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openai/gpt-5.6-sol-pro

Mean 0.937 · 31/40 perfect tests · $7.97 total · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-auditPASS 1.00

? The Question

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 $0.3045 · 73s · in 65,224 / out 5,959 tok
✓ noncomp_pct✓ no_ds1_0✓ regressed_0✓ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

noncomp_pct: 81.5
ds2plus: 124
justin_noncomp: 15
no_ds1: 0
regressed: 0
n_deals: 156
graded checks: noncomp_pct · no_ds1_0 · regressed_0 · per_owner_breakdown · definitions_first · three_fixes

Model's actual answer

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Definitions
- DS2+ non-compliant: stage DS2–DS5 with why_buys under 15 characters, blank, or a placeholder. LONG(n) is compliant.
- Missing DS1: t_ds1 is blank.
- Regressed: an earlier-stage timestamp is later than a later-stage timestamp.

Results
- Overall: 101 / 124 DS2+ deals = 81.5% non-compliant.
- Bryce Harmon: 15 / 18 = 83.3%
- Dana Mercer: 18 / 22 = 81.8%
- Alex Franklin: 48 / 61 = 78.7%
- Cole Ingram: 19 / 22 = 86.4%
- Farid Osman: 1 / 1 = 100.0%
- Gavin Porter, Hugo Lindqvist, and Elena Sinclair: N/A — 0 DS2+ deals.
- No DS1 timestamp: 0 deals.
- Regressed: 0 deals.

Process fixes
1. Block advancement to DS2+ unless why_buys passes the 15-character and placeholder validation.
2. Add a required DS1-entry timestamp automation when a deal first enters DS1.
3. Add timestamp-order validation and a regression alert for any earlier-stage timestamp later than a later-stage timestamp.
owner-resolutionPASS 1.00

? The Question

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 $0.2746 · 70s · in 91,673 / out 4,703 tok
✓ resolved_count✓ unmatched_none✓ deactivated_flagged✓ jake_amount✓ justin_amount✓ no_invented

✓ Correct Answer (ground truth)

resolved: 156
jake_amount: 1054144.0
justin_amount: 624310.0
per_owner: Bryce Harmon=1054144.0; Alex Franklin=624310.0; Dana Mercer=341195.0; Cole Ingram=288161.43; Farid Osman=4134.0; Elena Sinclair=2100.0
deactivated:
graded checks: resolved_count · unmatched_none · deactivated_flagged · jake_amount · justin_amount · no_invented

Model's actual answer

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Deals resolved: 156 of 156

Unmatched owner IDs: None

Archived/deactivated owners assigned to open deals: None
- Archived owners Gavin Porter (1520255671) and Hugo Lindqvist (77260721) have no open deals in the extract.

Pipeline amount per resolved owner:
- Bryce Harmon (119337721): 35 deals = $1,054,144.00
- Alex Franklin (84342457): 67 deals = $624,310.00
- Dana Mercer (83155923): 24 deals = $341,195.00
- Cole Ingram (83155924): 22 deals = $288,161.43
- Farid Osman (716654662): 7 deals = $4,134.00
- Elena Sinclair (701163055): 1 deal = $2,100.00

Arithmetic:
35 + 67 + 24 + 22 + 7 + 1 = 156 resolved deals

$1,054,144.00 + $624,310.00 + $341,195.00 + $288,161.43 + $4,134.00 + $2,100.00 = $2,314,044.43 total pipeline.
gong-hubspot-join-integrityPASS 1.00

? The Question

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 $0.3754 · 108s · in 109,888 / out 10,653 tok
✓ orphan_count✓ dup_keys_0✓ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

orphans: 30
orphan_aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09
dup: 0
ds3_cov_pct: 29.4
graded checks: orphan_count · dup_keys_0 · ds3_coverage · sample_ids_present

Model's actual answer

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(a) Orphan Gong rows: 30

Arithmetic: 67 Gong rows − 37 deal-ID matches in deals_open = 30 orphans.

Sample orphan aliases:
- Deal-8FA85D
- Deal-8FC3F9
- Deal-3B7945
- Deal-42B265
- Deal-9CCC42

(b) Duplicate conversation keys: 0 rows

Every Gong row has calls_90d = distinct_conversation_keys.

(c) DS3-or-later call coverage: 29.4%

Arithmetic: 25 DS3+ open deals with at least one logged call ÷ 85 total DS3+ open deals × 100 = 29.4%.

gong_owner_id was not used for matching because it is a Gong user ID, not a HubSpot owner ID.
snowflake-sql-trapped-catalog0.67

? The Question

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 $0.0752 · 28s · in 25,630 / out 1,760 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✗ uses_cohort_summary✗ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

