $36,500 (20,000 + 12,000 + 4,500)

**Active Pipeline**
* DS1: $237,421
* DS2: $308,660
* DS3: $473,385
* DS4: $23,574
* DS5: $45,730

**Conversion & Outcomes**
* Rolling 90-day DS2-to-won rate: 5.94% (6 won / 101 entered DS2 since 2026-06-07)
* Win Count: 8
* Loss Count: 27
* Top Loss Reason: Lost- Timing (1 year or more) (11 occurrences)

**Activity Volume (Last 30 Days)**
* Emails: 740
* Calls: 91
* Meetings: 76
* Notes: 48

**Coaching Observations**
1. **Low Conversion Efficiency:** Despite high activity (740 emails, 76 meetings), the 5.94% DS2-to-won rate is critically low, indicating a gap in qualification or closing execution.
2. **Pipeline Imbalance:** The pipeline is heavily weighted in early/mid stages (DS1-DS3), with only $45,730 in DS5. This creates significant risk for the remaining 25% of quota.
3. **Timing Qualification:** "Lost- Timing (1 year or more)" is the primary loss driver (40.7% of losses), suggesting Alex is failing to establish urgency or qualify timelines effectively during discovery.
multithreading-gap0.75
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 $0.0036 · 146s · in 18,910 / out 5,703 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
Data missing: Deal amount and Stage are not provided in the files.

**Deal-EC3025**
Amount: Data missing
Stage: Data missing
Active Contact Count: 1 (2 total - 1 former)
Personas Present: champion
Personas Missing: economic buyer, HR admin, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: CT-6827DB (economic buyer)

**Deal-92D97D**
Amount: Data missing
Stage: Data missing
Active Contact Count: 1 (2 total - 1 engaged > 60 days)
Personas Present: HR admin
Personas Missing: economic buyer, champion, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: none on file

**Deal-50D386**
Amount: Data missing
Stage: Data missing
Active Contact Count: 2 (2 total - 0 former/outdated)
Personas Present: champion, HR admin
Personas Missing: economic buyer, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: CT-A1C4B3 (economic buyer)

**Deal-D0D6B5**
Amount: Data missing
Stage: Data missing
Active Contact Count: 3 (3 total - 0 former/outdated)
Personas Present: champion
Personas Missing: economic buyer, HR admin, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: CT-1FA4DB (economic buyer)

**Deal-5BFE3B**
Amount: Data missing
Stage: Data missing
Active Contact Count: 2 (2 total - 0 former/outdated)
Personas Present: champion
Personas Missing: economic buyer, HR admin, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: none on file

**Deal-36C33F**
Amount: Data missing
Stage: Data missing
Active Contact Count: 1 (3 total - 2 former)
Personas Present: IT security
Personas Missing: economic buyer, champion, HR admin, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: CT-1DB73E (economic buyer)

**Deal-885F45**
Amount: Data missing
Stage: Data missing
Active Contact Count: 2 (2 total - 0 former/outdated)
Personas Present: economic buyer, champion
Personas Missing: HR admin, IT security, finance
Most Valuable Persona to Add: HR admin
Unengaged Contact Fit: CT-B3F25D (IT security)

**Deal-FCBE5B**
Amount: Data missing
Stage: Data missing
Active Contact Count: 1 (1 total - 0 former/outdated)
Personas Present: champion
Personas Missing: economic buyer, HR admin, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: none on file

**Deal-5408B0**
Amount: Data missing
Stage: Data missing
Active Contact Count: 2 (2 total - 0 former/outdated)
Personas Present: champion, HR admin
Personas Missing: economic buyer, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: CT-07FA76 (economic buyer)

**Deal-C6D97A**
Amount: Data missing
Stage: Data missing
Active Contact Count: 3 (3 total - 0 former/outdated)
Personas Present: champion
Personas Missing: economic buyer, HR admin, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: none on file

**Deal-F9A08A**
Amount: Data missing
Stage: Data missing
Active Contact Count: 1 (2 total - 1 engaged > 60 days)
Personas Present: champion
Personas Missing: economic buyer, HR admin, IT security, finance
Most Valuable Persona to Add: economic buyer
Unengaged Contact Fit: CT-697541 (economic buyer)
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 $0.0025 · 145s · in 19,695 / out 2,104 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

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**Lead (First 5 Minutes):** "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."

**Objection Handling:**
1. Budget locked: "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."
2. Revisit next quarter: "Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"
3. Spreadsheets/gift cards: "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:** 70% (7 agreements / 10 calls)

**Competitors Raised:** Awardco, Workhuman, Kudos

**Coaching Notes:**
1. The rep is using identical scripts for leads and objection handling across almost every call; encourage more personalization based on prospect cues.
2. The "next step" request is verbatim in every instance; test varied calls-to-action to improve conversion on the 30% of calls currently failing to lock a meeting.
reporting-analytics 5 tests
q3-weighted-forecast0.80
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 $0.0037 · 59s · in 20,745 / out 5,413 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

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COMMIT total: 44,729
(11,200 + 9,000 + 9,000 + 6,360 + 5,400 + 2,520 + 1,249)

BEST_CASE total: 201,565
(38,935 + 24,000 + 19,656 + 16,250 + 11,116 + 10,800 + 10,500 + 9,890 + 9,720 + 9,000 + 7,200 + 3,840 + 3,780 + 3,600 + 3,240 + 3,150 + 3,120 + 3,060 + 2,916 + 2,760 + 2,484 + 2,100 + 1,920 + 528)

Weighted forecast: 115,276.75
(44,729 * 1.0) + (201,565 * 0.35) = 44,729 + 70,547.75

