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openai/gpt-5.6-luna

Mean 0.936 · 30/40 perfect tests · $0.42 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 $0.0204 · 86s · in 12 / out 10,018 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+ = deals currently in DS2, DS3, DS4, or DS5.
- Why-buys non-compliant = blank, fewer than 15 characters, or a placeholder. `LONG(n)` is compliant.
- Regression = any populated earlier-stage timestamp later than a populated later-stage timestamp.
- DS1 missing = no `t_ds1` timestamp.

Results:
- DS2+ deals: 119
- DS2+ why-buys non-compliant: 97
- Overall non-compliance: 97 / 119 = 81.5%

By owner:
- Bryce Harmon: 15 / 18 = 83.3%
- Alex Franklin: 45 / 57 = 78.9%
- Dana Mercer: 17 / 21 = 81.0%
- Cole Ingram: 19 / 22 = 86.4%
- Farid Osman: 1 / 1 = 100.0%

Other hygiene findings:
- Deals with no DS1 timestamp: 0
- Deals with regressed stage timestamps: 0

Process fixes:
1. Make why-buys mandatory before moving a deal from DS1 to DS2; enforce a 15-character minimum and reject placeholders.
2. Add CRM validation preventing stage-entry timestamps from being edited into chronological regression.
3. Create a weekly hygiene report by owner showing DS2+ missing/invalid why-buys, missing DS1 timestamps, and timestamp regressions.
owner-resolution0.83

? 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 $0.0105 · 22s · in 6 / out 2,536 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: 155

Unmatched owner IDs: None. Every owner ID in the deal extract matched an owner in owners.csv.

Owner IDs mapped to archived/deactivated owners: None.
- Archived owners in the owners table—1520255671 (Gavin Porter) and 77260721 (Hugo Lindqvist)—do not appear in the deal extract.

Total pipeline amount per resolved owner:

- Bryce Harmon (owner_id 119337721): 35 deals  
  24,000 + 19,656 + 13,500 + 7,000 + 2,520 + 240,000 + 99,000 + 72,000 + 70,000 + 63,600 + 45,000 + 1 + 21,000 + 23,400 + 13,680 + 5,502 + 8,160 + 1 + 11,400 + 1 + 36,000 + 31,500 + 6,000 + 30,275 + 17,400 + 12,600 + 18,000 + 37,440 + 18,828 + 2,880 + 36,000 + 20,880 + 10,920 + 25,200 + 10,800 = $1,054,144

- Alex Franklin (owner_id 84342457): 68 deals  
  Sum of 68 deal amounts = $629,710

- Dana Mercer (owner_id 83155923): 22 deals  
  11,250 + 10,500 + 9,000 + 9,000 + 4,600 + 1,920 + 15,000 + 4,200 + 18,900 + 27,000 + 43,875 + 20,000 + 60,000 + 8,100 + 16,250 + 3,150 + 5,000 + 2,100 + 23,400 + 5,400 + 7,350 + 25,000 = $330,995

- Cole Ingram (owner_id 83155924): 22 deals  
  2,760 + 1,330 + 7,225.40 + 3,360 + 5,616 + 2,700 + 7,781.20 + 6,947.50 + 40,000 + 12,168 + 2,480.40 + 11,116 + 11,193 + 1,875 + 31,750 + 32,175 + 4,779.88 + 3,334.80 + 58,529.25 + 4,140 + 18,000 + 18,900 = $288,161.43

- Farid Osman (owner_id 716654662): 7 deals  
  1,249 + 1 + 1 + 1 + 2,880 + 1 + 1 = $4,134

- Elena Sinclair (owner_id 701163055): 1 deal  
  2,100 = $2,100
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 $0.0166 · 60s · in 9 / out 6,422 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 total − 37 rows whose deal IDs match deals_open = 30 orphans.

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

(b) Duplicate conversation keys: 0 rows

Arithmetic: For every Gong row, calls_90d = distinct_conversation_keys. Therefore, no row has calls_90d exceeding distinct_conversation_keys.