graded checks: avoids_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
-- Cannot construct the requested query from the provided catalog:
-- 1. No customer-company identifier is documented for PRODUCTION.PLG.COMPANY_COHORT_SUMMARY.
-- 2. No recognition-event table or giver identifier is documented, so unique givers cannot be calculated.
-- 3. No company identifier or event timestamp is documented for
--    PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2, so successful
--    redemptions cannot be attributed to a company or its first customer month.
-- 4. M1_USERS cannot be assumed to mean unique givers.
SELECT
    'Insufficient catalog data to produce the requested per-company metrics without inventing columns or definitions.' AS ERROR;
```
crm-completeness-enrichmentPASS 1.00

? The Question

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 $0.3139 · 108s · in 65,105 / out 15,284 tok
✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

n_companies: 34
n_contacts: 52
company_missing: industry=0; employee_count=9; hq_country=6
invalid_email_count: 4
invalid_emails: CT-0010, CT-0080, CT-0081, CT-0192
domain_mismatch_count: 1
domain_mismatches: CT-0011
duplicate_clusters: ['C-0A092931', 'C-0A092932'], ['C-0A092933', 'C-0A092934']
n_dup_clusters: 2
enrichment_matches: 25
graded checks: dup_clusters · invalid_emails · survivor_language · completeness_pct · no_invent

Model's actual answer

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CRM completeness audit

Deals

No deals extract was provided. Therefore:

- Owner completeness: unavailable
- Stage completeness: unavailable
- Amount completeness: unavailable
- Close-date completeness: unavailable
- Why-buys completeness: unavailable
- Pipeline amount at stake: unavailable

No deal-level remediation can be ranked by pipeline amount without deal records containing company/deal aliases and amounts.

Companies

34 company records.

| Field | Complete | Arithmetic | Completeness |
|---|---:|---:|---:|
| Industry | 34 | 34 ÷ 34 × 100 | 100.0% |
| Employee count | 25 | 25 ÷ 34 × 100 | 73.5% |
| HQ country | 28 | 28 ÷ 34 × 100 | 82.4% |

Enrichment-backed fills

Only these missing values have matching, populated enrichment values:

| Company | Field | Fill from enrichment |
|---|---|---|
| C-EC3025 | employee_count | 400 |
| C-96039F | employee_count | 400 |
| C-44EA29 | employee_count | 400 |
| C-D04904 | employee_count | 400 |
| C-B23205 | employee_count | 400 |
| C-60C75F | employee_count | 400 |
| C-7BBDFA | employee_count | 400 |
| C-50D386 | employee_count | 400 |

After these fills, employee-count completeness becomes:

- (25 + 8) ÷ 34 × 100 = 97.1%

Missing values that cannot be filled

- C-93C8BF: employee_count missing; no matching enrichment row.
- C-2D1F1B: hq_country missing; matching enrichment value is also blank.
- C-D73B89: hq_country missing; matching enrichment value is also blank.
- C-44EA29: hq_country missing; matching enrichment value is also blank.
- C-D04904: hq_country missing; matching enrichment value is also blank.
- C-2C60E5: hq_country missing; matching enrichment value is also blank.
- C-EE9FFB: hq_country missing; no matching enrichment row.

CRM/enrichment disagreements

Industry taxonomy disagreements:

| Company | CRM | Enrichment | Recommendation |
|---|---|---|---|
| C-66D1FC | tech | Computer Software | Use enrichment’s more specific taxonomy. |
| C-EC3025 | Technology | Computer Software | Use enrichment’s more specific taxonomy. |
| C-44EA29 | tech | Computer Software | Use enrichment’s more specific taxonomy. |
| C-92D97D | Technology | Computer Software | Use enrichment’s more specific taxonomy. |
| C-D04904 | Technology | Computer Software | Use enrichment’s more specific taxonomy. |
| C-77A95A | Technology | Computer Software | Use enrichment’s more specific taxonomy. |
| C-AA8DDA | Technology | Computer Software | Use enrichment’s more specific taxonomy. |
| C-B25F40 | Technology | Computer Software | Use enrichment’s more specific taxonomy. |
| C-60C75F | tech | Computer Software | Use enrichment’s more specific taxonomy. |
| C-425E2A | Tech | Computer Software | Use enrichment’s more specific taxonomy. |

Country representation disagreements:

| Company | CRM | Enrichment | Recommendation |
|---|---|---|---|
| C-66D1FC | US | United States | Same country; normalize to one controlled value. |
| C-950043 | US | United States | Same country; normalize to one controlled value. |
| C-EC3025 | USA | United States | Same country; normalize to one controlled value. |
| C-96039F | USA | United States | Same country; normalize to one controlled value. |
| C-77A95A | US | United States | Same country; normalize to one controlled value. |
| C-B23205 | US | United States | Same country; normalize to one controlled value. |
| C-E51FB7 | USA | United States | Same country; normalize to one controlled value. |
| C-D0662E | US | United States | Same country; normalize to one controlled value. |
| C-425E2A | USA | United States | Same country; normalize to one controlled value. |
| C-2D7423 | USA | United States | Same country; normalize to one controlled value. |

There are no employee-count disagreements where both sources contain a value.

Duplicate company clusters

No company-name field was provided, so name-variant matching cannot be performed. Two shared-domain clusters exist:

1. acme-corp.com
   - C-0A092931: Technology, 500, US
   - C-0A092932: tech, 510, USA
   - Provisional survivor: C-0A092931, because its industry value is standardized and its alias appears first.
   - Conflict requiring validation before merge: employee count 500 versus 510.
   - Normalize US/USA during consolidation.

2. globex.io
   - C-0A092933: SaaS, 200, US
   - C-0A092934: Technology, 200, US
   - Provisional survivor: C-0A092933, because “SaaS” is more specific than “Technology.”
   - Employee count and HQ country agree.

Contacts

52 contact records.

| Field | Complete | Arithmetic | Completeness |
|---|---:|---:|---:|
| Email populated | 52 | 52 ÷ 52 × 100 | 100.0% |
| Email syntactically valid | 48 | 48 ÷ 52 × 100 | 92.3% |
| Title | 39 | 39 ÷ 52 × 100 | 75.0% |
| Persona | 37 | 37 ÷ 52 × 100 | 71.2% |

Invalid emails

- CT-0010: `user0@`
- CT-0080: `user0@`
- CT-0081: `user1@`
- CT-0192: `user2@`

Domain mismatch

- CT-0011, company C-66D1FC: email `user1@other-domain.com`; expected company/contact domain `66d1fc.com`.

10 fixes with the most pipeline amount at stake

Pipeline-based ranking is impossible because no deals or amounts were provided. The following fixes are therefore unranked; pipeline amount at stake is unknown for each:

1. Provide the deals extract with deal alias, company alias, owner, stage, amount, close date, and why-buys.
2. Merge the acme-corp.com duplicate cluster after validating the 500-versus-510 employee-count conflict.
3. Merge the globex.io duplicate cluster using C-0A092933 as the provisional survivor.
4. Apply the eight enrichment-backed employee-count fills listed above.
5. Repair the four invalid emails: CT-0010, CT-0080, CT-0081, and CT-0192.
6. Resolve the domain mismatch for CT-0011 and verify whether the contact belongs to C-66D1FC.
7. Backfill the 13 missing contact titles.
8. Backfill the 15 missing contact personas.
9. Standardize industry values to a controlled taxonomy, using enrichment for the ten listed disagreements.
10. Standardize `US`, `USA`, and `United States` to one controlled HQ-country value.
deal-intelligence
closed-lost-classification0.80

? The Question

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 $0.2606 · 107s · in 54,520 / out 12,355 tok
✓ total_90✗ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

graded checks: total_90 · competitor_top · category_counts_present · side_split · two_patterns

Model's actual answer

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Classification — 90 deals

Pricing (5)
- Deal-7ED004 — buyer
- Deal-7B2236 — Bonusly
- Deal-C33D91 — buyer
- Deal-DAFB82 — buyer
- Deal-8A119B — buyer

Competitor (22)
- Deal-F7F635 — unknown
- Deal-422BA6 — Bonusly
- Deal-381C8C — unknown
- Deal-F1E8A6 — unknown
- Deal-DDAB52 — Bonusly
- Deal-ACE061 — unknown
- Deal-2D2F8D — unknown
- Deal-0F96AA — unknown
- Deal-1BCA50 — Bonusly
- Deal-7CC678 — unknown
- Deal-A2C349 — buyer
- Deal-C7156E — unknown
- Deal-8A0992 — buyer
- Deal-D0C698 — buyer
- Deal-EECC02 — unknown
- Deal-47F1A1 — buyer
- Deal-BF2A98 — buyer
- Deal-1E7DA9 — unknown
- Deal-286F9C — Bonusly
- Deal-369281 — buyer
- Deal-9FCD0D — buyer
- Deal-64B19A — buyer

No decision (31)
- Deal-AC944F — unknown
- Deal-214060 — unknown
- Deal-13E9CF — buyer
- Deal-21B045 — unknown
- Deal-ED9AE7 — buyer
- Deal-988493 — unknown
- Deal-F308CA — unknown
- Deal-70F704 — unknown
- Deal-4664E1 — unknown
- Deal-E74A73 — buyer
- Deal-D48E0B — unknown
- Deal-583ADB — unknown
- Deal-E0441F — unknown
- Deal-7CB44D — unknown
- Deal-FAC17C — buyer
- Deal-50E5D8 — buyer
- Deal-AFA56C — unknown
- Deal-413C56 — buyer
- Deal-2A292B — buyer
- Deal-D1AABF — unknown
- Deal-2BBA21 — unknown
- Deal-7FBAC6 — buyer
- Deal-386F6E — unknown
- Deal-3F86A0 — unknown
- Deal-096750 — unknown
- Deal-ABD14C — buyer
- Deal-79E61A — unknown
- Deal-AE7C4E — unknown
- Deal-DAB4F1 — unknown
- Deal-B4B50F — unknown
- Deal-5885B9 — unknown

Timing (22)
- Deal-DB0AAC — buyer
- Deal-91A056 — buyer
- Deal-29326C — buyer
- Deal-831B7B — buyer
- Deal-39E25C — buyer
- Deal-B3ABED — buyer
- Deal-B6AC09 — buyer
- Deal-E6E80A — buyer
- Deal-B038F0 — buyer
- Deal-175756 — buyer
- Deal-BB78F3 — buyer
- Deal-15DA99 — buyer
- Deal-F4AF5D — buyer
- Deal-79B7A1 — buyer
- Deal-9F176A — buyer
- Deal-5E64CE — buyer
- Deal-69CF3D — buyer
- Deal-ECBF89 — buyer
- Deal-D1A623 — buyer
- Deal-FEDBCB — buyer
- Deal-55867E — unknown
- Deal-2FEDDB — buyer

Product gap (8)
- Deal-F97C37 — Bonusly
- Deal-8E27DA — buyer
- Deal-242273 — Bonusly
- Deal-9048EB — Bonusly
- Deal-3618CC — Bonusly
- Deal-5AD03E — Bonusly
- Deal-981AD4 — Bonusly
- Deal-DC77FE — Bonusly

Champion left (1)
- Deal-F325A5 — buyer

Other (1)
- Deal-5DB9B0 — unknown

Category counts
- No decision: 31
- Timing: 22
- Competitor: 22
- Product gap: 8
- Pricing: 5
- Champion left: 1
- Other: 1
- Total: 31 + 22 + 22 + 8 + 5 + 1 + 1 = 90

Side split
- Buyer: 44
- Unknown: 34
- Bonusly: 12
- Total: 44 + 34 + 12 = 90

Clear tag/reason disagreements: 3
- Deal-70F704: tagged “Lost DM”; reason says MIA.
- Deal-3618CC: tagged “Lost DM”; reason says “Wanted Surveys.”
- Deal-5AD03E: tagged “Competitor”; reason identifies budget-access functionality rather than a competitor.

Two patterns most worth acting on
1. No decision and timing dominate: 31 + 22 = 53 of 90 losses. Repeated reasons include unresponsiveness, deprioritization, delayed approval, and plans to revisit in 2027 or later. This supports tighter qualification, documented approval paths, and structured recycling dates.
2. Competitive selection and product gaps account for 22 + 8 = 30 losses. Specific differentiators include broader offerings, ADP integration, surveys, customization, internal-points support, localization, and budget-access functionality. These should drive competitive enablement and product-gap prioritization.
pipeline-tieringPASS 1.00

? The Question

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 $0.3169 · 50s · in 111,091 / out 3,892 tok
✓ json_returned✓ tier_vocabulary✓ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

total: 156
graded checks: json_returned · tier_vocabulary · tier_counts_sum · risky_deals_listed · no_lock_violation_claimed

Model's actual answer

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{"tier_counts":{"LOCK":3,"ACTION":15,"BUILD":54,"REVIVE":19,"WATCH":27,"RISKY":38},"tier_examples":{"LOCK":["Deal-D348E1","Deal-C26D20","Deal-403845"],"ACTION":["Deal-25F752","Deal-944310","Deal-C6FE92"],"BUILD":["Deal-3974EB","Deal-6787C2","Deal-1FC049"],"REVIVE":["Deal-2D1F1B","Deal-66D1FC","Deal-950043"],"WATCH":["Deal-C9C286","Deal-332637","Deal-E25A09"],"RISKY":["Deal-E53952","Deal-5408B0","Deal-9AAE5F"]},"risky_deals":["Deal-E53952","Deal-5408B0","Deal-9AAE5F","Deal-547B2B","Deal-B7EBD1","Deal-A2B47C","Deal-2465CE","Deal-C61CF7","Deal-62D607","Deal-584EE5","Deal-C6D97A","Deal-7B3B0F","Deal-A5E80A","Deal-F9A08A","Deal-0660B4","Deal-FD9F4E","Deal-499BF6","Deal-BA571A","Deal-FC22A3","Deal-7BBDFA","Deal-60C2C2","Deal-4A13AD","Deal-8AD4A5","Deal-15D24F","Deal-9D0060","Deal-690476","Deal-635B8E","Deal-ED725A","Deal-55164C","Deal-3BA5EA","Deal-5FDCE4","Deal-F336B6","Deal-215CCA","Deal-5EED42","Deal-BA3DDC","Deal-7599B8","Deal-F9A3C1","Deal-FA32A0"],"lock_violations":0,"pipeline_shape":"156 total deals = 3 LOCK + 15 ACTION + 54 BUILD + 19 REVIVE + 27 WATCH + 38 RISKY. The pipeline is concentrated in BUILD and RISKY, while only 3 deals qualify as LOCK; 101 of 156 deals have zero meetings_30d, limiting near-term confidence."}
call-transcript-extractionPASS 1.00

? The Question

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 $0.1142 · 30s · in 30,183 / out 4,718 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

rep: Alex Franklin
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
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
allowed_new: TX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar
graded checks: json_emitted · budget_signal · null_discipline · stakeholders · competitor_prospect_only

Model's actual answer

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[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "why-buys": [
      "The big win for us would be automating anniversary and birthday awards — our HR team of three cannot keep up with it manually."
    ],
    "pain_points": [
      "Our HR team of three cannot keep up with it manually.",
      "Right now we track everything in a spreadsheet, and people slip through the cracks."
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "We have about $40k earmarked for engagement tools this fiscal year.",