Deal counts (Inside Q3):
COMMIT: 7
BEST_CASE: 24
PIPELINE: 24

Excluded deals (Outside Q3):
Count: 32
Total amount: 227,575
(43,875 + 18,000 + 17,000 + 13,770 + 10,800 + 9,000 + 9,000 + 7,920 + 7,690 + 7,500 + 7,200 + 5,700 + 5,400 + 5,400 + 5,400 + 5,400 + 5,160 + 4,800 + 4,400 + 4,300 + 4,000 + 3,600 + 3,600 + 3,600 + 3,300 + 2,400 + 1,800 + 1,800 + 1,680 + 1,600 + 1,400 + 1,080)

Top 5 BEST_CASE deals (Inside Q3):
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

## Data quality
Most deals have missing owner data and zero characters in why_buys_chars, suggesting poor qualification. There is a stage-category mismatch where a Discovery (DS1) deal (Deal-A5E80A) is marked as COMMIT. The dataset contains inconsistent number formatting, with one deal using decimals while others use integers.
aha-moment-2x2PASS 1.00
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 $0.0067 · 339s · in 28,409 / out 12,237 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

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**Activation Hypothesis Test Results**

**2x2 Cohort Analysis**
*   **Both Signals** (m1_users >= 5 AND m1_redemptions >= 1): 30 active / 46 total = 65.2%
*   **Givers-Only** (m1_users >= 5 AND m1_redemptions < 1): 22 active / 48 total = 45.8%
*   **Redemption-Only** (m1_users < 5 AND m1_redemptions >= 1): 9 active / 29 total = 31.0%
*   **Neither** (m1_users < 5 AND m1_redemptions < 1): 38 active / 92 total = 41.3%

**Exclusions**
None; all companies in the provided extract were included in the denominator.

**Largest Retention Lift**
The single signal with the largest retention lift is **m1_users >= 5** (Givers), providing a +4.5 percentage point lift over the "Neither" cohort (45.8% vs 41.3%).

**Analysis**
*   **What this proves:** There is a strong positive correlation between achieving both activation signals in the first month and 24-month retention.
*   **What this does not prove:** Causality. The data does not prove that driving these specific behaviors *causes* retention; these signals may instead be proxies for high-intent users or a strong product-market fit.
arr-rollforward-reconciliation0.60
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 $0.0021 · 37s · in 2,125 / out 3,220 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

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**ARR Reconciliation Report (2026-09-05)**

**1. Totals & Variance**
*   **CRM Total ARR:** $566,788.44
*   **Billing Total ARR:** $511,512.12
    *   *Arithmetic:* (1436.77 + 591.00 + 1312.34 + 2370.77 + 1932.00 + 2233.00 + 521.00 + 1515.00 + 601.00 + 856.77 + 251.34 + 1988.00 + 851.00 + 1804.34 + 1003.00 + 600.77 + 272.77 + 1127.99 + 1920.34 + 1777.00 + 1602.00 + 2463.00 + 649.00 + 777.77 + 748.99 + 2150.99 + 1906.34 + 204.77 + 1809.77 + 1099.00 + 779.00 + 1692.00 + 2202.00 + 2149.00 + 1753.00 + 1440.77 + 2001.34) * 12 = 42626.01 * 12 = 511,512.12
*   **Variance:** $55,276.32

**2. Variance Decomposition**
*   **Status Mismatch:** $12,758.48
    *   C-0C8323BF: 4905.24 (CRM) - 0 (Billing Cancelled) = 4905.24
    *   C-0DC4FB8C: 8253.24 (CRM) - 0 (Billing Cancelled) = 8253.24
*   **Missing Records:** $16,497.24
    *   C-0D5BBE3A: 16497.24 (CRM) - 0 (Not in Billing) = 16497.24
*   **Rounding:** $0.00
*   **Other:** $26,020.60
    *   (Detailed in mismatched accounts list below)
*   **Total:** $55,276.32