(c) Open deals at DS3 or later with at least one logged call: 23 of 83 = 27.71%

Arithmetic: 23 open DS3+ deals with a matching Gong row ÷ 83 open DS3+ deals = 0.2771 = 27.71%.
snowflake-sql-trapped-catalog0.50

? 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 $0.0044 · 8s · in 3 / out 527 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 produce a valid query using only the provided catalog.
-- The catalog does not provide:
--   1. A customer-company identifier in PRODUCTION.PLG.COMPANY_COHORT_SUMMARY.
--   2. A giving/recognition event table for calculating unique givers.
--   3. A recognition-event table or documented recognition-count column.
--   4. A documented mapping between PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2
--      and customer companies or calendar months.
-- M1_USERS, M1_GIVES, and M1_REDEMPTIONS cannot be substituted because they do not
-- document unique givers, recognition counts, or the requested calendar-month logic.
```
crm-completeness-enrichment0.80

? 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 $0.0129 · 66s · in 9 / out 6,553 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

Scope:
- Companies: 34 records
- Contacts: 50 records
- Deals: No deals file was provided, so deal-field completeness and pipeline exposure cannot be calculated.

Completeness by field

Companies

| Field | Complete | Arithmetic | Completeness |
|---|---:|---:|---:|
| industry | 34/34 | 34 ÷ 34 | 100.00% |
| employee_count | 23/34 | 23 ÷ 34 | 67.65% |
| hq_country | 28/34 | 28 ÷ 34 | 82.35% |

Missing company fields:
- employee_count: 11 records
- hq_country: 6 records
- industry: 0 records

Contacts

| Field | Complete | Arithmetic | Completeness |
|---|---:|---:|---:|
| email populated | 50/50 | 50 ÷ 50 | 100.00% |
| email valid format | 46/50 | 46 ÷ 50 | 92.00% |
| title | 41/50 | 41 ÷ 50 | 82.00% |
| persona | 36/50 | 36 ÷ 50 | 72.00% |

Deals

No `deals.csv` data was provided. Completeness for owner, stage, amount, close date, and why-buys is therefore not computable.

Duplicate company clusters

1. Shared domain: `acme-corp.com`
   - C-0A092931
   - C-0A092932
   - Recommended survivor: C-0A092931
   - Recommended action: merge C-0A092932 into C-0A092931 after resolving `employee_count` (500 vs 510), `industry` (`Technology` vs `tech`), and `hq_country` (`US` vs `USA`).

2. Shared domain: `globex.io`
   - C-0A092933
   - C-0A092934
   - Recommended survivor: C-0A092933
   - Recommended action: merge C-0A092934 into C-0A092933 after resolving `industry` (`SaaS` vs `Technology`). Employee count and HQ country agree.

Invalid emails

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

Domain mismatch:
- CT-0011: `user1@other-domain.com`
  - Contact/company domain field: `66d1fc.com`
  - Email domain: `other-domain.com`

No other email-domain mismatches were identified.

Company enrichment fixes

The enrichment export has matching rows for the following missing CRM `employee_count` values:

| Company alias | CRM value | Enrichment value | Recommendation |
|---|---|---:|---|
| C-EC3025 | missing | 400 | Fill from enrichment |
| C-96039F | missing | 400 | Fill from enrichment |
| C-44EA29 | missing | 400 | Fill from enrichment |
| C-D04904 | missing | 400 | Fill from enrichment |
| C-B23205 | missing | 400 | Fill from enrichment |
| C-60C75F | missing | 400 | Fill from enrichment |
| C-2C60E5 | missing | 340 | Fill from enrichment |
| C-7BBDFA | missing | 400 | Fill from enrichment |
| C-50D386 | missing | 400 | Fill from enrichment |

No missing CRM `hq_country` values can be filled from enrichment because the matching enrichment rows are also blank or unavailable:
- C-2D1F1B: CRM blank; enrichment blank
- C-D73B89: CRM blank; enrichment blank
- C-2C60E5: CRM blank; enrichment blank
- C-EE9FFB: CRM blank; no matching enrichment row

Industry disagreements

| Company alias | CRM value | Enrichment value | Recommendation |
|---|---|---|---|
| C-66D1FC | tech | Computer Software | Prefer enrichment as the standardized value |
| C-EC3025 | Technology | Computer Software | Prefer enrichment as the standardized value |