    "timeline_signal": "Ideally we would have this live before open enrollment in November.",
    "competitor_mentioned": "Achievers",
    "next_step": "Security review on September 12.",
    "objections": [
      "We looked at Achievers last year, but it was too heavy for a team our size.",
      "One concern: we need SSO and audit logs for IT to sign off."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why-buys": [
      "We want to tie recognition to retention for our hourly workforce — regretted turnover there is over 30%."
    ],
    "pain_points": [
      "Regretted turnover for our hourly workforce is over 30%."
    ],
    "stakeholders": [
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "budget_signal": "Finance has approved a $25k pilot budget for this quarter.",
    "timeline_signal": "We want a decision by end of September.",
    "competitor_mentioned": null,
    "next_step": "Send the pilot agreement; the prospect will route it to legal this week.",
    "objections": [
      "Integration with Workday has to be rock solid — that's my one condition."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why-buys": [
      "We need to make recognition visible across our 12 retail locations."
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition today."
    ],
    "stakeholders": [
      "Prospect (People Ops Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "Honestly there's no rush on our side until Q1.",
    "competitor_mentioned": "Bucketlist",
    "next_step": "Schedule a call with the CEO; the prospect will send two times.",
    "objections": [
      "The CEO has to be sold first — she decides anything people-related."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why-buys": [
      "We want to consolidate three separate recognition tools into one."
    ],
    "pain_points": [
      "We're paying for three tools and none of them talk to our HRIS."
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "If it's under $15k annually, I can approve it without going to the board.",
    "timeline_signal": "Our procurement cycle runs six to eight weeks minimum.",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "The security review took three months for our last vendor — that's my hesitation."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why-buys": [
      "Two things: automate service milestones, and give us analytics on recognition equity across departments."
    ],
    "pain_points": [
      "Our night-shift teams feel invisible — their engagement scores run 20 points lower."
    ],
    "stakeholders": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "We have $12k approved under our engagement line.",
    "timeline_signal": "We need this running before our January all-hands.",
    "competitor_mentioned": "Nectar",
    "next_step": "Present to the exec team on October 2.",
    "objections": [
      "We're mid-pilot with Nectar right now, so you'd need to beat that experience.",
      "Our exec team is skeptical after a failed rollout two years ago."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why-buys": [
      "I'd love to cut the admin time on service awards."
    ],
    "pain_points": [
      "I personally spend five hours a month ordering and shipping plaques."
    ],
    "stakeholders": [
      "Prospect (HR Manager)"
    ],
    "budget_signal": "Budget isn't the issue — time is.",
    "timeline_signal": "Our fiscal year resets in February, so a Q1 start is realistic.",
    "competitor_mentioned": null,
    "next_step": "Send the one-page overview; the prospect will forward it to the COO this week.",
    "objections": [
      "Our COO usually prefers building things in-house."
    ],
    "confidence": "high"
  }
]
next-to-close0.75

? The Question

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 $0.0812 · 39s · in 25,874 / out 2,299 tok
✓ exactly_three✗ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

top3: rank=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
exactly_three: True
blocked_excluded: deal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)
ranking_rule: Slack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc
graded checks: exactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

show the model's full answer
1. Deal-547B2B — $11,200  
Why close: DS5/COMMIT; redlines are clean, signing page is out, and the VP People said they would sign the next day.  
What is left: Signature only.

2. Deal-403845 — $9,000  
Why close: DS5/COMMIT; Slack says the order form is with the buyer’s finance team.  
What is left: Finance review/approval and signature. Note: CRM lists Alex Franklin as owner, while Dana Mercer described it as on her side.

3. Deal-A2B47C — $6,360  
Why close: DS5/COMMIT with a 2026-09-11 close date; Slack confirms it is still warm and progressing at a normal legal-review pace.  
What is left: Complete legal review and obtain signature.

Combined amount: $11,200 + $9,000 + $6,360 = $26,560.
product-gap-detectionPASS 1.00

? The Question

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 $0.0732 · 16s · in 25,222 / out 1,649 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

real_gaps: transcript_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
plan_gates: transcript_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?
enablement_issues: transcript_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_voice: transcript_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
allowed_new: TG-001, TG-002, TG-003, TG-004, TG-005, SCIM user provisioning, ADP Workforce Now integration, Custom report builder
graded checks: real_gaps · plan_gate_classified · enablement_classified · gap_summary

Model's actual answer

show the model's full answer
Candidate classifications

1. Deal-EC3025 — Amount: missing from provided data  
Prospect: “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.”  
Classification: Real gap  
Basis: Product docs state SCIM user provisioning is not currently listed as supported.

2. Deal-D0D6B5 — Amount: missing from provided data  
Prospect: “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.”  
Classification: Real gap  
Basis: Product docs state ADP Workforce Now integration is not currently listed as supported.

3. Deal-CFE7F4 — Amount: missing from provided data  
Prospect: “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?”  
Classification: Plan gate  
Basis: The custom report builder is supported on Enterprise only.

4. Deal-84DBA6 — Amount: missing from provided data  
Prospect: “We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.”  
Classification: Rollout/enablement issue  
Basis: Slack integration is supported on all plans; the stated issue is lack of manager training.

Real-gap summary

- Deal-EC3025: SCIM user provisioning — amount missing.
- Deal-D0D6B5: ADP Workforce Now integration — amount missing.

Arithmetic: 4 prospect-raised candidates − 1 plan gate − 1 rollout/enablement issue = 2 real gaps.

Excluded: The native-mobile-app statement for Deal-36C33F came from Alex Franklin, not the prospect, so it does not count as prospect voice.
rep-performance
stale-pipeline-by-rep0.67

? The Question

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 $0.3937 · 109s · in 96,942 / out 15,985 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✓ kevin_stale_amt✗ jenn_stale_amt✗ stale_count

✓ Correct Answer (ground truth)

stale_count: 65
jake_amt: 626243.0
kevin_amt: 252905.03
jenn_amt: 261645.0
top_alias: Deal-2D1F1B
top_amount: 240000.0
graded checks: stale_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
Stale = most recent logged email/call/meeting is more than 7 days before 2026-09-05. Future meeting dates are excluded because they were not logged contacts as of the snapshot.

Bryce Harmon
- Deal-2D1F1B | DS1 | $240,000 | 81 days
- Deal-66D1FC | DS1 | $99,000 | 16 days
- Deal-950043 | DS1 | $70,000 | 19 days
- Deal-B23205 | DS1 | $45,000 | 16 days
- Deal-7BBDFA | DS3 | $37,440 | 46 days
- Deal-332637 | DS2 | $36,000 | 9 days
- Deal-1BEEBF | DS1 | $31,500 | 19 days
- Deal-A414F6 | DS1 | $25,200 | 19 days
- Deal-C5658B | DS1 | $23,400 | 16 days
- Deal-40522D | DS3 | $21,000 | 19 days
- Deal-C1FA6D | DS1 | $18,000 | 16 days
- Deal-01E193 | DS1 | $12,600 | 8 days
- Deal-F0EBBB | DS3 | $11,400 | 24 days
- Deal-927338 | DS1 | $10,920 | 18 days
- Deal-E25A09 | DS1 | $6,000 | 9 days
- Deal-C9C286 | DS2 | $5,502 | 9 days
- Deal-012CB1 | DS1 | $1 | 23 days
- Deal-3795AD | DS2 | $1 | 8 days

Owner total: 18 stale deals  
$240,000 + $99,000 + $70,000 + $45,000 + $37,440 + $36,000 + $31,500 + $25,200 + $23,400 + $21,000 + $18,000 + $12,600 + $11,400 + $10,920 + $6,000 + $5,502 + $1 + $1 = $692,964

Dana Mercer
- Deal-44EA29 | DS2 | $60,000 | 10 days
- Deal-E51FB7 | DS2 | $43,875 | 12 days
- Deal-B42F46 | DS1 | $27,000 | 19 days
- Deal-BA3DDC | DS3 | $23,400 | 15 days
- Deal-9DDE86 | DS2 | $20,000 | 15 days
- Deal-215CCA | DS3 | $18,900 | 17 days
- Deal-5EED42 | DS3 | $16,250 | 11 days
- Deal-57887A | DS2 | $15,000 | 8 days
- Deal-944310 | DS4 | $10,500 | 33 days
- Deal-B7EBD1 | DS5 | $9,000 | 16 days
- Deal-3974EB | DS4 | $9,000 | 8 days
- Deal-F40F04 | DS2 | $8,100 | 15 days
- Deal-7599B8 | DS3 | $7,350 | 18 days
- Deal-87DDD1 | DS1 | $5,000 | 19 days
- Deal-F336B6 | DS3 | $4,200 | 15 days
- Deal-0660B4 | DS4 | $1,920 | 16 days

Owner total: 16 stale deals  
$60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $16,250 + $15,000 + $10,500 + $9,000 + $9,000 + $8,100 + $7,350 + $5,000 + $4,200 + $1,920 = $279,495

Alex Franklin
- Deal-E73427 | DS3 | $18,000 | 10 days
- Deal-885F45 | DS2 | $9,300 | 12 days
- Deal-C2FF3C | DS1 | $8,316 | 10 days
- Deal-0D2F7A | DS3 | $5,100 | 12 days
- Deal-6C60D4 | DS3 | $4,800 | 12 days
- Deal-13FEBD | DS2 | $4,680 | 12 days
- Deal-819506 | DS1 | $4,400 | 8 days
- Deal-9D0060 | DS3 | $3,840 | 12 days
- Deal-690476 | DS2 | $3,600 | 18 days
- Deal-C6D97A | DS4 | $3,240 | 8 days
- Deal-EE195F | DS3 | $3,120 | 8 days
- Deal-278DEC | DS3 | $2,700 | 8 days
- Deal-635B8E | DS3 | $2,600 | 18 days
- Deal-6883F3 | DS1 | $2,400 | 16 days
- Deal-4A13AD | DS3 | $2,160 | 26 days
- Deal-F67D31 | DS2 | $1,800 | 8 days
- Deal-5FDCE4 | DS3 | $1,600 | 12 days
- Deal-BA571A | DS4 | $1,080 | 18 days

Owner total: 18 stale deals  
$18,000 + $9,300 + $8,316 + $5,100 + $4,800 + $4,680 + $4,400 + $3,840 + $3,600 + $3,240 + $3,120 + $2,700 + $2,600 + $2,400 + $2,160 + $1,800 + $1,600 + $1,080 = $82,736

Cole Ingram
- Deal-D04904 | DS2 | $58,529.25 | 11 days
- Deal-B25F40 | DS3 | $40,000 | 8 days
- Deal-813836 | DS2 | $32,175 | 11 days
- Deal-1BA595 | DS2 | $31,750 | 11 days
- Deal-CFE1E8 | DS3 | $18,000 | 11 days
- Deal-CD47A6 | DS2 | $12,168 | 11 days
- Deal-627646 | DS3 | $11,193 | 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 | 10 days
- Deal-175395 | DS3 | $4,779.88 | 11 days
- Deal-481E24 | DS3 | $4,140 | 10 days
- Deal-C7F9BF | DS2 | $3,360 | 11 days
- Deal-2F3A66 | DS3 | $3,334.80 | 11 days
- Deal-342E96 | DS2 | $2,700 | 24 days
- Deal-E568D5 | DS3 | $1,875 | 11 days
- Deal-FD9F4E | DS5 | $1,330 | 10 days

Owner total: 18 stale deals  
$58,529.25 + $40,000 + $32,175 + $31,750 + $18,000 + $12,168 + $11,193 + $7,781.20 + $7,225.40 + $6,947.50 + $5,616 + $4,779.88 + $4,140 + $3,360 + $3,334.80 + $2,700 + $1,875 + $1,330 = $252,905.03

Farid Osman
- Deal-8BA24E | DS1 | $2,880 | 8 days
- Deal-8FDCD2 | DS1 | $1 | 15 days

Owner total: 2 stale deals  
$2,880 + $1 = $2,881

Missing engagement data
- Deal-3EED2C | Alex Franklin | DS2 | $7,200
- Deal-57FF13 | Elena Sinclair | DS1 | $2,100

These two deals have no row in `engagements_by_deal_90d.csv`, so days since last contact and stale status cannot be determined from the provided data.
activity-mix-vs-outcomePASS 1.00

? The Question

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 $0.4543 · 118s · in 139,264 / out 9,727 tok
✓ justin_ds2✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

justin_ds2: 4
graded checks: justin_ds2 · justin_volume_high · efficient_named · mix_present · no_causal_claim

Model's actual answer

show the model's full answer
Window: 2026-08-06 through 2026-09-05, inclusive.

| Rank | Owner | Emails | Calls | Meetings | Activity mix | DS2 entries | Activities per DS2 |
|---:|---|---:|---:|---:|---|---:|---:|
| 1 | Alex Franklin | 307 | 36 | 41 | 79.9% / 9.4% / 10.7% | 18 | 384 ÷ 18 = **21.33** |
| 2 | Bryce Harmon | 162 | 0 | 43 | 79.0% / 0.0% / 21.0% | 4 | 205 ÷ 4 = **51.25** |
| 3 | Cole Ingram | 96 | 14 | 1 | 86.5% / 12.6% / 0.9% | 2 | 111 ÷ 2 = **55.50** |
| 4 | Farid Osman | 38 | 0 | 34 | 52.8% / 0.0% / 47.2% | 1 | 72 ÷ 1 = **72.00** |
| 5 | Dana Mercer | 84 | 18 | 11 | 74.3% / 15.9% / 9.7% | 1 | 113 ÷ 1 = **113.00** |
| — | Elena Sinclair | Not computable | Not computable | Not computable | Not computable | 0 | Undefined |
| — | Gavin Porter (archived) | 0 | 0 | 0 | Undefined | 0 | Undefined |
| — | Hugo Lindqvist (archived) | 0 | 0 | 0 | Undefined | 0 | Undefined |

Arithmetic examples:
- Alex Franklin: 307 + 36 + 41 = 384; email share = 307 ÷ 384 = 79.9%.
- Bryce Harmon: 162 + 0 + 43 = 205; meeting share = 43 ÷ 205 = 21.0%.
- Cole Ingram: 96 + 14 + 1 = 111.
- Farid Osman: 38 + 0 + 34 = 72.
- Dana Mercer: 84 + 18 + 11 = 113.

Most efficient: **Alex Franklin**, at 21.33 recorded activities per DS2 entry.

Highest volume: **Alex Franklin**, with 384 recorded activities.

They do not differ.

Data limitation: `Deal-3EED2C` for Alex Franklin and `Deal-57FF13` for Elena Sinclair have no matching engagement records. Therefore, Alex Franklin’s activity total and efficiency ratio use only recorded engagement data and may be incomplete; Elena Sinclair’s activity totals and mix cannot be computed.
ae-qtd-scorecardPASS 1.00

? The Question

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 $0.3386 · 84s · in 96,731 / out 8,717 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

owner: Alex Franklin
quarter: 2026-Q3
snapshot_date: 2026-09-05
bookings: 150000
quota: 200000
attainment_pct: 75.0
new_bookings: 113500
new_count: 5