**3. Mismatched Accounts**
| Company Alias | CRM ARR | Billing ARR | Delta | Suggested Owner |
| :--- | :--- | :--- | :--- | :--- |
| C-0BC46AD9 | 17,241.24 | 17,241.24 | 0.00 | N/A |
| C-1794A52C | 7,092.00 | 7,092.00 | 0.00 | N/A |
| C-0F4910FE | 15,748.08 | 15,748.08 | 0.00 | Account Manager |
| C-0D66DF9E | 23,200.00 | 23,184.00 | 16.00 | Billing Ops |
| C-0F7269D7 | 24,396.00 | 26,796.00 | (2,400.00) | Billing Ops |
| C-0FA7E799 | 6,252.00 | 6,252.00 | 0.00 | N/A |
| C-14D70CE0 | 18,200.00 | 18,180.00 | 20.00 | Billing Ops |
| C-0BD1AF2B | 7,212.00 | 7,212.00 | 0.00 | N/A |
| C-0BEAC6F3 | 10,281.24 | 10,281.24 | 0.00 | N/A |
| C-0DDFCE99 | 3,016.08 | 3,016.08 | 0.00 | N/A |
| C-0DB48281 | 23,856.00 | 23,856.00 | 0.00 | N/A |
| C-0F5E249E | 10,212.00 | 10,212.00 | 0.00 | N/A |
| C-0C8323BF | 4,905.24 | 0.00 | 4,905.24 | Account Manager |
| C-0DC4FB8C | 8,253.24 | 0.00 | 8,253.24 | Account Manager |
| C-0DA98AC7 | 21,652.08 | 21,652.08 | 0.00 | N/A |
| C-0DB03492 | 12,036.00 | 12,036.00 | 0.00 | N/A |
| C-0B511454 | 7,209.24 | 7,209.24 | 0.00 | N/A |
| C-0FAE9868 | 3,273.24 | 3,273.24 | 0.00 | N/A |
| C-0ACCDBC3 | 13,535.88 | 13,535.88 | 0.00 | N/A |
| C-0FB9D5AF | 23,044.08 | 23,044.08 | 0.00 | N/A |
| C-0B344485 | 21,324.00 | 21,324.00 | 0.00 | N/A |
| C-0F5D2323 | 19,224.00 | 19,224.00 | 0.00 | N/A |
| C-0B7D2C30 | 29,556.00 | 29,556.00 | 0.00 | N/A |
| C-22170CA1 | 7,788.00 | 7,788.00 | 0.00 | N/A |
| C-0FC4DBB8 | 9,333.24 | 9,333.24 | 0.00 | N/A |
| C-0D2AB865 | 8,987.88 | 8,987.88 | 0.00 | N/A |
| C-0EC6999D | 25,811.88 | 25,811.88 | 0.00 | N/A |
| C-0BC34584 | 22,876.08 | 22,876.08 | 0.00 | N/A |
| C-0BBC4E7A | 2,457.24 | 2,457.24 | 0.00 | N/A |
| C-0B20DB64 | 21,717.24 | 21,717.24 | 0.00 | N/A |
| C-0BCDB8C2 | 13,188.00 | 13,188.00 | 0.00 | N/A |
| C-0CB2C1B4 | 9,348.00 | 9,348.00 | 0.00 | N/A |
| C-0F9F8F13 | 20,304.00 | 20,304.00 | 0.00 | N/A |
| C-0FD551AB | 26,424.00 | 26,424.00 | 0.00 | N/A |
| C-0D5BBE3A | 16,497.24 | 0.00 | 16,497.24 | Billing Ops |
| C-0B144C78 | 25,788.00 | 25,788.00 | 0.00 | N/A |
| C-0BBE3E60 | 21,036.00 | 21,036.00 | 0.00 | N/A |
| C-0B7A7546 | 17,289.24 | 17,289.24 | 0.00 | N/A |
| C-0B369871 | 24,016.08 | 24,016.08 | 0.00 | N/A |

*Note: "Other" variance calculation = $55,276.32 - $12,758.48 - $16,497.24 = $26,020.60.*

**4. Business Rule Violations**
*   **C-1794A52C** (SUB-0002): term_months=24, cf_agreement_end_date is empty.
*   **C-22170CA1** (SUB-0019): term_months=36, cf_agreement_end_date is empty.
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 $0.0031 · 60s · in 3,372 / out 5,845 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
The largest relative move is Pulse check engagement, driven by the `enterprise` size_band, where values dropped from an average of ~0.55 in July to ~0.27 in August.

Arithmetic:
- Giving Rate: Aug (17.9521/30 = 0.5984) vs July (17.9447/30 = 0.5982)
- Redemptions: Aug (51.6453/30 = 1.7215) vs July (51.6498/30 = 1.7217)
- 1:1 Engagement: Aug (13.3853/30 = 0.4462) vs July (13.3739/30 = 0.4458)
- Pulse Engagement: Aug (13.5606/30 = 0.4520) vs July (17.2689/30 = 0.5756)

| KVM | 2026-08 | 2026-07 | Abs Change | Rel Change | Direction |
| :--- | :--- | :--- | :--- | :--- | :--- |
| Giving rate | 0.5984 | 0.5982 | +0.0002 | +0.04% | Up |
| Redemptions per user | 1.7215 | 1.7217 | -0.0002 | -0.01% | Down |
| 1:1 meetings engagement | 0.4462 | 0.4458 | +0.0004 | +0.09% | Up |
| Pulse check engagement | 0.4520 | 0.5756 | -0.1236 | -21.47% | Down |
rewards-ytd-sectionPASS 1.00
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 $0.0171 · 138s · in 99,096 / out 23,932 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: 2026-08

Redemption Count: 378
Spend: $27,846.00
Unique Redeemers: 235
Redemptions per Redeemer: 1.61 (378 / 235)

Provider Mix (% of Spend):
- custom: 39.05% ($10,868.00 / $27,846.00)
- Tremendous: 30.54% ($8,504.00 / $27,846.00)
- Snappy: 18.81% ($5,238.00 / $27,846.00)
- TangoCard: 11.60% ($3,236.00 / $27,846.00)
Total: 100%

Top 5 Countries by Redemptions:
1. US: 244
2. CA: 24
3. AU: 21
4. GB: 17
5. NL: 17
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 $0.0028 · 3559s · in 18,233 / out 3,448 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
**Qualifying At-Risk Accounts**

| Account Alias | Amount at Stake | Play | Signal |
| :--- | :--- | :--- | :--- |
| C-0F6C0F34 | 49,707.00 | Executive Touch | champion_active=false |
| C-0B827671 | 25,365.00 | Usage Revival | usage_trend_3m=declining |
| C-0B360C78 | 35,748.00 | Commercial Concession | health_score=57 (R1) |
| C-0B0F1BAB | 5,494.00 | Executive Touch | champion_active=false |
| C-0CA21961 | 16,829.00 | Commercial Concession | health_score=58 (R1) |
| C-0E9C27D1 | 41,235.00 | Commercial Concession | health_score=39 (R1) |
| C-0CEF69FD | 32,621.00 | Executive Touch | champion_active=false |
| C-0D3278C7 | 17,602.00 | Usage Revival | usage_trend_3m=declining |

**Total Amount at Stake**
49,707.00 + 25,365.00 + 35,748.00 + 5,494.00 + 16,829.00 + 41,235.00 + 32,621.00 + 17,602.00 = 224,601.00