| C-44EA29 | tech | Computer Software | Prefer enrichment as the standardized value |
| C-92D97D | Technology | Computer Software | Prefer enrichment as the standardized value |
| C-D04904 | Technology | Computer Software | Prefer enrichment as the standardized value |
| C-77A95A | Technology | Computer Software | Prefer enrichment as the standardized value |
| C-AA8DDA | Technology | Computer Software | Prefer enrichment as the standardized value |
| C-B25F40 | Technology | Computer Software | Prefer enrichment as the standardized value |
| C-60C75F | tech | Computer Software | Prefer enrichment as the standardized value |
| C-425E2A | Tech  | Computer Software | Prefer enrichment as the standardized value |

The recommendation is to use the enrichment value for normalization, while retaining the CRM value in history or an audit field.

Employee-count disagreements

The matching enrichment rows disagree with CRM because CRM is blank for the nine companies listed in the enrichment-fix table above. No populated CRM employee counts conflict with populated enrichment counts.

HQ-country representation disagreements

These appear to be country-label normalization differences rather than different countries:

| Company alias | CRM value | Enrichment value | Recommendation |
|---|---|---|---|
| C-66D1FC | US | United States | Standardize to `United States` |
| C-C6FE92 | United States | United States | No substantive disagreement |
| C-950043 | US | United States | Standardize to `United States` |
| C-EC3025 | USA | United States | Standardize to `United States` |
| C-96039F | USA | United States | Standardize to `United States` |
| C-77A95A | US | United States | Standardize to `United States` |
| C-E51FB7 | USA | United States | Standardize to `United States` |
| C-D0662E | US | United States | Standardize to `United States` |
| C-425E2A | USA | United States | Standardize to `United States` |

Contact fixes

- Correct malformed emails for CT-0010, CT-0080, CT-0081, and CT-0192.
- Resolve the domain mismatch for CT-0011.
- Populate missing titles for 9 contacts.
- Populate missing personas for 14 contacts.
- Validate whether contact `domain` should be derived from the email domain or remain linked to the company domain.

Top 10 fixes by pipeline amount at stake

Cannot be calculated. No deal records, deal amounts, owners, stages, close dates, or why-buys were provided.
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 $0.0112 · 40s · in 6 / out 4,602 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 of all 89 lost deals

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

Category counts

- Other: 26
- Timing: 22
- Competitor: 20
- No decision: 10
- Pricing: 6
- Product gap: 5
- Champion left: 0

Arithmetic: 26 + 22 + 20 + 10 + 6 + 5 + 0 = 89 deals.

Side split

- Buyer: 59
- Unknown: 25
- Bonusly: 5

Arithmetic: 59 + 25 + 5 = 89 deals.

Structured-tag disagreements

15 deals have a structured `closed_lost_tag` that clearly disagrees with the primary category indicated by the free-text reason.

Two patterns most worth acting on

1. Timing and no-decision losses are substantial:
   - Timing: 22
   - No decision: 10
   - Combined: 22 + 10 = 32 deals
   - The text frequently cites pauses, deprioritization, budget cycles, future-year follow-up, or lack of approval.

2. Competitor losses are concentrated and often have specific product or commercial drivers:
   - Competitor: 20 deals
   - The cited drivers include broader offerings, integrations or preferred partnerships, customization, local/Canadian providers, existing platforms, and budget-related considerations. Five deals were classified as product gaps, including cases where the structured tag was Competitor or MIA.
pipeline-tiering0.80