expansion_bookings: 36500
expansion_count: 3
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}}
open_deals_count: 125
ds2_to_won: window_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286
wins_qtd: 8
losses_qtd: 27
top_loss_reason: reason=Lost- Timing (1 year or more); count=13
activity_30d_totals: emails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50
prior_quarter_excluded: deal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000
allowed_new: 18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2
graded checks: bookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

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

Bookings vs. quota
- QTD bookings: $150,000
- Quota: $200,000
- Attainment: $150,000 ÷ $200,000 = 75.0%
- Remaining gap: $200,000 − $150,000 = $50,000
- 8 closed-won deals included.
- Deal-B3E6F1 ($24,000; closed 2026-06-20) excluded because it predates Q3.

Bookings mix
- New: 5 wins totaling $113,500
  - $113,500 ÷ $150,000 = 75.7% of bookings
- Expansion: 3 wins totaling $36,500
  - $36,500 ÷ $150,000 = 24.3% of bookings
- Check: $113,500 + $36,500 = $150,000

Active pipeline
- DS1: 20 deals | $284,621
- DS2: 28 deals | $353,760
- DS3: 67 deals | $552,705
- DS4: 5 deals | $23,574
- DS5: 5 deals | $45,730
- Total: 125 deals | $1,260,390

Rolling 90-day DS2-to-won rate
- Window: 2026-06-08 through 2026-09-05
- Deals entering DS2: 111
- Won: 8
- Rate: 8 ÷ 111 = 7.2%
- Of this cohort, 27 were lost and 76 remained open as of the snapshot.

QTD outcomes
- Wins: 8
- Losses: 27
- Top loss reason: “Lost- Timing (1 year or more)” — 13 losses
- Share of losses: 13 ÷ 27 = 48.1%

Last-30-day activity
- Emails: 807
- Calls: 112
- Meetings: 128
- Notes: 50
- Total activities: 807 + 112 + 128 + 50 = 1,097

Coaching observations
1. Alex is at 75.0% attainment with a $50,000 gap. DS4 and DS5 contain $69,304 combined ($23,574 + $45,730), equal to 138.6% of the remaining quota gap, so late-stage execution is the clearest path to quota.
2. The rolling DS2-to-won rate is 7.2%, while 76 of 111 DS2 entrants remain open. Prioritize qualification and progression within this cohort rather than relying on the large $1.26M headline pipeline.
3. Timing caused 48.1% of QTD losses. Tightening timing qualification and establishing compelling events earlier could address the largest identifiable loss pattern.
multithreading-gapPASS 1.00

? The Question

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 $0.1798 · 70s · in 23,834 / out 6,787 tok
✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

active_window_start: 2026-07-07
persona_universe: economic buyer, champion, HR admin, IT security, finance
flag_rule: flag 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
best_persona_rule: DS2/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
flagged: deal_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_flagged: Deal-4B0BEB, Deal-84DBA6, Deal-D348E1
allowed_new: 59 items — e.g. C-077A0E, C-0D15DF, C-1BDFE0, C-2AE3AA
graded checks: flagged_recall · persona_language · single_thread_term · on_file_fit

Model's actual answer

show the model's full answer
Cannot definitively identify “every open deal” because open/closed status, amount, stage, and the analysis date are not provided. Therefore, stage-specific persona recommendations cannot be determined.

Using 2026-09-13 as the reference date, the 60-day cutoff is:

2026-09-13 − 60 days = 2026-07-15

Assuming every listed deal is open, 11 deals are flagged:

| Deal | Classification | Amount | Stage | Active contacts | Personas present | Personas missing | Most valuable persona to add | On-file unengaged fit |
|---|---|---:|---|---:|---|---|---|---|
| Deal-EC3025 | Single-threaded | Not provided | Not provided | 1 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | CT-6827DB — Chief People Officer — economic buyer |
| Deal-92D97D | Single-threaded | Not provided | Not provided | 1 | HR admin | economic buyer, champion, IT security, finance | Cannot determine without stage | None on file |
| Deal-50D386 | Under-threaded | Not provided | Not provided | 2 | champion, HR admin | economic buyer, IT security, finance | Cannot determine without stage | CT-A1C4B3 — Chief People Officer — economic buyer |
| Deal-D0D6B5 | Under-threaded: all 3 contacts share one persona | Not provided | Not provided | 3 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | CT-1FA4DB — Chief People Officer — economic buyer |
| Deal-5BFE3B | Under-threaded: all contacts share one persona | Not provided | Not provided | 2 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | None on file |
| Deal-36C33F | Single-threaded | Not provided | Not provided | 1 | IT security | economic buyer, champion, HR admin, finance | Cannot determine without stage | CT-1DB73E — Chief People Officer — economic buyer |
| Deal-885F45 | Under-threaded | Not provided | Not provided | 2 | economic buyer, champion | HR admin, IT security, finance | Cannot determine without stage | CT-B3F25D — IT Security Lead — IT security |
| Deal-FCBE5B | Single-threaded | Not provided | Not provided | 1 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | None on file |
| Deal-5408B0 | Under-threaded | Not provided | Not provided | 2 | champion, HR admin | economic buyer, IT security, finance | Cannot determine without stage | CT-07FA76 — Chief People Officer — economic buyer |
| Deal-C6D97A | Under-threaded: all 3 contacts share one persona | Not provided | Not provided | 3 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | None on file |
| Deal-F9A08A | Single-threaded | Not provided | Not provided | 1 | champion | economic buyer, HR admin, IT security, finance | Cannot determine without stage | CT-697541 — Chief People Officer — economic buyer |

Count arithmetic:

- Single-threaded: 5
- Under-threaded: 6
- Total flagged: 5 + 6 = 11

Deal-F9A08A’s economic buyer engagement dated 2026-06-20 is older than the assumed cutoff, so that contact is not active. Former contacts were excluded regardless of engagement date.
talk-track-from-transcriptsPASS 1.00

? The Question

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 $0.1604 · 67s · in 42,993 / out 5,652 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

rep: Alex Franklin
n_transcripts: 10
opener_theme: theme=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
objections: objection=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
objection_count: 3
next_step: agreed=7; total=10; rate=0.7
competitors_prospect_raised: Awardco, Kudos
competitor_quotes: Awardco=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.
rep_raised_competitors_excluded: Workhuman
allowed_new: 13 items — e.g. TT-001, TT-002, TT-003, TT-004
graded checks: objection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer
What they lead with in the first five minutes

- 8/10 calls: a quantified retailer success story focused on turnover reduction and automated milestone awards: Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-EDC141, Deal-D9A12F, and Deal-84DBA6.
  “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.”
- 1/10: security and pricing agenda in Deal-403845.
- 1/10: direct pricing in Deal-1E2498.
- Deal-C61CF7 also included an unsolicited Workhuman comparison at minute 2.

Three most common objections and handling

1. Budget locked — 4/10 calls: Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6.
   - Alex reframes the purchase around turnover savings and finance-approved avoided-backfill economics.
   - “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. Timing/bandwidth; revisit next quarter — 3/10 calls: Deal-5408B0, Deal-C61CF7, Deal-D9A12F.
   - Alex reduces scope to a 90-day, single-department pilot that can generate evidence before planning.
   - “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?”

3. Existing spreadsheet and gift-card process — 3/10 calls: Deal-403845, Deal-EDC141, Deal-1E2498.
   - Alex contrasts the status quo with automated milestones and recognition analytics.
   - “Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized.”

Concrete next-step rate

- Agreed: 7 calls — Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, Deal-1E2498.
- Not agreed: 3 calls — Deal-403845, Deal-EDC141, Deal-84DBA6.
- Arithmetic: 7 agreed ÷ 10 calls × 100 = 70%.
- “Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager.”

Competitors raised by prospects

- Awardco — Deal-547B2B: “We're also in late talks with Awardco — their rewards catalog looks bigger than yours.”
- Kudos — Deal-EDC141: “How are you different from Kudos? Our CEO used them at her last company.”

Workhuman was raised by Alex, not a prospect.

Coaching notes

1. Preserve the concrete next-step ask, but use it consistently: all seven calls containing that ask secured a meeting, while Deal-403845, Deal-EDC141, and Deal-84DBA6 ended without one.
2. Add discovery before repeating the retailer story or standard rebuttal; 8/10 openings and all responses to each common objection use nearly identical language, limiting personalization to the prospect’s specific business case.
reporting-analytics
q3-weighted-forecastPASS 1.00

? The Question

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 $0.2028 · 84s · in 45,027 / out 8,805 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)

graded checks: 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

Model's actual answer

show the model's full answer
# Q3 2026 forecast

- COMMIT: $44,729 across 7 deals  
  Arithmetic: $11,200 + $9,000 + $9,000 + $6,360 + $5,400 + $2,520 + $1,249 = $44,729
- BEST_CASE: $203,565 across 24 deals
- PIPELINE: 23 deals; weighted contribution = $0
- Weighted forecast: $44,729 + (35% × $203,565) = $44,729 + $71,247.75 = **$115,976.75**

## Top 5 BEST_CASE deals

1. Deal-2D7423 — $38,935
2. Deal-25F752 — $24,000
3. Deal-E53952 — $19,656
4. Deal-5EED42 — $16,250
5. Deal-FA32A0 — $11,116

## Excluded for being outside Q3

32 deals totaling **$227,575**, all closing between 2026-10-01 and 2026-10-15.

Arithmetic: $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 + $5,160 + $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 + $5,400 = $227,575.

Excluded deals: Deal-E51FB7, Deal-B936FE, Deal-D9A12F, Deal-D348E1, Deal-4062CF, Deal-293AF3, Deal-034D49, Deal-E0ADD8, Deal-9F2E43, Deal-FCBE5B, Deal-712010, Deal-6691E0, Deal-C61CF7, Deal-600CD9, Deal-A92065, Deal-1D532E, Deal-48B656, Deal-E531A6, Deal-D1E6C2, Deal-D9E112, Deal-5AD94B, Deal-901332, Deal-47AE31, Deal-15D24F, Deal-766C74, Deal-ED725A, Deal-8AD4A5, Deal-D7E999, Deal-ED13B0, Deal-5FDCE4, Deal-7FA0C3, and Deal-F5A622.

## Data quality

Owner is missing for 85 of 86 deals, preventing reliable owner-level accountability. Most deals have `why_buys_chars` equal to zero, including many COMMIT and BEST_CASE deals, so forecast categories lack documented justification. Stage and forecast category are inconsistent in cases such as DS1 COMMIT Deal-A5E80A and DS5 BEST_CASE Deal-C61CF7. The 2026-09-05 extract also contains open deals with close dates before the extraction date, indicating potentially stale records.
aha-moment-2x2PASS 1.00

? The Question

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 $0.2914 · 53s · in 96,916 / out 4,225 tok
✓ both_66✓ givers_47✓ redemp_31✓ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

graded checks: both_66 · givers_47 · redemp_31 · neither_40 · excluded_0 · largest_lift_givers

Model's actual answer

show the model's full answer
| Activation cohort | Retained / cohort | 24-month retention |
|---|---:|---:|
| Both signals | 31 / 47 | 31 ÷ 47 = **66.0%** |
| Givers-only | 23 / 49 | 23 ÷ 49 = **46.9%** |
| Redemption-only | 9 / 29 | 9 ÷ 29 = **31.0%** |
| Neither | 38 / 95 | 38 ÷ 95 = **40.0%** |

**Excluded:** 0 companies. All 220 companies had the required `m1_users`, `m1_redemptions`, and `current_status` values. Only `active` was counted as retained; `cancelled` and `non_renewing` were counted as not retained.

**Largest single-signal lift:** 5+ unique givers. Givers-only retention exceeded neither by **46.9% − 40.0% = 6.9 percentage points**. Redemption-only produced **31.0% − 40.0% = −9.0 points**.

**Conclusion:** The extract supports the activation hypothesis descriptively: companies with both signals had the highest retention, exceeding givers-only by **19.0 points**, redemption-only by **34.9 points**, and neither by **26.0 points**.

This proves an association within this cohort. It does **not** prove that these activation behaviors caused retention; no controls, randomization, significance testing, or adjustment for confounding factors were provided.
arr-rollforward-reconciliationPASS 1.00

? The Question

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 $0.1986 · 56s · in 42,589 / out 9,001 tok
✓ billing_total✓ crm_total✓ variance✓ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

as_of: 2026-09-05
billing_arr_total: 604739.28
crm_arr_total: 603581.76
variance: 1157.52
variance_definition: billing_arr_total - crm_arr_total (billing ARR = sum of MRR x 12 over status=active subscriptions)
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
bucket_sum_equals_variance: True
n_mismatched_accounts: 7
rule: term_months != 12 requires cf_agreement_end_date
violations: subscription_id=SUB-0002; company_alias=C-1794A52C; term_months=24 | subscription_id=SUB-0019; company_alias=C-22170CA1; term_months=36
n_companies: 40
graded checks: billing_total · crm_total · variance · buckets_sum · violations_listed

Model's actual answer

show the model's full answer
Reconciliation basis: active billing subscriptions only; cancelled subscriptions contribute $0 billing ARR. Variance = CRM ARR − billing ARR.

Totals
- CRM ARR: $603,581.76
- Billing ARR: $604,739.28
- Variance: $603,581.76 − $604,739.28 = −$1,157.52

Variance decomposition
- Status mismatch: +$13,158.48
  - C-0C8323BF: +$4,905.24
  - C-0DC4FB8C: +$8,253.24
- Rounding: +$36.00
  - C-0D66DF9E: +$16.00
  - C-14D70CE0: +$20.00
- Missing records: −$11,952.00