**At-Risk Accounts Not Qualifying**

| Account Alias | Reason |
| :--- | :--- |
| C-0BC71BDD | Fails R2: churn_save_eligible_amount=0.00 |
| C-0BA71F12 | Fails R3: renewal_date (2027-04-11) is > 120 days from 2026-09-05 |
| C-0F6694C3 | Fails R2: churn_save_eligible_amount=0.00 |
| C-0BE96399 | Fails R2: churn_save_eligible_amount=0.00 |
| C-0F876796 | Fails R3: renewal_date (2027-02-06) is > 120 days from 2026-09-05 |
| C-0FCCD2DF | Fails R2: churn_save_eligible_amount=0.00 |
| C-10A56B0F | Fails R2: churn_save_eligible_amount=0.00 |
expansion-kitPASS 1.00
Plain-English question: Build the expansion kit for the customer who just replied to our outreach.
Prompt — what the model saw
A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0019 · 1303s · in 15,528 / out 1,372 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

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**Company Alias:** C-0DDFC9A7

**Seat Coverage:** 37.5% (150 licensed seats / 400 headcount)

**Usage Health:**
- Monthly active users have grown steadily every month from 88 (March) to 126 (August).
- Current usage is at 84% of licensed capacity (126 users / 150 seats).

**Headroom:**
- Seats: 250 (400 headcount - 150 licensed seats)
- ARR: $15,000 (250 headroom seats * $60 current per-seat rate [9,000 ARR / 150 seats])

**Buyer Analysis:**
- Replied: Maria S. (Cannot buy; explicitly stated she is not the right person for purchasing decisions).
- Right Buyer: Dana R. (VP People).

**Reply Email:**
Hi Maria,

Thanks for the kind words! It's great to hear the team loves the recognition feed.

I noticed your monthly active users have grown steadily from 88 in March to 126 this past August, which is a fantastic trend.

Since Dana R. handles the budget and seat expansion, I would love to take you up on that introduction. I can share these usage numbers and the growth trend with her to help with the planning she's been asking about.

Best,

Cole
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 $0.0019 · 1558s · in 17,006 / out 951 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

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**Company:** C-0D284E42

**Complete**
- Slack integration (2026-08-12)
- Allowance set (2026-08-13)
- Admins added (2)
- First recognition given (2026-08-15 14:22)

**Not Complete**
- HRIS integration (no data)
- First redemption (no data)

**Early Engagement Signals**
- Growth in active givers: 15 (2026-09-04) - 3 (2026-08-11) = 12 additional active givers.

**Three Things to Cover**
1. HRIS integration setup (missing).
2. Strategy to drive the first redemption (missing).
3. Analyzing the growth of active givers from 3 to 15 to identify a success pattern.
renewal-risk-conflicting-dates0.75
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 $0.0041 · 1588s · in 6,868 / out 7,796 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

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90-Day Renewal Risk Brief (2026-09-08 to 2026-12-07)

C-0B7D2C30 | Dana Mercer | 65,901.00 | 2026-09-15 (Chargebee: Multi-year contract) | [FLAG: CZ 2026-09-10] | 57.56% (274/476) | Jun: 97, Jul: 94, Aug: 84 | High: Usage trending down over last 3 months.
C-0BCDB8C2 | Cole Ingram | 54,427.00 | 2026-09-18 (Chargebee: Multi-year contract) | [FLAG: CZ 2027-09-18] | 54.72% (232/424) | Jun: 127, Jul: 118, Aug: 110 | High: Monthly active users consistently declining.
C-0D2AB865 | Elena Sinclair | 38,022.00 | 2026-09-22 (Chargebee: Multi-year contract) | [FLAG: CZ 2026-09-10] | 61.42% (250/407) | Jun: 125, Jul: 117, Aug: 109 | High: 3-month usage trend is negative.
C-0BBE3E60 | Dana Mercer | 30,993.00 | 2026-09-26 (Chargebee: Multi-year contract) | [FLAG: CZ 2027-09-26] | 64.91% (74/114) | Jun: 39, Jul: 35, Aug: 33 | High: Usage declining steadily.
C-0F5D2323 | Cole Ingram | 90,647.00 | 2026-09-29 (Chargebee: Multi-year contract) | [FLAG: CZ 2026-09-10] | 28.46% (111/390) | Jun: 20, Jul: 21, Aug: 18 | Critical: Extremely low seat utilization.
C-0EC6999D | Elena Sinclair | 79,419.00 | 2026-10-03 (Agreed) | 27.68% (31/112) | Jun: 17, Jul: 16, Aug: 15 | Critical: Utilization below 30% with stagnant usage.
C-0B20DB64 | Dana Mercer | 21,770.00 | 2026-10-07 (Agreed) | 56.61% (214/378) | Jun: 294, Jul: 298, Aug: 294 | Low: Usage remains stable.
C-0BBC4E7A | Cole Ingram | 56,374.00 | 2026-10-10 (Agreed) | 67.66% (228/337) | Jun: 142, Jul: 141, Aug: 139 | Low: Steady usage patterns.
C-0FD551AB | Elena Sinclair | 48,815.00 | 2026-10-14 (Agreed) | 55.85% (210/376) | Jun: 123, Jul: 122, Aug: 126 | Low: Stable usage.
C-0F9F8F13 | Dana Mercer | 46,230.00 | 2026-10-18 (Agreed) | 56.53% (199/352) | Jun: 185, Jul: 185, Aug: 182 | Low: Stable usage.
C-0BC34584 | Cole Ingram | 16,740.00 | 2026-10-22 (Agreed) | 66.19% (327/494) | Jun: 104, Jul: 104, Aug: 106 | Low: Consistent usage.
C-0B7A7546 | Elena Sinclair | 35,062.00 | 2026-10-25 (Agreed) | 88.78% (182/205) | Jun: 64, Jul: 65, Aug: 63 | Low: Very high seat utilization.
C-0B369871 | Dana Mercer | 85,128.00 | 2026-10-29 (Agreed) | 75.12% (317/422) | Jun: 326, Jul: 330, Aug: 333 | Low: Usage trend is increasing.
C-0B144C78 | Cole Ingram | 30,899.00 | 2026-11-02 (Agreed) | 75.45% (169/224) | Jun: 101, Jul: 101, Aug: 106 | Low: Positive usage trend.
C-0FC4DBB8 | Elena Sinclair | 94,732.00 | 2026-11-05 (Agreed) | 76.72% (356/464) | Jun: 189, Jul: 191, Aug: 193 | Low: Positive usage trend.
C-0D5BBE3A | Dana Mercer | 39,740.00 | 2026-11-09 (Agreed) | 83.33% (85/102) | Jun: 88, Jul: 90, Aug: 91 | Low: High utilization and growth.
C-0FB9D5AF | Cole Ingram | 63,158.00 | 2026-11-13 (Agreed) | 72.36% (144/199) | Jun: 173, Jul: 173, Aug: 176 | Low: Stable/Up usage.
C-0B344485 | Elena Sinclair | 64,384.00 | 2026-11-16 (Agreed) | 78.05% (224/287) | Jun: 238, Jul: 240, Aug: 244 | Low: Positive usage trend.
C-0CB2C1B4 | Dana Mercer | 40,628.00 | 2026-11-20 (Agreed) | 81.61% (386/473) | Jun: 47, Jul: 48, Aug: 49 | Low: High utilization.
C-22170CA1 | Cole Ingram | 45,646.00 | 2026-11-24 (Agreed) | 85.37% (251/294) | Jun: 143, Jul: 148, Aug: 146 | Low: High utilization.