? 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 $0.0193 · 454s · in 15 / out 6,771 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":8,"ACTION":47,"BUILD":28,"REVIVE":35,"WATCH":32,"RISKY":5},"tier_examples":{"LOCK":["Deal-25F752","Deal-C26D20","Deal-403845"],"ACTION":["Deal-A5E80A","Deal-63436734854","Deal-63925115724"],"BUILD":["Deal-66D1FC","Deal-D73B89","Deal-523604"],"REVIVE":["Deal-61129636004","Deal-7BBDFA","Deal-950043"],"WATCH":["Deal-E53952","Deal-5408B0","Deal-C6FE92"],"RISKY":["Deal-547B2B","Deal-B7EBD1","Deal-A2B47C"]},"risky_deals":["Deal-547B2B","Deal-B7EBD1","Deal-A2B47C","Deal-2465CE","Deal-584EE5"],"lock_violations":0,"pipeline_shape":"The pipeline is heavily concentrated in early-stage PIPELINE deals, with limited meeting activity relative to total coverage. A small advanced-stage group has current engagement and supports LOCK or ACTION treatment, while several COMMIT deals lack meetings_30d and are RISKY because forecast category conflicts with engagement evidence; older last-contact dates create a meaningful REVIVE segment."}
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 $0.0057 · 12s · in 3 / out 1,423 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": [
      "Automating anniversary and birthday awards would be a big win.",
      "The HR team of three cannot keep up with the process manually."
    ],
    "pain_points": [
      "Awards are tracked manually in a spreadsheet.",
      "People slip through the cracks."
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (HR Admin)"
    ],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year.",
    "timeline_signal": "Ideally live before open enrollment in November.",
    "competitor_mentioned": "Achievers",
    "next_step": "Security review on September 12.",
    "objections": [
      "Needs SSO and audit logs for IT sign-off.",
      "Achievers was too heavy for a team their size."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-002",
    "deal_alias": "Deal-70BB30",
    "why_buys": [
      "Tie recognition to retention for the hourly workforce."
    ],
    "pain_points": [
      "Regretted turnover among the hourly workforce is over 30%."
    ],
    "stakeholders": [
      "Prospect (Head of Total Rewards)",
      "Prospect (CFO)"
    ],
    "budget_signal": "$25k pilot budget approved for this quarter.",
    "timeline_signal": "Decision by end of September.",
    "competitor_mentioned": null,
    "next_step": "Send the pilot agreement; the prospect will route it to legal this week.",
    "objections": [
      "Workday integration has to be rock solid."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-003",
    "deal_alias": "Deal-530B50",
    "why_buys": [
      "Make recognition visible across 12 retail locations."
    ],
    "pain_points": [
      "Store managers have zero budget autonomy for on-the-spot recognition.",
      "The CEO must be sold first because she decides anything people-related."
    ],
    "stakeholders": [
      "Prospect (People Ops Manager)"
    ],
    "budget_signal": null,
    "timeline_signal": "No rush 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."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": [
      "Consolidate three separate recognition tools into one."
    ],
    "pain_points": [
      "They are paying for three tools.",
      "The tools do not integrate with their HRIS."
    ],
    "stakeholders": [
      "Prospect (VP People)",
      "Prospect (IT Security Lead)"
    ],
    "budget_signal": "If under $15k annually, the VP People can approve it without going to the board.",
    "timeline_signal": "Procurement cycle runs six to eight weeks minimum.",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": [
      "The security review took three months for the last vendor.",
      "The prospect needs to check the CFO's calendar and made no promise about a follow-up."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-005",
    "deal_alias": "Deal-F8767A",
    "why_buys": [
      "Automate service milestones.",
      "Get analytics on recognition equity across departments."
    ],
    "pain_points": [
      "Night-shift teams feel invisible.",
      "Night-shift engagement scores run 20 points lower.",
      "The executive team is skeptical after a failed rollout two years ago."
    ],
    "stakeholders": [
      "Prospect (HR Director)",
      "Prospect (People Ops Coordinator)"
    ],
    "budget_signal": "$12k approved under the engagement line.",