  - C-21629AA4 missing from CRM: −$28,449.24
  - C-0D5BBE3A missing from billing: +$16,497.24
- Other: −$2,400.00
  - C-0F7269D7: −$2,400.00

Check:
$13,158.48 + $36.00 − $11,952.00 − $2,400.00 = −$1,157.52

Mismatched accounts

| Company alias | CRM ARR | Billing ARR | Variance | Bucket | Suggested owner |
|---|---:|---:|---:|---|---|
| C-0C8323BF | $4,905.24 | $0.00 | +$4,905.24 | Status mismatch: billing cancelled | Revenue Operations |
| C-0DC4FB8C | $8,253.24 | $0.00 | +$8,253.24 | Status mismatch: billing cancelled | Revenue Operations |
| C-0D66DF9E | $23,200.00 | $23,184.00 | +$16.00 | Rounding | Revenue Operations |
| C-14D70CE0 | $18,200.00 | $18,180.00 | +$20.00 | Rounding | Revenue Operations |
| C-21629AA4 | Missing | $28,449.24 | −$28,449.24 | Missing CRM record | Revenue Operations |
| C-0D5BBE3A | $16,497.24 | Missing | +$16,497.24 | Missing billing record | Billing Operations |
| C-0F7269D7 | $24,396.00 | $26,796.00 | −$2,400.00 | Other ARR discrepancy | Revenue Operations |

No named account owners were provided; suggested owners are functional teams.

Agreement-end-date violations
- SUB-0002 / C-1794A52C: 24-month term; `cf_agreement_end_date` missing.
- SUB-0019 / C-22170CA1: 36-month term; `cf_agreement_end_date` missing.
kvm-month-over-monthPASS 1.00

? The Question

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 $0.1723 · 58s · in 43,941 / out 6,653 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

aggregation: simple mean of company-level values per month
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
biggest_mover: pulse_engagement
driving_segment: size_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13
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,
n_company_rows_per_month: 30
graded checks: four_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer
| KVM | 2026-08 value | 2026-07 prior | Absolute change | Relative change | Direction |
|---|---:|---:|---:|---:|---|
| Giving rate | 60.2713% | 60.2297% | +0.0417 pp | +0.0692% | Up |
| Redemptions per user | 1.730163 | 1.729983 | +0.000180 | +0.0104% | Up |
| 1:1 meetings engagement | 44.7177% | 44.6887% | +0.0290 pp | +0.0649% | Up |
| Pulse check engagement | 50.8610% | 60.0587% | −9.1977 pp | −15.3145% | Down |

Arithmetic: each monthly value is the unweighted mean across 30 companies. Absolute change = August − July. Relative change = absolute change ÷ July × 100.

Pulse check engagement had the largest relative move: −15.3145%. The **enterprise** `size_band` drove it, falling from 54.9980% to 27.4280%: −27.5700 pp, or `(−27.5700 ÷ 54.9980) × 100 = −50.1291%`. SMB declined 0.2247%; mid_market increased 0.2108%. All records are `tier_three`, so plan-tier comparison is unsupported.
rewards-ytd-section0.50

? The Question

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 $0.2943 · 48s · in 104,283 / out 3,603 tok
✗ count✗ spend✗ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

redemption_count: 378
total_spend_usd: 27846.00
unique_redeemers: 235
redemptions_per_redeemer: 1.61
provider_mix_pct_of_spend: TangoCard=11.6; Tremendous=30.54; custom=39.05; Snappy=18.81
top5_countries_by_count: country=US; count=244 | country=CA; count=24 | country=AU; count=21 | country=GB; count=17 | country=NL; count=17
graded checks: count · spend · unique_redeemers · month_named · provider_mix · top_country

Model's actual answer

show the model's full answer
Redemption — YTD through 2026-08

Last completed month: August 2026 (2026-08)

- Redemption count: 1,188
- Spend: $90,463.00
- Unique redeemers: 335
- Redemptions per redeemer: 1,188 ÷ 335 = 3.55

Provider mix by spend:
- custom: $36,163 ÷ $90,463 = 39.97%
- Tremendous: $26,270 ÷ $90,463 = 29.04%
- Snappy: $16,055 ÷ $90,463 = 17.75%
- TangoCard: $11,975 ÷ $90,463 = 13.24%
- Total: 100.00% (custom adjusted by 0.01 percentage point for rounding)

Top 5 countries by redemptions:
1. US: 754
2. CA: 85
3. AU: 58
4. NL: 53
5. GB: 51
customer-success
churn-save-eligibilityPASS 1.00

? The Question

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 $0.1877 · 74s · in 24,699 / out 8,129 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
rules: health_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot
eligible: account_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)
total_amount_at_stake: 224601.00
non_eligible_at_risk: account_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)
n_accounts: 30
graded checks: eligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

show the model's full answer
Eligibility requires all three rules: health score <60, eligible amount >$0, and renewal within 120 days of 2026-09-05.

Qualified accounts

| Account | Amount at stake | Best-fit play | Justifying signal |
|---|---:|---|---|
| C-0F6C0F34 | $49,707.00 | Executive touch | Champion inactive; usage is growing and utilization is 308/395 = 78.0%, so usage revival is not indicated. |
| C-0B827671 | $25,365.00 | Usage revival | Usage declining; utilization is 113/202 = 55.9%. |
| C-0B360C78 | $35,748.00 | Commercial concession | Health score is 57 despite growing usage, an active champion, and 246/327 = 75.2% utilization. No usage or champion issue is evident. |
| C-0B0F1BAB | $5,494.00 | Executive touch | Champion inactive; renewal is only 18 days from the snapshot. |
| C-0CA21961 | $16,829.00 | Usage revival | Only 84/325 seats used = 25.8%; usage is flat. |
| C-0E9C27D1 | $41,235.00 | Commercial concession | Health score is 39 despite an active champion and 134/157 = 85.4% utilization; renewal is only 19 days away. |
| C-0CEF69FD | $32,621.00 | Executive touch | Champion inactive; usage is growing, so re-establishing executive sponsorship fits better than usage revival. |
| C-0D3278C7 | $17,602.00 | Usage revival | Usage declining; only 126/380 seats used = 33.2%. |

Total amount at stake

$49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = $224,601.00

The eligibility file does not document play-assignment rules. The plays above are best-fit classifications based only on usage trend, seat utilization, champion activity, health, and renewal timing. “Commercial concession” is used where neither usage revival nor executive touch has a supporting signal; no explicit pricing or budget signal was provided.

At risk but not qualified

| Account | Why it does not qualify |
|---|---|
| C-0BC71BDD | Health score 55 and renewal within 52 days, but eligible amount is $0. |
| C-0BA71F12 | Health score 52 and eligible amount $6,824, but renewal is 218 days away—outside the 120-day window. |
| C-0F6694C3 | Health score 43, but eligible amount is $0 and renewal is 197 days away. |
| C-0BE96399 | Health score 54 and renewal within 54 days, but eligible amount is $0. |
| C-0F876796 | Health score 47 and eligible amount $19,958, but renewal is 154 days away. |
| C-0FCCD2DF | Health score 43, but eligible amount is $0 and renewal is 230 days away. |
| C-10A56B0F | Health score 54 and renewal within 98 days, but eligible amount is $0. |
expansion-kitPASS 1.00

? The Question

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 $0.1157 · 40s · in 33,777 / out 3,265 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

company_alias: C-0DDFC9A7
coverage_pct: 37.5
headroom_seats: 250
per_seat_annual_rate: 60.00
headroom_arr: 15000.00
replier: Maria S.
replier_can_buy: False
right_buyer: name=Dana R.; title=VP People
usage_facts: active_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84.0
usage_health_lines: Monthly 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 checks: coverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

show the model's full answer
Account: C-0DDFC9A7

Seat coverage
- 150 licensed seats ÷ 400 employees = 37.5% coverage.

Usage health
- Monthly active users increased every month, from 88 in March to 126 in August: 126 − 88 = 38 users, or 38 ÷ 88 = 43.2% growth.
- August utilization was 126 ÷ 150 = 84.0% of licensed seats.

Expansion headroom
- Seats: 400 − 150 = 250 seats.
- Current rate: $9,000 ARR ÷ 150 seats = $60 per seat annually.
- Incremental ARR headroom: 250 × $60 = $15,000 ARR.

Reply and buying authority
- Maria S., People Operations Coordinator, replied.
- She explicitly said she cannot make purchasing decisions.

Right buyer
- Dana R., VP People. Maria identified Dana as responsible for budget and seat expansion.

Reply email

Subject: Re: Growing your team’s recognition program

Hi Maria,

Thanks for the context—and great to hear the team loves Bonusly. Monthly active users have grown from 88 in March to 126 in August, so I can see why Dana has been asking about the usage numbers.

An introduction would be very helpful. I’d be glad to share a concise view of current adoption and the options for expanding coverage, without creating extra work for you.

If it’s easiest, feel free to connect Dana and me on this thread, and I’ll take it from there.

Thanks again,  
Cole
onboarding-status0.75

? The Question

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 $0.1175 · 29s · in 17,592 / out 2,711 tok
✓ complete_items✓ incomplete_items✓ three_items✗ grounded

✓ Correct Answer (ground truth)

company_alias: C-0D284E42
days_live: 25
checklist: {"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a
engagement_signals: avg_active_givers_week1=4.3; avg_active_givers_last_week=13.0; latest_active_givers=15; days_with_activity=25
call_items: Connect 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.
graded checks: complete_items · incomplete_items · three_items · grounded

Model's actual answer

show the model's full answer
C-0D284E42 — Mid-onboarding call prep

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

Not complete
- HRIS integration: no completion date provided
- First redemption: no completion date provided

Early engagement signals
- Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04.
- Increase: 15 − 3 = 12 active givers.
- Percentage increase: 12 ÷ 3 × 100 = 400%.
- First seven-day average: (3 + 3 + 4 + 4 + 5 + 4 + 7) ÷ 7 = 30 ÷ 7 = 4.3 active givers.
- Latest seven-day average: (11 + 13 + 13 + 15 + 15 + 15 + 15) ÷ 7 = 91 ÷ 7 = 13.0 active givers.
- Average increase: 13.0 − 4.3 = 8.7 active givers.

Three things to cover
1. Identify what is blocking the HRIS integration and agree on an owner and next step.
2. Review the rise in active givers and determine what is driving adoption so it can be reinforced.
3. Address the absence of a first redemption and agree on how to encourage one.
renewal-risk-conflicting-datesPASS 1.00

? The Question

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 $0.2770 · 73s · in 59,657 / out 13,693 tok
✓ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
window: 2026-09-05 to 2026-12-04
trust_rule: multi-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins
accounts: 20 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=1.0; 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=78.0; 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%
disagreements: account_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
n_disagreements: 5
total_arr_renewing_90d: 1048715.00
arr_at_risk_90d: 359409.00
n_accounts: 20
graded checks: total_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

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90-Day Renewal Risk Brief

Window: 2026-09-13 through 2026-12-12. Company names were not provided, so account aliases are used exactly as given.

Method:
- Seat utilization = seats_used ÷ seats.
- 3-month trend = June → July → August active users; percentage change = (August − June) ÷ June.
- High risk: utilization below 50%.
- Medium risk: utilization of 50%–69.9% or declining usage.
- Low risk: utilization at least 70% with flat/growing usage.
- Chargebee is trusted for multi-year contracts because ChurnZero multi-year dates are known to be wrong. Where dates agree, the shared date is used.

1. C-0B7D2C30 — Dana Mercer — $65,901 ARR
   Date used: 2026-09-15 (Chargebee).
   DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-15; Chargebee is trusted because this is a 36-month multi-year contract.
   Seat utilization: 274 ÷ 476 = 57.6%.
   Usage trend: 97 → 94 → 84; change = -13 ÷ 97 = -13.4%.
   Risk: Medium — utilization is below 70% and active usage declined 13.4%.

2. C-0BCDB8C2 — Cole Ingram — $54,427 ARR
   Date used: 2026-09-18 (Chargebee).
   DISAGREEMENT: ChurnZero 2027-09-18 vs Chargebee 2026-09-18; Chargebee is trusted because this is a 36-month multi-year contract.
   Seat utilization: 232 ÷ 424 = 54.7%.
   Usage trend: 127 → 118 → 110; change = -17 ÷ 127 = -13.4%.
   Risk: Medium — utilization is below 70% and active usage declined 13.4%.

3. C-0D2AB865 — Elena Sinclair — $38,022 ARR
   Date used: 2026-09-22 (Chargebee).
   DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-22; Chargebee is trusted because this is a 24-month multi-year contract.
   Seat utilization: 250 ÷ 407 = 61.4%.
   Usage trend: 125 → 117 → 109; change = -16 ÷ 125 = -12.8%.
   Risk: Medium — utilization is below 70% and active usage declined 12.8%.

4. C-0BBE3E60 — Dana Mercer — $30,993 ARR
   Date used: 2026-09-26 (Chargebee).
   DISAGREEMENT: ChurnZero 2027-09-26 vs Chargebee 2026-09-26; Chargebee is trusted because this is a 24-month multi-year contract.
   Seat utilization: 74 ÷ 114 = 64.9%.
   Usage trend: 39 → 35 → 33; change = -6 ÷ 39 = -15.4%.
   Risk: Medium — utilization is below 70% and active usage declined 15.4%.

5. C-0F5D2323 — Cole Ingram — $90,647 ARR
   Date used: 2026-09-29 (Chargebee).
   DISAGREEMENT: ChurnZero 2026-09-10 vs Chargebee 2026-09-29; Chargebee is trusted because this is a 24-month multi-year contract.
   Seat utilization: 111 ÷ 390 = 28.5%.
   Usage trend: 20 → 21 → 18; change = -2 ÷ 20 = -10.0%.
   Risk: High — only 28.5% of seats are used, with active usage also declining 10.0%.

6. C-0EC6999D — Elena Sinclair — $79,419 ARR
   Date used: 2026-10-03 (both systems agree).
   Seat utilization: 31 ÷ 112 = 27.7%.