Total ARR Renewing: 1,012,665.00
(65901 + 54427 + 38022 + 30993 + 90647 + 79419 + 21770 + 56374 + 48815 + 46230 + 16740 + 35062 + 85128 + 30899 + 94732 + 39740 + 63158 + 64384 + 40628 + 45646)

Total ARR at Risk: 359,409.00
(65901 + 54427 + 38022 + 30993 + 90647 + 79419)
ticket-theme-synthesis0.80
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 $0.0064 · 1624s · in 21,698 / out 13,148 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

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### Broad Patterns
Ranked by ARR Exposure

1. HRIS Sync & Provisioning
Count: 12
Share: 15% (12/80)
Distinct Accounts: C-0B2213A9, C-0F6C0F34, C-0DDFC9A7
ARR Affected: 114,000 (36,000 + 30,000 + 48,000)
Tickets: IC-460059, IC-460062
Recommendation: Audit HRIS sync logs to identify and resolve silent provisioning failures.

2. Redemption & Gift Card Fulfillment
Count: 18
Share: 22.5% (18/80)
Distinct Accounts: C-0CEF69FD, C-0B827671, C-0FCCD2DF, C-0F876796, C-0D9CA315, C-0B0F1BAB, C-14264ABD
ARR Affected: 68,800 (8,900 + 10,700 + 9,600 + 8,700 + 9,600 + 10,300 + 11,000)
Tickets: IC-460025, IC-460024
Recommendation: Optimize checkout performance and implement transactional logic to prevent point deduction on failed orders.

3. Points Posting & Balance Errors
Count: 20
Share: 25% (20/80)
Distinct Accounts: C-0D3278C7, C-0BE96399, C-0DD0626C, C-0B2895EF, C-0D284E42, C-0BF20542, C-0D0B047C, C-0D6CC8E3, C-21FEBCBB
ARR Affected: 31,100 (3,500 + 2,700 + 2,500 + 2,900 + 3,400 + 4,500 + 4,500 + 4,200 + 2,900)
Tickets: IC-460004, IC-460016
Recommendation: Investigate async processing delays and the root cause of team-wide posting outages.

4. Slack Integration Failures
Count: 14
Share: 17.5% (14/80)
Distinct Accounts: C-0B843542, C-0BA71F12, C-10A56B0F, C-8C2E8F00
ARR Affected: 18,900 (4,400 + 3,900 + 5,400 + 5,200)
Tickets: IC-460041, IC-460046
Recommendation: Fix OAuth token persistence and resolve API errors affecting slash commands.

### Single-Account Noise
Account C-0E9C27D1 (ARR: 52,000) submitted 16 tickets (20% of total volume) exclusively regarding billing, seat count discrepancies, and renewal tier pricing. Recommendation: Escalate to Finance/Billing for manual account reconciliation.
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 · 9s · in 15,821 / out 1,777 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
1. C-11C31562: 3 matches (size_band, use_case, region)
2. C-64171065: 3 matches (industry, size_band, use_case)
3. C-A13C193D: 3 matches (industry, size_band, region)
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 $0.0040 · 26s · in 4,478 / out 8,041 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
**Paid Channel Performance**

**paid_search**
* Spend: $36,000 (6,000 * 6)
* SQMs: 39
* SQOs: 17
* Cost per SQM: $923.08 (36,000 / 39)
* Cost per SQO: $2,117.65 (36,000 / 17)
* SQM-to-SQO Rate: 43.59% (17 / 39)
* Pipeline Amount: $680,000 (17 * 40,000)
* Pipeline per Dollar: $18.89 (680,000 / 36,000)