    "timeline_signal": "Need it running before the January all-hands.",
    "competitor_mentioned": "Nectar",
    "next_step": "Present to the executive team on October 2.",
    "objections": [
      "They are mid-pilot with Nectar, so the experience must be better.",
      "The executive team is skeptical after a failed rollout two years ago."
    ],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": [
      "Cut the administrative time spent on service awards."
    ],
    "pain_points": [
      "The HR Manager spends five hours a month ordering and shipping plaques.",
      "The COO usually prefers building things in-house."
    ],
    "stakeholders": [
      "Prospect (HR Manager)"
    ],
    "budget_signal": "Budget is not the issue.",
    "timeline_signal": "A Q1 start is realistic because the fiscal year resets in February.",
    "competitor_mentioned": "Doing it internally / building things in-house",
    "next_step": "Send the one-page overview; the prospect will forward it to the COO this week.",
    "objections": [
      "The 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 $0.0044 · 7s · in 3 / out 470 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: Redlines came back clean, the signing page is out, and the VP People said they are signing tomorrow. Slack explicitly calls it “signature-imminent.”  
What’s left: Customer signature.

2. Deal-403845 — $9,000  
Why close: Slack says it is “moving fine,” and the order form is with the customer’s finance team. It is DS5/COMMIT with a 2026-09-11 close date.  
What’s left: Finance-team review and order-form signature.

3. Deal-A2B47C — $6,360  
Why close: Slack says it is “still warm” and progressing at a normal legal-review pace. It is DS5/COMMIT with a 2026-09-11 close date.  
What’s left: Completion of legal review and signature.

Deal-D348E1 is similarly described as “still warm” and at a normal legal-review pace, but its CRM close date is later (2026-10-15 versus Deal-A2B47C’s 2026-09-11), so Deal-A2B47C ranks third.

Amount arithmetic: $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 $0.0045 · 9s · in 3 / out 631 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 classification

| Deal | Amount | Prospect quote | Classification | Basis |
|---|---:|---|---|---|
| Deal-EC3025 | Not provided | “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” | Real gap | Product docs state SCIM user provisioning is not currently listed as supported. |
| Deal-D0D6B5 | Not provided | “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” | Real gap | Product docs state ADP Workforce Now integration is not currently listed as supported. |
| Deal-CFE7F4 | Not provided | “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?” | Plan gate | Product docs place the custom report builder on Enterprise only. |
| Deal-84DBA6 | Not provided | “We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.” | Rollout/enablement issue | Slack integration is supported on all plans; the stated issue is lack of manager training. |
| Deal-36C33F | Not provided | “Good to know. The web version should be fine for our office staff for now.” | Not a product gap raised by the prospect | The mobile-app limitation was stated by the rep, not the prospect, and the prospect accepted the web version. |

Summary — real product gaps only

- Deal-EC3025 — Amount: not provided  
  “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.”  
  Product gap: SCIM user provisioning is not listed as supported.

- Deal-D0D6B5 — Amount: not provided  
  “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.”  
  Product gap: ADP Workforce Now integration is not listed as supported.

Arithmetic: No deal amounts were provided, so no amount calculation is possible.
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 $0.0191 · 62s · in 6 / out 8,131 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
Snapshot date: 2026-09-05. Stale means the latest of last_email, last_call, and last_meeting was before 2026-08-29, or all three fields were missing.

Bryce Harmon

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