   Usage trend: 17 → 16 → 15; change = -2 ÷ 17 = -11.8%.
   Risk: High — only 27.7% of seats are used, with active usage declining 11.8%.

7. C-0B20DB64 — Dana Mercer — $21,770 ARR
   Date used: 2026-10-07 (both systems agree).
   Seat utilization: 214 ÷ 378 = 56.6%.
   Usage trend: 294 → 298 → 294; change = 0 ÷ 294 = 0.0%.
   Risk: Medium — usage is flat, but seat utilization is only 56.6%.

8. C-0BBC4E7A — Cole Ingram — $56,374 ARR
   Date used: 2026-10-10 (both systems agree).
   Seat utilization: 228 ÷ 337 = 67.7%.
   Usage trend: 142 → 141 → 139; change = -3 ÷ 142 = -2.1%.
   Risk: Medium — utilization is below 70% and active usage declined 2.1%.

9. C-0FD551AB — Elena Sinclair — $48,815 ARR
   Date used: 2026-10-14 (both systems agree).
   Seat utilization: 210 ÷ 376 = 55.9%.
   Usage trend: 123 → 122 → 126; change = 3 ÷ 123 = +2.4%.
   Risk: Medium — active usage grew 2.4%, but seat utilization remains only 55.9%.

10. C-0F9F8F13 — Dana Mercer — $46,230 ARR
    Date used: 2026-10-18 (both systems agree).
    Seat utilization: 199 ÷ 352 = 56.5%.
    Usage trend: 185 → 185 → 182; change = -3 ÷ 185 = -1.6%.
    Risk: Medium — utilization is 56.5% and active usage declined 1.6%.

11. C-0BC34584 — Cole Ingram — $16,740 ARR
    Date used: 2026-10-22 (both systems agree).
    Seat utilization: 327 ÷ 494 = 66.2%.
    Usage trend: 104 → 104 → 106; change = 2 ÷ 104 = +1.9%.
    Risk: Medium — active usage grew 1.9%, but seat utilization remains below 70%.

12. C-0B7A7546 — Elena Sinclair — $35,062 ARR
    Date used: 2026-10-25 (both systems agree).
    Seat utilization: 182 ÷ 205 = 88.8%.
    Usage trend: 64 → 65 → 63; change = -1 ÷ 64 = -1.6%.
    Risk: Medium — seat utilization is strong, but active usage declined 1.6%.

13. C-0B369871 — Dana Mercer — $85,128 ARR
    Date used: 2026-10-29 (both systems agree).
    Seat utilization: 317 ÷ 422 = 75.1%.
    Usage trend: 326 → 330 → 333; change = 7 ÷ 326 = +2.1%.
    Risk: Low — utilization is 75.1% and active usage grew 2.1%.

14. C-0B144C78 — Cole Ingram — $30,899 ARR
    Date used: 2026-11-02 (both systems agree).
    Seat utilization: 169 ÷ 224 = 75.4%.
    Usage trend: 101 → 101 → 106; change = 5 ÷ 101 = +5.0%.
    Risk: Low — utilization is 75.4% and active usage grew 5.0%.

15. C-0FC4DBB8 — Elena Sinclair — $94,732 ARR
    Date used: 2026-11-05 (both systems agree).
    Seat utilization: 356 ÷ 464 = 76.7%.
    Usage trend: 189 → 191 → 193; change = 4 ÷ 189 = +2.1%.
    Risk: Low — utilization is 76.7% and active usage grew 2.1%.

16. C-0D5BBE3A — Dana Mercer — $39,740 ARR
    Date used: 2026-11-09 (both systems agree).
    Seat utilization: 85 ÷ 102 = 83.3%.
    Usage trend: 88 → 90 → 91; change = 3 ÷ 88 = +3.4%.
    Risk: Low — utilization is 83.3% and active usage grew 3.4%.

17. C-0FB9D5AF — Cole Ingram — $63,158 ARR
    Date used: 2026-11-13 (both systems agree).
    Seat utilization: 144 ÷ 199 = 72.4%.
    Usage trend: 173 → 173 → 176; change = 3 ÷ 173 = +1.7%.
    Risk: Low — utilization is 72.4% and active usage grew 1.7%.

18. C-0B344485 — Elena Sinclair — $64,384 ARR
    Date used: 2026-11-16 (both systems agree).
    Seat utilization: 224 ÷ 287 = 78.0%.
    Usage trend: 238 → 240 → 244; change = 6 ÷ 238 = +2.5%.
    Risk: Low — utilization is 78.0% and active usage grew 2.5%.

19. C-0CB2C1B4 — Dana Mercer — $40,628 ARR
    Date used: 2026-11-20 (both systems agree).
    Seat utilization: 386 ÷ 473 = 81.6%.
    Usage trend: 47 → 48 → 49; change = 2 ÷ 47 = +4.3%.
    Risk: Low — utilization is 81.6% and active usage grew 4.3%.

20. C-22170CA1 — Cole Ingram — $45,646 ARR
    Date used: 2026-11-24 (both systems agree).
    Seat utilization: 251 ÷ 294 = 85.4%.
    Usage trend: 143 → 148 → 146; change = 3 ÷ 143 = +2.1%.
    Risk: Low — utilization is 85.4% and active usage increased 2.1% overall.

Totals:
- September ARR: $65,901 + $54,427 + $38,022 + $30,993 + $90,647 = $279,990.
- October ARR: $79,419 + $21,770 + $56,374 + $48,815 + $46,230 + $16,740 + $35,062 + $85,128 = $389,538.
- November ARR: $30,899 + $94,732 + $39,740 + $63,158 + $64,384 + $40,628 + $45,646 = $379,187.
- Total ARR renewing: $279,990 + $389,538 + $379,187 = $1,048,715.
- High-risk ARR: $90,647 + $79,419 = $170,066.
- Medium-risk ARR: $414,334.
- ARR at risk: $170,066 + $414,334 = $584,400.
ticket-theme-synthesis0.80

? The Question

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 $0.1810 · 44s · in 28,230 / out 5,900 tok
✗ theme_count_range✓ counts_verifiable✓ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

quarter_window: 2026-06-01 through 2026-08-31
total_tickets: 80
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": {"
ranking_by_arr_exposure: hris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync
single_account_noise_theme: billing_dispute
note: existing tags are noisy/unreliable; classify by text
sample_ticket_ids: 80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041
graded checks: theme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

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Total: 80 tickets. Share = theme tickets ÷ 80. ARR affected counts each distinct account once per theme.

Broad patterns — ranked by ARR exposure

1. HRIS provisioning/sync failures
- Count: 12
- Share: 12 ÷ 80 = 15.0%
- Distinct accounts: 3
- ARR affected: $36,000 C-0B2213A9 + $30,000 C-0F6C0F34 + $48,000 C-0DDFC9A7 = $114,000
- Example tickets: IC-460059, IC-460060
- Recommendation: Prioritize HRIS sync reliability and alert on skipped provisioning or silent log failures.

2. Redemption and gift-card fulfillment failures
- Count: 18
- Share: 18 ÷ 80 = 22.5%
- Distinct accounts: 7
- ARR affected: $8,900 C-0CEF69FD + $10,700 C-0B827671 + $9,600 C-0FCCD2DF + $8,700 C-0F876796 + $11,000 C-14264ABD + $9,600 C-0D9CA315 + $10,300 C-0B0F1BAB = $68,800
- Example tickets: IC-460025, IC-460024
- Recommendation: Make redemption processing idempotent and automatically reconcile deducted points when fulfillment fails.

3. Points posting/balance failures
- Count: 20
- Share: 20 ÷ 80 = 25.0%
- Distinct accounts: 9
- ARR affected: $3,500 C-0D3278C7 + $4,500 C-0BF20542 + $4,500 C-0D0B047C + $2,700 C-0BE96399 + $3,400 C-0D284E42 + $4,200 C-0D6CC8E3 + $2,900 C-21FEBCBB + $2,500 C-0DD0626C + $2,900 C-0B2895EF = $31,100
- Example tickets: IC-460004, IC-460016
- Recommendation: Investigate recognition-to-ledger processing and add monitoring for delivered recognitions with unposted points.

4. Slack integration failures
- Count: 14
- Share: 14 ÷ 80 = 17.5%
- Distinct accounts: 4
- ARR affected: $4,400 C-0B843542 + $5,400 C-10A56B0F + $3,900 C-0BA71F12 + $5,200 C-8C2E8F00 = $18,900
- Example tickets: IC-460041, IC-460047
- Recommendation: Fix authentication persistence, sync-toggle resets, and slash-command failures across the Slack integration.

Single-account concentration

5. Billing seat-count and tier-pricing errors
- Count: 16
- Share: 16 ÷ 80 = 20.0%
- Distinct accounts: 1
- ARR affected: $52,000 C-0E9C27D1 = $52,000
- Example tickets: IC-460071, IC-460078
- Recommendation: Audit C-0E9C27D1’s billing configuration and renewal history; do not generalize this as a portfolio-wide pattern without more accounts.
marketing
case-study-matchPASS 1.00

? The Question

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 $0.0720 · 16s · in 25,051 / out 1,569 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

top3: company_alias=C-11C31562; similarity_score=3 | company_alias=C-64171065; similarity_score=3 | company_alias=C-A13C193D; similarity_score=3
decoys_excluded: company_alias=C-D6217CAA; similarity_score=4; reason=no_case_study | company_alias=C-C153868F; similarity_score=4; reason=no_case_study
constraints: no_billing_data, no_contact_names, only_customers_with_has_case_study_true_named
prospect_fields_used: industry, size_band, use_case, region
prospect_alias: C-82AF3719
graded checks: top3 · decoys_excluded · cites_fields · no_billing

Model's actual answer

show the model's full answer
1. C-64171065 — 3/4 fields match
   - Industry: Technology ✓
   - Size band: Mid-Market ✓
   - Use case: employee_recognition ✓
   - Region: NA-East ≠ NA-West
   - Public case study: true

2. C-A13C193D — 3/4 fields match
   - Industry: Technology ✓
   - Size band: Mid-Market ✓
   - Use case: retention ≠ employee_recognition
   - Region: NA-West ✓
   - Public case study: true

3. C-11C31562 — 3/4 fields match
   - Industry: Manufacturing ≠ Technology
   - Size band: Mid-Market ✓
   - Use case: employee_recognition ✓
   - Region: NA-West ✓
   - Public case study: true

Arithmetic: each customer matches 3 of 4 similarity fields, so all three tie at 3 ÷ 4 = 75%. The data provides no weighting rule to break the tie.
channel-attribution-mathPASS 1.00

? The Question

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 $0.2326 · 82s · in 57,426 / out 8,834 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

months: 2026-03, 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
undefined_channels: paid_social
flagged_rows_sqo_before_sqm: CT-000041, CT-000044
best_pipeline_per_dollar_channel: paid_search
reallocation_recommendation: from=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.
confidence_note: webinars and referral have small SQM samples (<20); treat rates as directional.
graded checks: undefined_channel · undefined_not_zero · flagged_rows · best_channel · reallocation

Model's actual answer

show the model's full answer
Trailing 6 months: 2026-03 through 2026-08

Paid performance

| Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM→SQO | Pipeline | Pipeline/$ |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| paid_search | $36,000 | 40 | 18 | $900 | $2,000 | 45.0% | $720,000 | $20.00 |
| linkedin_ads | $24,000 | 25 | 8 | $960 | $3,000 | 32.0% | $96,000 | $4.00 |
| paid_social | $18,000 | 0 | 0 | Undefined | Undefined | Undefined | $0 | $0.00 |
| webinars | $9,000 | 12 | 5 | $750 | $1,800 | 41.7% | $60,000 | $6.67 |
| Total | $87,000 | 77 | 31 | $1,129.87 | $2,806.45 | 40.3% | $876,000 | $10.07 |

Arithmetic:
- Spend = six monthly amounts: paid_search $6,000×6=$36,000; linkedin_ads $4,000×6=$24,000; paid_social $3,000×6=$18,000; webinars $1,500×6=$9,000.
- Cost/SQM = spend ÷ SQMs.
- Cost/SQO = spend ÷ SQOs.
- SQM→SQO rate = SQOs ÷ SQMs.
- Pipeline/$ = pipeline ÷ spend.
- paid_social’s acquisition costs and conversion rate are undefined because it spent $18,000 but generated zero SQMs.

Organic performance

| Channel | Volume | SQOs | SQO rate | Pipeline |
|---|---:|---:|---:|---:|
| organic_search | 30 | 10 | 33.3% | $90,000 |
| referral | 15 | 6 | 40.0% | $48,000 |
| Total organic | 45 | 16 | 35.6% | $138,000 |

Arithmetic:
- organic_search: 10÷30=33.3%; 10×$9,000=$90,000.
- referral: 6÷15=40.0%; 6×$8,000=$48,000.
- Total: 16÷45=35.6%; $90,000+$48,000=$138,000.
- No organic spend data was provided, so organic acquisition costs and pipeline per dollar cannot be calculated.

Date-quality flags

- CT-000044: SQO date 2026-07-18 precedes SQM date 2026-07-23.
- CT-000041: SQO date 2026-06-09 precedes SQM date 2026-06-14.

Both are included as SQOs as recorded; their dates require correction.

Recommendation

Reallocate paid_social’s $18,000 away from that channel because it produced zero SQMs. Prioritize paid_search, which generated the highest pipeline per dollar ($20.00) with the largest paid sample (40 SQMs, 18 SQOs). Test a smaller increase in webinars, which had the best unit costs but only 12 SQMs and 5 SQOs. Consider reducing linkedin_ads after paid_social because its $4.00 pipeline per dollar was the weakest among paid channels producing SQMs.

Confidence: Moderate. paid_search has the strongest sample, but webinars and linkedin_ads have only 12 and 25 SQMs, respectively, and there is no evidence showing whether performance will remain constant as spend increases.
competitor-battlecard-updatePASS 1.00

? The Question

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 $0.1888 · 63s · in 25,034 / out 8,162 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

competitor: Rivally
pricing: value_per_user_per_month=7; source_snippet=S17; source_date=2026-08-12
win_loss_12mo: wins=13; losses=7
unverified_old_claims: Rivally lacks a Slack integration., Rivally was acquired by WorkHuman in 2025.
rep_opinion_snippet_ids_not_facts: S09, S21
citation_ids_available: 25 items — e.g. S01, S02, S03, S04
graded checks: competitor_named · pricing · win_loss · unverified_marked · citations

Model's actual answer

show the model's full answer
# Battlecard: Rivally

## One-line positioning

Points-based employee recognition with an engaging social feed, quick setup, and strengths for distributed EU teams; analytics and administration are comparatively limited. [S02, S04, S12, S16]

## Pricing

Current public price: Recognition Starter is $7/user/month with annual billing, effective August 12, 2026. [S17]

Source conflict:
- $5/user/month with annual billing on January 20, 2026. [S03]
- Still $5/user/month on April 1, 2026. [S08]
- A 500-seat prospect reported a $6.50/user/month annual quote on June 2, 2026. [S13]
- A prospect reported a $7/user/month list price and a 15% discount for a three-year term on August 14, 2026. [S18]

Use the newer public source: $7/user/month annually as of 2026-08-12. [S17]

## Where they win

- Engaging points-based recognition feed. [S02, S16]
- Fast implementation; one reviewer reported setup in under a week. [S04]
- Slack integration reportedly works out of the box. [S04]
- Strong fit for distributed EU teams, with praised multi-language support. [S12]
- EU data residency became generally available in July 2026. [S15]
- Support response time was praised as under four hours. [S22]

## Where we win

- Analytics depth: reviewers describe Rivally’s analytics and reporting dashboards as limited or basic, and an 800-seat prospect selected Bonusly citing analytics depth. [S02, S07, S25]
- Enterprise provisioning: an enterprise reviewer reported that Rivally lacks SCIM provisioning and requires painful manual user management. [S10]