**linkedin_ads**
* Spend: $24,000 (4,000 * 6)
* SQMs: 24
* SQOs: 8
* Cost per SQM: $1,000.00 (24,000 / 24)
* Cost per SQO: $3,000.00 (24,000 / 8)
* SQM-to-SQO Rate: 33.33% (8 / 24)
* Pipeline Amount: $96,000 (8 * 12,000)
* Pipeline per Dollar: $4.00 (96,000 / 24,000)

**paid_social**
* Spend: $18,000 (3,000 * 6)
* SQMs: 0
* Performance: Undefined

**webinars**
* Spend: $9,000 (1,500 * 6)
* SQMs: 12
* SQOs: 5
* Cost per SQM: $750.00 (9,000 / 12)
* Cost per SQO: $1,800.00 (9,000 / 5)
* SQM-to-SQO Rate: 41.67% (5 / 12)
* Pipeline Amount: $60,000 (5 * 12,000)
* Pipeline per Dollar: $6.67 (60,000 / 9,000)

**Organic Channel Performance**

**organic_search**
* Volume: 29
* SQO Rate: 34.48% (10 / 29)
* Pipeline: $90,000 (10 * 9,000)

**referral**
* Volume: 14
* SQO Rate: 42.86% (6 / 14)
* Pipeline: $48,000 (6 * 8,000)

**Data Flags**
* linkedin_ads: CT-000044 (SQO 2026-07-18 precedes SQM 2026-07-23)
* linkedin_ads: CT-000041 (SQO 2026-06-09 precedes SQM 2026-06-14)

**Recommendation**
Reallocate budget from paid_social (undefined/zero SQMs) and linkedin_ads (lowest paid ROI at $4.00 pipeline/$) to paid_search (highest ROI at $18.89 pipeline/$).

**Confidence**
Low (Small sample sizes: only 30 total SQOs across all paid channels).
competitor-battlecard-update0.80
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 $0.0018 · 3s · in 18,096 / out 606 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
# Battlecard: Rivally

- Positioning: Points-based recognition for mid-market [S02, existing_battlecard.md].
- Pricing: $7 per user/month, annual billing (Source: pricing_page, 2026-08-12) [S17]. Conflict: Previous pricing was $5 per user/month (Source: pricing_page, 2026-04-01) [S08].
- Where they win: Distributed EU teams via multi-language support [S12], EU data residency [S05, S15], quick setup/Slack integration for mid-market [S04], and fast support response times under 4 hours [S22].
- Where we win: Analytics depth [S07, S20, S25], enterprise admin tooling (SCIM provisioning, bulk recognition editing) [S10, S16, S24], and rewards catalog depth in EMEA [S14].
- Objections and Responses:
    - Objection: Rivally offers EU data residency [S05]. Response: [Missing Data].
- Recent Changes: Raised $40M Series C led by Northgate Ventures (2025-11-04) [S01]; launched 'Rivally Pulse' engagement survey add-on (2026-03-05, exited beta 2026-09-01) [S06, S23]; opened Dublin office and made EU data residency generally available (2026-07-01) [S15]; released Microsoft Teams app v2 public preview (2026-08-20) [S19].
- 12-Month Win/Loss Record (Sept 2025 - Aug 2026):
    - Total Deals: 20
    - Wins: 13
    - Losses: 7
    - Win Rate: 65% (13/20)
    - Calculation: 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) / Total (20) = 0.65.

Unverified:
- Rivally was acquired by WorkHuman in 2025 [existing_battlecard.md].
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 $0.0035 · 17s · in 16,208 / out 3,704 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 Performance**
- **New Logo Nurture**: Sent: 1,386 | Open: 35.4% (490/1,386) | Reply: 6.5% (90/1,386) | Meeting: 1.9% (27/1,386) | Weakest: Step 3
- **Expansion Nurture**: Sent: 875 | Open: 64.6% (565/875) | Reply: 6.7% (59/875) | Meeting: 1.3% (12/875) | Weakest: Step 3
- **Cold Outbound - HR Leaders**: Sent: 1,785 | Open: 30.5% (545/1,785) | Reply: 0.4% (8/1,785) | Meeting: 0% (0/1,785) | Weakest: Step 3
- **Cold Outbound - People Ops**: Sent: 1,163 | Open: 29.2% (340/1,163) | Reply: 2.4% (29/1,163) | Meeting: 0.5% (6/1,163) | Weakest: Step 3

**Analysis**
- **Tracking Error**: Expansion Nurture Step 2 (Opened 340 > Sent 300).
- **Audience Overlap**: Multiple contacts (e.g., CT-001255, CT-001103, CT-001101) are enrolled in both Cold Outbound - HR Leaders and Cold Outbound - People Ops. CT-000301 overlaps New Logo Nurture and Expansion Nurture.
- **Failure Mode (Cold Outbound - HR Leaders)**: Value proposition mismatch. The open rate is healthy (30.5%), but the reply rate is critical (0.4%), indicating the content fails to resonate upon reading.

**Required Changes**
- **New Logo Nurture**: Refresh Step 3 content to prevent engagement decay.
- **Expansion Nurture**: Refine Step 3 targeting.
- **Cold Outbound - HR Leaders**: Full rewrite of the offer/messaging.
- **Cold Outbound - People Ops**: Optimize Step 3 Call to Action (CTA).

**Priority**: Fix **Cold Outbound - HR Leaders** first due to total meeting failure (0%) and critical reply rates.
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 $0.0018 · 11s · in 399 / out 2,662 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**Weekly Marketing Goals Update (Q3-2026)**
Days Elapsed: 66 / 92 (71.74%)

**SQMs**
- QTD Actual: 230
- Target: 300
- Delta: -70 (230 - 300)
- Pace: Ahead (Actual 230 > Required 215.22 [300 * 0.7174])

**SQOs**
- QTD Actual: 84
- Target: 120
- Delta: -36 (84 - 120)
- Pace: Behind (Actual 84 < Required 86.09 [120 * 0.7174])

**DS2s**
- QTD Actual: 40
- Target: 75
- Delta: -35 (40 - 75)
- Pace: Behind (Actual 40 < Required 53.80 [75 * 0.7174])

**Closed-Lost MIA Rate**
- QTD Actual: 0.20 (5 / 25)
- Target: 0.10
- Delta: +0.10 (0.20 - 0.10)
- Pace: Behind (Actual 0.20 > Target 0.10; lower_better)

**Same-Quarter Closes**
- QTD Actual: 10
- Target: 20
- Delta: -10 (10 - 20)
- Pace: Behind (Actual 10 < Required 14.35 [20 * 0.7174])

**Active Pipeline Coverage**
- QTD Actual: 3,000,000
- Target: 4,000,000
- Delta: -1,000,000 (3,000,000 - 4,000,000)
- Pace: Ahead (Actual 3,000,000 > Required 2,869,565 [4,000,000 * 0.7174])

Data regarding weekly movement is missing; only QTD snapshots were provided.
communication 5 tests
ceo-slack-compressionPASS 1.00
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 $0.0024 · 721s · in 17,143 / out 2,379 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
The Q3 weighted forecast of 115,976.75 (44,729 COMMIT + 35% of 203,565 BEST_CASE) is unreliable because all 7 COMMIT deals lack documented why-buys. Review the 32 deals pushed to October to verify 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 $0.0019 · 6s · in 15,519 / out 1,127 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

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Subject: Following up on our demo

Hi,

I'm following up on the pricing sent on August 5th for 150 seats. During our July 28 demo, your People team expressed a strong reaction to the automated milestone awards and the points catalog.

Do you have any questions regarding the pricing, or are you ready to discuss the next steps?

Best,
Alex Franklin
funnel-digest-paragraphPASS 1.00
Plain-English question: Write the weekly GTM digest — one paragraph per team, every number traceable.
Prompt — what the model saw
Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.0012 · 8s · in 350 / out 1,036 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Marketing hit 46 SQMs against a target of 52 (46/52 = 88.5% of goal). Shoutout to the webinar channel for delivering 18 of those SQMs, and paid search efficiency remained steady at $150 per SQM.

Sales drove significant momentum with $310,000 in new pipeline created. The team converted 14 SQOs, set 9 DS2 meetings, and secured 3 same-quarter closes.

Customer Success kept the momentum high with 2 renewals saved and a Team NPS of 61. The team is focused on resolving 3 open red-flag accounts heading into next week.

Our PLG engine is humming with 412 new signups and a 31% activation rate. We're celebrating 38 companies that reached the "aha moment" of 10 recognition gives!
partner-digest-section0.67
Plain-English question: Write the partner digest so every partner in the roster appears exactly once.
Prompt — what the model saw
Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0014 · 9s · in 576 / out 1,452 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

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Apex Rewards Co: 2 deals, $275,000 ($180,000 + $95,000) [Deal-DDAAF2, Deal-2CF33E]
HRCloud Partners: 1 deal, $140,000 [Deal-F1CDA5]
CultureBridge: 2 deals, $135,000 ($60,000 + $75,000) [Deal-096E1D, Deal-067213]
WorkWell Group: 0 deals, $0
Recogniq: Quiet
KudosWave: Quiet
PeopleFirst Advisors: Quiet
TotalPerk: Quiet
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 $0.0010 · 6s · in 521 / out 257 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
Q1: Yes, customer data is encrypted at rest using AES-256. [P1]
Q2: Yes, data in transit is encrypted with TLS 1.2 or higher. [P1]
Q3: Yes, Bonusly maintains a SOC 2 Type II report renewed annually; it is available to prospects under NDA. [P2]
Q4: Customer data is retained for 90 days after the duration of the contract. [P3]
Q5: The subprocessor list is published at trust.bonusly.com; customers receive 30 days notice before a new subprocessor is added. [P4]
Q6: Yes, SAML 2.0 single sign-on is available on the Enterprise plan. [P5]
Q7: Production databases are backed up daily; backups are encrypted and retained for 35 days. [P6]
Q8: Unanswerable. Certification documentation or a compliance matrix would answer this.
Q9: Unanswerable. An SLA document or Master Service Agreement (MSA) would answer this.
Q10: Unanswerable. A HIPAA compliance statement or BAA template would answer this.
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 $0.0096 · 3810s · in 86,952 / out 5,233 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

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(1) Overlapping Triggers:
- CRITICAL | MERGE | `comms-drafter` and `email-drafter` share duplicate ALWAYS-trigger phrases: "write me an email", "draft a follow-up", "help me reply", "what should I say", "bump email", "contract nudge".

(2) Circular Delegation:
- INFO | REVIEW | No circular delegation chains identified.

(3) Dangling Targets:
- CRITICAL | REVIEW | The following delegation targets are not present in the manifest: `bonusly-brand`, `prospect-research-multithreading`.

(4) Version Conflicts:
- INFO | REVIEW | No version conflicts identified; no duplicate skill files provided.

(5) Description Length:
- INFO | REVIEW | 0 manifest descriptions exceed 1,024 characters. (Max: 1,006).