- Administration: reviewers report lagging admin tooling and no bulk recognition editing. [S16, S24]
- Data portability: one reviewer reported difficult migration because analytics exports are CSV-only. [S20]
- EMEA rewards breadth: a reviewer described Rivally’s EMEA catalog as thinner than its US catalog. [S14]

## Objections and responses

**“Rivally has a more engaging recognition experience.”**  
Response: Reviewers do praise its recognition feed, but other reviews identify limited analytics, basic reporting, and lagging administration. [S02, S07, S16]

**“Rivally is better for European teams.”**  
Response: Rivally has EU data residency and praised multi-language support, but its EMEA rewards catalog has been described as thinner than its US catalog. [S12, S14, S15]

**“Rivally is faster to deploy.”**  
Response: One reviewer reported setup in under a week and an out-of-the-box Slack integration; evaluate whether that initial speed offsets manual user management where SCIM is required. [S04, S10]

**“Rivally is cheaper.”**  
Response: Its current public annual price is $7/user/month, up from $5/user/month earlier in 2026; one prospect reported a 15% discount tied to a three-year term. [S03, S08, S17, S18]

**“Rivally includes engagement surveys.”**  
Response: Rivally Pulse is available, but it is priced as an add-on rather than bundled. [S23]

## Recent changes

- Raised a $40M Series C led by Northgate Ventures. [S01]
- Launched the Rivally Pulse engagement-survey add-on in March 2026. [S06]
- Hired an ex-Workday VP EMEA to lead European expansion. [S11]
- Opened a Dublin office and made EU data residency generally available. [S15]
- Increased Recognition Starter’s public price from $5 to $7/user/month. [S08, S17]
- Put Microsoft Teams app v2 into public preview. [S19]
- Moved Rivally Pulse out of beta as a separately priced add-on. [S23]

## Our 12-month win/loss record against Rivally

Period: September 2025 through August 2026.

- Wins: 13
- Losses: 7
- Total: 13 + 7 = 20
- Win rate: 13 ÷ 20 × 100 = **65%**
- Loss rate: 7 ÷ 20 × 100 = **35%**

Wins: Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392.

Losses: Deal-7767F5, Deal-5645A5, Deal-C6FFAA, Deal-D263E0, Deal-935746, Deal-9066A6, Deal-72A02F.

The deal file contains no `snippet_id` field, so these win/loss claims cannot be assigned snippet citations.

## Existing-card verification

- “Points-based recognition”: re-sourced. [S02]
- “For mid-market”: **unverified**; no supplied source establishes this as Rivally’s market positioning.
- “Starts at $5/user/month”: outdated; superseded by the $7/user/month public price. [S17]
- “Rivally lacks a Slack integration”: contradicted by a review reporting that its Slack integration worked out of the box. [S04]
- “Rivally was acquired by WorkHuman in 2025”: **unverified**; no supplied source supports this claim.
- “Strong in EU enterprise with multi-language support”: re-sourced from an EU enterprise review. [S12]
nurture-sequence-diagnosisPASS 1.00

? The Question

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 $0.3524 · 66s · in 112,681 / out 6,252 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

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_error: sequence=Expansion Nurture; step=2; sent=300; opened=340; issue=opened exceeds sent
audience_overlap_pair: Cold Outbound - HR Leaders, Cold Outbound - People Ops
all_pairwise_overlaps: ['Cold Outbound - HR Leaders', 'Cold Outbound - People Ops'], ['Expansion Nurture', 'New Logo Nurture']
under_2pct_reply: Cold Outbound - HR Leaders
failure_mode: Cold 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_first: Cold Outbound - HR Leaders
graded checks: tracking_error · overlap_pair · weakest_step · fix_priority

Model's actual answer

show the model's full answer
Rates use summed step activity: total outcomes ÷ total sent.

- New Logo Nurture: sent 1,386 (500+458+428); open rate 35.35% (490÷1,386); reply rate 6.49% (90÷1,386); meeting rate 1.95% (27÷1,386). Weakest: step 3, 4.21% replies (18÷428).
- Expansion Nurture: sent 875; open rate 64.57% (565÷875), but invalid due to tracking error; reply rate 6.74% (59÷875); meeting rate 1.37% (12÷875). Weakest: step 3, 4.36% replies (12÷275).
- Cold Outbound - HR Leaders: sent 1,785; open rate 30.53% (545÷1,785); reply rate 0.45% (8÷1,785); meeting rate 0.00% (0÷1,785). Weakest: step 3, 0.17% replies (1÷590).
- Cold Outbound - People Ops: sent 1,163; open rate 29.23% (340÷1,163); reply rate 2.49% (29÷1,163); meeting rate 0.52% (6÷1,163). Weakest: step 3, 1.59% replies (6÷377).

Tracking error: Expansion Nurture step 2 reports 340 opened from 300 sent (113.33%). Audit event deduplication before trusting its open rate.

Audience overlap:
- Expansion Nurture / New Logo Nurture: CT-000301, CT-000624.
- Cold Outbound - HR Leaders / Cold Outbound - People Ops: 21 contacts—CT-000849, CT-000884, CT-000890, CT-000908, CT-001033, CT-001097, CT-001101, CT-001103, CT-001105, CT-001130, CT-001153, CT-001159, CT-001217, CT-001227, CT-001236, CT-001255, CT-001258, CT-001277, CT-001285, CT-001311, CT-001345.

Under-2% failure modes and changes:
- Cold Outbound - HR Leaders: all steps are under 2%; opens without replies indicate message/audience relevance failure. Change: rewrite step 1 around an HR-leader-specific pain and CTA.
- Cold Outbound - People Ops step 3: declining engagement indicates sequence fatigue. Change: replace step 3 with a concise breakup email.

Fix first: Cold Outbound - HR Leaders because it has the lowest overall reply rate and zero meetings.
weekly-marketing-goals-rollupPASS 1.00

? The Question

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 $0.1157 · 33s · in 33,732 / out 3,275 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

days_elapsed: 66
days_in_quarter: 92
elapsed_fraction: 0.7174
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,
coverage_ratio_active_pipeline: 0.75
pace_rule: expected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.
this_week_paragraph_required: True
graded checks: metrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer
Q3-2026: 66 of 92 days elapsed = 71.7%.

| Metric | QTD actual | Target | Delta (actual − target) | Pace |
|---|---:|---:|---:|---|
| SQMs | 230 | 300 | -70 | Ahead |
| SQOs | 84 | 120 | -36 | Behind |
| DS2s | 40 | 75 | -35 | Behind |
| Closed-lost MIA rate | 20.0% | ≤10.0% | +10.0 pp unfavorable | Behind |
| Same-quarter close count | 10 | 20 | -10 | Behind |
| Active pipeline coverage | $3,000,000 (75.0%) | $4,000,000 (100%) | -$1,000,000 / -25.0 pp | Ahead |

Arithmetic:
- Expected progress at this point: 66 ÷ 92 = 71.7%.
- SQMs: 230 ÷ 300 = 76.7%; 76.7% > 71.7% → ahead.
- SQOs: 84 ÷ 120 = 70.0%; 70.0% < 71.7% → behind.
- DS2s: 40 ÷ 75 = 53.3%; 53.3% < 71.7% → behind.
- Closed-lost MIA rate: 5 ÷ 25 = 20.0%; 20.0% > 10.0% ceiling → behind.
- Same-quarter closes: 10 ÷ 20 = 50.0%; 50.0% < 71.7% → behind.
- Pipeline coverage: $3,000,000 ÷ $4,000,000 = 75.0%; 75.0% > 71.7% → ahead.

What moved this week cannot be determined because no prior-week data was provided. Current QTD results show SQMs and active pipeline ahead of elapsed-quarter pace, while SQOs, DS2s, same-quarter closes, and the closed-lost MIA rate are behind.
communication
ceo-slack-compression0.80

? The Question

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 $0.0665 · 17s · in 24,305 / out 1,173 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✗ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

graded checks: max_3_sentences · under_80_words · weighted_present · caveat_present · no_em_dash

Model's actual answer

show the model's full answer
Plan Q3 to $115,976.75: $44,729 COMMIT + 35% × $203,565 BEST_CASE ($71,247.75), with PIPELINE at $0. This includes 54 of 86 deals: 7 COMMIT, 24 BEST_CASE, and 23 PIPELINE; 32 deals worth $227,575 close after Q3, including Deal-D348E1 at $13,770. Caveat: why-buys are blank on all 7 in-quarter COMMIT deals, so the $44,729 committed forecast lacks documented purchase rationale.
stalled-deal-followup-emailPASS 1.00

? The Question

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 $0.0573 · 11s · in 23,339 / out 450 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

deal_alias: Deal-0D2F7A
amount: 5100
stage: DS3
owner: Alex Franklin
days_since_last_engagement: 31
word_cap: 120
must_reference: engagement_id=ENG-00002; date=2026-07-28; type=meeting; why=most substantive prior touch; concrete prospect reactions to milestone awards and points catalog
single_cta: True
tone: warm, concise, professional (matches prior thread)
allowed_facts: 14 items — e.g. Deal-0D2F7A, $5,100, DS3, Alex Franklin
allowed_new: ENG-00001, ENG-00002, ENG-00003, ENG-00004, ENG-00005
graded checks: word_cap · references_touch · single_ask

Model's actual answer

show the model's full answer
Subject: Following up on 150-seat pricing

Hi there,

I’m following up on the August 5 recap and pricing for 150 seats after our July 28 demo. Is this still something your team plans to evaluate?

Best,  
Alex Franklin
funnel-digest-paragraphPASS 1.00

? The Question

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 $0.0715 · 17s · in 8,429 / out 1,054 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

required_paragraphs: Marketing, Sales, CS, PLG
allowed_numbers: 16 items — e.g. 2, 3, 9, 08
word_cap: 400
banned: customer contact names, employee performance commentary, compensation
graded checks: four_paragraphs · word_cap · teams

Model's actual answer

show the model's full answer
Marketing: SQMs reached 46 against a target of 52, a gap of 6 (52 − 46) and 88.5% attainment (46 ÷ 52 × 100). Webinars contributed 18 SQMs, or 39.1% of the total (18 ÷ 46 × 100). Paid-search cost per SQM held at $150.

Sales: The team converted 14 SQOs, set 9 DS2 meetings, and created $310,000 in new pipeline. Same-quarter closes totaled 3 for the week.

CS: The team saved 2 renewals this week, while team NPS moved to 61. Looking ahead, 3 open red-flag accounts require attention next week.

PLG: PLG welcomed 412 new signups, with activation at 31%. In addition, 38 companies reached the aha moment of 10 recognition gives.
partner-digest-sectionPASS 1.00

? The Question

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 $0.1078 · 24s · in 33,493 / out 2,522 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

roster: partner_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)
per_partner_pipeline: {"Apex Rewards Co": {"count": 2, "amount": 275000}, "HRCloud Partners": {"count": 1, "amount": 140000}, "CultureBridge": {"count": 2, "amount": 135000}}
partner_sourced_pipeline: count=5; amount=550000
quiet_partners: Recogniq, KudosWave, PeopleFirst Advisors, TotalPerk
active_no_deals: WorkWell Group
rule: every roster partner named exactly once; quiet partners get one line; no partners outside roster.
graded checks: all_partners · pipeline_amount · quiet_one_line

Model's actual answer

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

- Apex Rewards Co — Co-webinar scheduled for 09-15. 2 partner-sourced DS1 deals: Deal-DDAAF2 ($180,000) + Deal-2CF33E ($95,000) = $275,000.
- HRCloud Partners — Security review completed; referral moved to DS2. 1 partner-sourced deal: Deal-F1CDA5 = $140,000.
- CultureBridge — Lunch-and-learn generated 2 early-stage partner-sourced deals: Deal-096E1D ($60,000) + Deal-067213 ($75,000) = $135,000.
- WorkWell Group — Joint-playbook restart planned for Q4; planning call scheduled for 09-09. 0 partner-sourced deals = $0.
- Recogniq — Quiet; 0 partner-sourced deals = $0.
- KudosWave — Quiet; 0 partner-sourced deals = $0.
- PeopleFirst Advisors — Quiet; 0 partner-sourced deals = $0.
- TotalPerk — Quiet; 0 partner-sourced deals = $0.

Total partner-sourced pipeline: 2 + 1 + 2 = 5 deals; $275,000 + $140,000 + $135,000 = $550,000.
rfp-answer-from-policyPASS 1.00

? The Question

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 $0.0661 · 15s · in 24,105 / out 1,169 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

answered_count: 7
answer_map: {"Q1": ["P1"], "Q2": ["P1"], "Q3": ["P2"], "Q4": ["P3"], "Q5": ["P4"], "Q6": ["P5"], "Q7": ["P6"]}
unanswerable: Q8, Q9, Q10
valid_citation_ids: P1, P2, P3, P4, P5, P6, P7, P8
rule: unanswerable items must be flagged and the document that would answer them named; do not guess.
graded checks: answered_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 using TLS 1.2 or higher. [P1]

Q3: Yes. Bonusly maintains a SOC 2 Type II report, renewed annually. Prospects can obtain the report under NDA. [P2]

Q4: Customer data is retained for 90 days after contract termination, then deleted from production systems. [P3]

Q5: The current 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 from the provided excerpts. An ISO 27001 certificate or certification statement would answer this question. [P1–P8]

Q9: Unanswerable from the provided excerpts. The contractual Service Level Agreement or uptime policy would provide the uptime SLA percentage. [P1–P8]

Q10: Unanswerable from the provided excerpts. A HIPAA/BAA policy or standard Business Associate Agreement terms would answer this question. [P1–P8]
ops-maintenance
skill-file-reconciliationPASS 1.00

? The Question

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 $0.4296 · 50s · in 163,781 / out 7,006 tok
✓ duplicate_trigger_found✓ circular_chain✓ dangling_target✓ oversized_desc_count✓ hardcoded_values✓ manifest_drift✓ severity_and_action

✓ Correct Answer (ground truth)

graded checks: duplicate_trigger_found · circular_chain · dangling_target · oversized_desc_count · hardcoded_values · manifest_drift · severity_and_action

Model's actual answer

show the model's full answer
| Severity | Action | Finding | Proposal |
|---|---|---|---|
| CRITICAL | MERGE | `comms-drafter` and `email-drafter` duplicate triggers including `"write me an email"`, `"draft a follow-up"`, `"what should I say"`, `"bump email"`, `"contract nudge"`, and pasted-message review/rewrites. Their email guidance and output formats also substantially duplicate. | Merge email-specific signature retrieval and formatting requirements into `comms-drafter`; retain `comms-drafter` as the broader surviving skill and delete `email-drafter` after migration. |
| CRITICAL | REVIEW | `pipeline-intelligence-report` and `weekly-pipeline-report` overlap on `"pipeline update"`, `"pipeline report"`, `"pipeline review/summary"`, and `"what's/what does the pipeline look like"`. Both claim mandatory routing for general pipeline requests. | Establish mutually exclusive triggers: scored deal-level pipeline → `pipeline-intelligence-report`; weekly performance/funnel metrics → `weekly-pipeline-report`. |
| INFO | REVIEW | No circular delegation chain is demonstrated by the provided bodies. `pipeline-intelligence-report → closed-lost-analysis` is one-way. `next-to-close → pipeline-intelligence-report` is also one-way. | No change unless an omitted target skill delegates back into its caller. |
| CRITICAL | REVIEW | Dangling skill targets not represented by a manifest row or provided file: `bonusly-brand`, `prospect-research-multithreading`, `signalforge-reports`, `bonusly-data-questions`, `bonusly-product-questions`, `bonusly-business-reporting-questions`, `bonusly-rewards-questions`, `bonusly-ppp-questions`, `bonusly-feature-flag-questions`, `bonusly-deal-desk-questions`, and `bonusly-datadog-questions`. | Add the missing skills and manifest rows, or remove/redirect each invocation. Do not execute dependent delegation until resolved. |
| WARNING | UPDATE_BODY | `analysis-validator` declares v3.6 in its header, changelog, and footer, but its validation-trail template says `Validator: analysis-validator v3.2`. | Keep v3.6 as the surviving version and update the stale v3.2 reference. |
| INFO | TRIM_DESC | Manifest descriptions exceeding 1,024 characters: `0 / 14 = 0%`. Maximum supplied value is `1,006`, appearing for `pipeline-intelligence-report` and `signalforge-claim-compressor`; `1,006 ≤ 1,024`. | No trimming required. |
| WARNING | UPDATE_BODY | `analysis-validator` hardcodes dates and people: `April 26, 2026`, `May 4, 2026`, `May 9, 2026`, the May 2026 roster, `Manish`, `Amani`, and numerous named GTM employees. | Replace operational dates and personnel rosters with live references or runtime lookups; retain dates only in the changelog. |
| WARNING | UPDATE_BODY | `closed-lost-analysis` hardcodes dated examples and snapshots, including `May 2026`, `May 4–12`, and company-specific historical examples. | Move historical examples into a dated reference file and keep the executable body date-agnostic. |
| WARNING | UPDATE_BODY | `deal-strategy-coach` hardcodes Confluence page ID `2257879045`, `April 2026`, 2026 pricing, and the person name `Farid`. | Resolve the playbook and routing owner dynamically or through a maintained reference. |
| WARNING | UPDATE_BODY | `model-selection` hardcodes registry date `2026-05-19`, model-release dates, deprecation dates, and a dated model catalog. | Keep only the update procedure in the body and load the current registry dynamically. |
| WARNING | UPDATE_BODY | `partner-digest` hardcodes page/folder IDs `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, and `2238283777`; dates including `May 16–19, 2026`; and people including `Amani`, `Kelli`, `Jen Lee`, `Hani`, `Bryce`, and `Sara`. | Move destination IDs, contacts, and canonical-page mappings into maintained configuration. |
| WARNING | UPDATE_BODY | `pipeline-intelligence-report` hardcodes `May 2026` and AE names `Bryce Harmon`, `Dana Mercer`, `Cole Ingram`, `Alex Franklin`, and `Gavin Porter`. | Resolve owners dynamically and move historical version dates to the changelog. |
| WARNING | UPDATE_BODY | `sales-forecast` hardcodes Confluence IDs `2232811524` and `2232582148`, dated examples including `April 27, 2026` and `July 9, 2026`, and the person name `Alaina`. | Move publication IDs to configuration and resolve the forecast owner dynamically. |
| WARNING | UPDATE_BODY | `signalforge-claim-compressor` hardcodes changelog date `2026-05-09`. | Keep the date only if changelog history is intentionally immutable; otherwise move changelog metadata outside the executable body. |
| WARNING | UPDATE_BODY | `signalforge-feedback` hardcodes page IDs `2295136266`, `2232811524`, `2234417154`, and `2247295002`. | Move Confluence destinations into configuration and validate them at runtime. |
| WARNING | UPDATE_BODY | `stale-pipeline-report` hardcodes `Alaina`, example dates such as `5/7`, `5/15`, and `5/19`, plus changelog date `2026-06-10`. | Replace person-specific routing and dated examples with role-based/runtime placeholders; keep release dates only in the changelog. |
| WARNING | UPDATE_BODY | `weekly-pipeline-report` hardcodes `Ben Lavin`, fixed Q2 dates `April 1 – June 30, 2026`, and static Q1 2026 figures. | Make the reporting owner and quarter dynamic; move historical benchmark figures into a dated reference. |
| INFO | REVIEW | No qualifying hardcoded page IDs, literal dates, or person names were found in `comms-drafter`, `email-drafter`, or `next-to-close`. | No change. |
| INFO | REVIEW | Manifest drift, files without manifest rows: `0`. Arithmetic: 14 provided skill files − 14 matched manifest files = `0`. | No action. |
| INFO | REVIEW | Manifest drift, rows without files: `0`. Arithmetic: 14 manifest rows − 14 matched skill files = `0`. | No action. |
runbook-from-slack-thread0.75

? The Question

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 $0.1836 · 30s · in 49,858 / out 3,282 tok
✗ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

steps: order=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)
ambiguous_step: order=4; msg=M05; mark=needs-confirmation
rule: no steps beyond what the thread contains; every step traces to a message id.
graded checks: ordered_steps · rollback · needs_confirmation · sourced

Model's actual answer

show the model's full answer
| Step | Exact command or action | Who ran it | Success verification | Rollback | Trace |
|---:|---|---|---|---|---|
| 1 | Acknowledge the PagerDuty alert for reward-worker queue depth > 10k and take incident command. | Bryce Harmon | Bryce stated he was acknowledging the alert and taking IC. No separate verification was recorded. | Not provided; needs confirmation if rollback of acknowledgement or IC assignment is required. | [M01] |
| 2 | `bundle exec rake sidekiq:queue_depth` | Farid Osman | Command returned 48,213 pending reward jobs. Normal was stated as under 500. | N/A — read-only diagnostic. | [M02] |
| 3 | Inspect the dead set. Exact command not provided. | Farid Osman | Found 112 jobs, all showing `Redis::TimeoutError` from around 13:58. | N/A — read-only diagnostic. | [M03] |
| 4 | `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'` | Farid Osman | No explicit verification that the flag was disabled was recorded; needs confirmation. | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` | [M04] |
| 5 | Clear the dead set from the console. Exact command or console action not provided. | Elena Sinclair | No explicit verification was recorded; needs confirmation. | Not provided; needs confirmation. | [M05] |
| 6 | `kubectl scale deployment/reward-worker --replicas=6` — increased from 3 replicas. | Bryce Harmon | Queue subsequently reached 9,400 and was falling approximately 1,200 jobs/min. No explicit verification of the replica count was recorded; needs confirmation. | `kubectl scale deployment/reward-worker --replicas=3` | [M06], [M07] |
| 7 | Monitor the reward queue depth. Exact command not provided. | Farid Osman | Queue depth was 9,400 and falling approximately 1,200 jobs/min. | N/A — monitoring action. | [M07] |
| 8 | `bundle exec rake sidekiq:queue_depth`; check the Datadog error rate. | Cole Ingram | Queue-depth command returned 0, and the Datadog error rate was back to baseline. | N/A — verification actions. | [M08] |
| 9 | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` | Bryce Harmon | Forty new jobs processed cleanly during the next 3 minutes. | Not explicitly specified. The thread contains the disable command from Step 4, but its use as rollback here needs confirmation. | [M09], [M04] |
| 10 | `kubectl scale deployment/reward-worker --replicas=3` — reduced from 6 replicas. | Bryce Harmon | Queue remained stable at 0; incident was declared resolved. | Not explicitly specified. The thread contains the scale-to-6 command from Step 6, but its use as rollback here needs confirmation. | [M10], [M06] |
datadog-incident-timelinePASS 1.00

? The Question

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 $0.1354 · 34s · in 21,774 / out 3,341 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

first_error: timestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
cascade_order: service=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
job_class_involved: RewardGiveJob
confirming_query: service:reward-service status:error "@message:Redis::TimeoutError"
logs_do_not_show: root 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)
clean_services: postgres
graded checks: first_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

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

Cascade, in order:
1. 14:01:12Z — `reward-service` loses Redis connectivity.
2. 14:01:20Z–14:01:40Z — Redis retries are exhausted for `RewardGiveJob`.
3. 14:01:40Z onward — `sidekiq` reports repeated `RewardGiveJob` failures and retries.
4. 14:02:30Z — reward queue depth exceeds 10,000.
5. 14:03:05Z onward — `api-gateway` returns 502 upstream timeouts while calling `reward-service`.
6. 14:03:30Z onward — `web-app` give-form submissions fail because of the upstream 502s.
7. 14:22:10Z — Redis connectivity is restored and job processing resumes.
8. 14:24:45Z — reward queue depth falls below 500.

Service and jobs involved:
- Initial failing service: `reward-service`
- Worker service: `sidekiq`
- Primary affected job: `RewardGiveJob`
- `sidekiq_jobs.csv` also shows `RecognitionDigestJob` failing with `Redis::TimeoutError`, beginning at 2026-09-03T14:02:36Z.

Datadog query to confirm the first error:
```text
service:reward-service status:error "Redis::TimeoutError" "redis-primary:6379"
```
Time window: `2026-09-03T14:01:00Z` through `2026-09-03T14:01:20Z`.

The logs do not show:
- Why `redis-primary:6379` stopped responding.
- Any Redis host metrics, infrastructure failure, deployment, or configuration change.
- Why connectivity was restored at 14:22:10Z.
- Whether every failed job ultimately succeeded or whether any jobs were lost.
- Evidence that PostgreSQL caused the failure; its entries only report successful checkpoints.
feature-flag-state-summaryPASS 1.00

? The Question

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 $0.0716 · 16s · in 24,758 / out 1,592 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

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
code_referenced: recognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2
orphan_flags_no_code_reference: legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup
flags_with_no_targets: paused_offboard_cleanup
rule: company counts must equal the export; do not describe flags absent from the export.
graded checks: orphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer
| Flag | State | What it controls per code | Targeting rule | Exported company count |
|---|---|---|---|---:|
| `recognition_streaks_v2` | On | Records recognition gives through `StreakTracker.record(give)`. | `segment:beta_companies` | 42 |
| `points_budget_guardrails` | On | Enforces giver point budgets through `BudgetService.enforce!`. | `all_companies` | 220 |
| `slack_dm_nudges` | On | Sends Slack DM nudges; the job exits when the flag is disabled. | `segment:region_na` | 87 |
| `redeem_flow_redesign` | Off | Selects the V2 redemption component when enabled; otherwise renders V1. | `targeted_list` | 12 |
| `analytics_dashboard_v3` | On | Initializes the V3 analytics dashboard. | `segment:tier_three` | 65 |
| `ms_teams_app_v2` | Off | Installs Teams App V2 when enabled. | `targeted_list` | 9 |
| `legacy_give_modal` | Off | No reference in the provided code excerpt. | `segment:legacy_plan` | 14 |
| `survey_boosters_q3` | On | No reference in the provided code excerpt. | `segment:legacy_plan` | 7 |
| `paused_offboard_cleanup` | Off | No reference in the provided code excerpt. | No targeting rule or targets | 0 |

Company names are not provided, so only segments, targeting types, and counts can be identified.

Count check: `42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456` exported flag-company assignments. Unique company count cannot be calculated because companies may overlap across flags. On-state assignments total `42 + 220 + 87 + 65 + 7 = 421`; off-state assignments total `12 + 9 + 14 + 0 = 35`.
nda-triagePASS 1.00

? The Question

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 $0.0628 · 19s · in 23,939 / out 870 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

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_in: NDA-2
checks: classification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked
graded checks: green · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer
NDA-1 — GREEN — Standard mutual NDA: “Carve-outs: information that (a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order”; standard Delaware law and term.

NDA-2 — YELLOW — Counsel review required for embedded 18-month employee non-solicit/no-hire: “During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.”

NDA-3 — RED — Full legal review required due to the broad three-year non-compete (“Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business”), absent confidentiality carve-outs (“No confidentiality carve-outs are stated in this Agreement”), five-year one-way term, and unusual foreign governing law (“the laws of the Republic of Ireland” with “exclusive jurisdiction of its courts”).