(6) Hardcoded Values:
- WARNING | UPDATE_BODY | Hardcoded IDs, dates, and person names found in:
    - `analysis-validator` (IDs: 150582536, 1175632767, 83155923, etc.; Names: Manish, Amani; Dates: May 9, 2026)
    - `closed-lost-analysis` (Dates: May 2026; Companies: Softheon, Estee Lauder, etc.)
    - `deal-strategy-coach` (IDs: 150582536, etc.; Date: April 2026)
    - `model-selection` (Dates: 2026-05-19, Feb 2025, etc.)
    - `next-to-close` (IDs: 150582536, etc.; Hubspot Org ID: 1973303)
    - `partner-digest` (IDs: 73fe98de..., 1958248479, 2286616609; Names: Amani Phipps, Kelli, etc.; Date: May 16, 2026)
    - `pipeline-intelligence-report` (IDs: 150582536, 1973303, etc.; Names: Bryce Harmon, Dana Mercer, etc.; Date: May 2026)
    - `sales-forecast` (IDs: 73fe98de..., 2232811524, etc.; Names: Alaina, Elena; Date: April 27, 2026)
    - `signalforge-feedback` (IDs: 2295136266, 2232811524, etc.)
    - `stale-pipeline-report` (IDs: 150582536, C0561C1JCPJ; Date: 2026-06-10)
    - `weekly-pipeline-report` (IDs: 1CLZeOsElVDF..., 1ENuaEcCuLjd...; Name: Ben Lavin; Dates: April 1, June 30, 2026)

(7) Manifest Drift:
- INFO | REVIEW | No manifest drift identified. 14 rows in manifest match 14 provided files.
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 $0.0017 · 10s · in 806 / out 2,299 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

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1. Action: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
   Who: Farid Osman
   Verification: Needs confirmation
   Rollback: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
   Trace: [M04]

2. Action: Clear out the dead set via console
   Who: Elena Sinclair
   Verification: Needs confirmation
   Rollback: Needs confirmation
   Trace: [M05]

3. Action: `kubectl scale deployment/reward-worker --replicas=6`
   Who: Bryce Harmon
   Verification: `bundle exec rake sidekiq:queue_depth` returns 0 [M08]
   Rollback: `kubectl scale deployment/reward-worker --replicas=3`
   Trace: [M06]

4. Action: `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
   Who: Bryce Harmon
   Verification: 40 new jobs processed cleanly in the next 3 minutes
   Rollback: `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` [M04]
   Trace: [M09]

5. Action: `kubectl scale deployment/reward-worker --replicas=3`
   Who: Bryce Harmon
   Verification: Queue stable at 0
   Rollback: `kubectl scale deployment/reward-worker --replicas=6` [M06]
   Trace: [M10]
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 $0.0012 · 6s · in 2,201 / out 522 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

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**Failure Sequence**

1. **First Error:** 2026-09-03T14:01:12Z — `reward-service` reported `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`.
2. **Cascade:**
    * **2026-09-03T14:01:20Z - 14:01:40Z:** `reward-service` reports exhausted retries for `RewardGiveJob`.
    * **2026-09-03T14:01:40Z - 14:06:47Z:** `sidekiq` reports `RewardGiveJob` failures and retries.
    * **2026-09-03T14:02:30Z:** `sidekiq` reports "Queue reward depth above 10,000".
    * **2026-09-03T14:03:05Z - 14:06:52Z:** `api-gateway` reports 502 upstream timeouts calling `reward-service`.
    * **2026-09-03T14:03:30Z - 14:06:49Z:** `web-app` reports "Give form submission failed" due to 502s from `api-gateway`.

**Service and Job Involved**
* **Service:** `reward-service` (root cause), `sidekiq` (job processing), `api-gateway` (routing), `web-app` (frontend).
* **Job:** `RewardGiveJob` (primary failure); `RecognitionDigestJob` also failed with `Redis::TimeoutError` between 14:02:36Z and 14:05:50Z (per `sidekiq_jobs.csv`).

**Datadog Query**
`service:reward-service status:error "Redis::TimeoutError"`

**What the logs do not show**
The logs do not show the root cause of the Redis connection timeout (e.g., Redis server crash, network partition, or resource exhaustion) nor the specific number of users impacted by the "Give form submission" failures.
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 $0.0015 · 59s · in 604 / out 1,972 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

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- recognition_streaks_v2: Controls StreakTracker.record(give). State: on. Targets: segment:beta_companies (42 companies). Rule: segment:beta_companies.
- points_budget_guardrails: Controls BudgetService.new(company).enforce!(giver, points). State: on. Targets: all_companies (220 companies). Rule: all_companies.
- slack_dm_nudges: Controls SlackDm.send_nudge(user). State: on. Targets: segment:region_na (87 companies). Rule: segment:region_na.
- redeem_flow_redesign: Controls RedeemV2Component vs RedeemV1Component rendering. State: off. Targets: targeted_list (12 companies). Rule: targeted_list.
- analytics_dashboard_v3: Controls AnalyticsV3.new(company). State: on. Targets: segment:tier_three (65 companies). Rule: segment:tier_three.
- ms_teams_app_v2: Controls TeamsAppV2.install(company). State: off. Targets: targeted_list (9 companies). Rule: targeted_list.
- legacy_give_modal: No code reference. State: off. Targets: segment:legacy_plan (14 companies). Rule: segment:legacy_plan.
- survey_boosters_q3: No code reference. State: on. Targets: segment:legacy_plan (7 companies). Rule: segment:legacy_plan.
- paused_offboard_cleanup: No code reference. State: off. No targets (0 companies). Rule: [missing].
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 $0.0009 · 5s · in 602 / out 121 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

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NDA-1: GREEN. Standard terms and carve-outs. "Governing law: the laws of the State of Delaware, USA."
NDA-2: YELLOW. Contains embedded non-solicit. "neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement."
NDA-3: RED. One-way obligations, non-compete, foreign governing law, and missing carve-outs. "Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business."