Arithmetic: $240,000 + $99,000 + $70,000 + $45,000 + $37,440 + $36,000 + $31,500 + $23,400 + $21,000 + $11,400 + $6,000 + $5,502 + $1 = $626,243.00

Stale deals: 13
Total stale amount: $626,243.00

Dana Mercer

| Deal alias | Stage | Amount | Days since last contact |
|---|---:|---:|---:|
| Deal-44EA29 | DS2 | $60,000.00 | 10 |
| Deal-E51FB7 | DS2 | $43,875.00 | 12 |
| Deal-B42F46 | DS1 | $27,000.00 | 19 |
| Deal-BA3DDC | DS3 | $23,400.00 | 15 |
| Deal-9DDE86 | DS2 | $20,000.00 | 15 |
| Deal-215CCA | DS3 | $18,900.00 | 17 |
| Deal-B7EBD1 | DS5 | $9,000.00 | 16 |
| Deal-3974EB | DS4 | $9,000.00 | 8 |
| Deal-F40F04 | DS2 | $8,100.00 | 15 |
| Deal-87DDD1 | DS1 | $5,000.00 | 19 |
| Deal-F336B6 | DS3 | $4,200.00 | 15 |
| Deal-0660B4 | DS4 | $1,920.00 | 16 |
| Deal-FD9F4E | DS5 | $1,330.00 | 10 |

Arithmetic: $60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $9,000 + $9,000 + $8,100 + $5,000 + $4,200 + $1,920 + $1,330 = $231,725.00

Stale deals: 13
Total stale amount: $231,725.00

Alex Franklin

| Deal alias | Stage | Amount | Days since last contact |
|---|---:|---:|---:|
| Deal-CC08D1 | DS1 | $24,000.00 | 16 |
| Deal-E73427 | DS3 | $18,000.00 | 10 |
| Deal-885F45 | DS2 | $9,300.00 | 12 |
| Deal-C2FF3C | DS1 | $8,316.00 | 10 |
| Deal-0D2F7A | DS3 | $5,100.00 | 12 |
| Deal-6C60D4 | DS3 | $4,800.00 | 12 |
| Deal-13FEBD | DS2 | $4,680.00 | 12 |
| Deal-9D0060 | DS3 | $3,840.00 | 12 |
| Deal-690476 | DS2 | $3,600.00 | 18 |
| Deal-C6D97A | DS4 | $3,240.00 | 8 |
| Deal-EE195F | DS3 | $3,120.00 | 8 |
| Deal-278DEC | DS3 | $2,700.00 | 8 |
| Deal-635B8E | DS3 | $2,600.00 | 18 |
| Deal-6883F3 | DS1 | $2,400.00 | 16 |
| Deal-4A13AD | DS3 | $2,160.00 | 26 |
| Deal-F67D31 | DS2 | $1,800.00 | 8 |
| Deal-5FDCE4 | DS3 | $1,600.00 | 12 |
| Deal-7FA0C3 | DS2 | $1,400.00 | 8 |
| Deal-BA571A | DS4 | $1,080.00 | 18 |

Arithmetic: $24,000 + $18,000 + $9,300 + $8,316 + $5,100 + $4,800 + $4,680 + $3,840 + $3,600 + $3,240 + $3,120 + $2,700 + $2,600 + $2,400 + $2,160 + $1,800 + $1,600 + $1,400 + $1,080 = $103,736.00

Stale deals: 19
Total stale amount: $103,736.00

Cole Ingram

| Deal alias | Stage | Amount | Days since last contact |
|---|---:|---:|---:|
| Deal-D04904 | DS2 | $58,529.25 | 11 |
| Deal-B25F40 | DS3 | $40,000.00 | 8 |
| Deal-813836 | DS2 | $32,175.00 | 11 |
| Deal-1BA595 | DS2 | $31,750.00 | 11 |
| Deal-CFE1E8 | DS3 | $18,000.00 | 11 |
| Deal-898FC5 | DS3 | $12,600.00 | 11 |
| Deal-CD47A6 | DS2 | $12,168.00 | 11 |
| Deal-627646 | DS3 | $11,193.00 | 11 |
| Deal-FF809F | DS2 | $7,781.20 | 11 |
| Deal-AF932D | DS2 | $7,225.40 | 11 |
| Deal-A71728 | DS2 | $6,947.50 | 11 |
| Deal-8BC9F5 | DS2 | $5,616.00 | 10 |
| Deal-175395 | DS3 | $4,779.88 | 11 |
| Deal-481E24 | DS3 | $4,140.00 | 10 |
| Deal-2F3A66 | DS3 | $3,334.80 | 11 |
| Deal-342E96 | DS2 | $2,700.00 | 24 |
| Deal-E568D5 | DS3 | $1,875.00 | 11 |

Arithmetic: $58,529.25 + $40,000 + $32,175 + $31,750 + $18,000 + $12,600 + $12,168 + $11,193 + $7,781.20 + $7,225.40 + $6,947.50 + $5,616 + $4,779.88 + $4,140 + $3,334.80 + $2,700 + $1,875 = $260,815.03

Stale deals: 17
Total stale amount: $260,815.03

Elena Sinclair

| Deal alias | Stage | Amount | Days since last contact |
|---|---:|---:|---:|
| Deal-57FF13 | DS1 | $2,100.00 | Not computable — no last_email, last_call, or last_meeting value provided |

Arithmetic: $2,100 = $2,100.00

Stale deals: 1
Total stale amount: $2,100.00
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 $0.0539 · 219s · in 45 / out 12,972 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
30-day window: 2026-08-06 through 2026-09-05, inclusive.

Efficiency ranking: lowest activities per DS2 entry first.

1. Alex Franklin
   - Emails: 307
   - Calls: 36
   - Meetings: 41
   - Total activities: 307 + 36 + 41 = 384
   - Activity mix:
     - Emails: 307 / 384 = 79.9%
     - Calls: 36 / 384 = 9.4%
     - Meetings: 41 / 384 = 10.7%
   - DS2 entries: 18
   - Activities per DS2 entry: 384 / 18 = 21.3

2. Bryce Harmon
   - Emails: 162
   - Calls: 0
   - Meetings: 43
   - Total activities: 162 + 0 + 43 = 205
   - Activity mix:
     - Emails: 162 / 205 = 79.0%
     - Calls: 0 / 205 = 0.0%
     - Meetings: 43 / 205 = 21.0%
   - DS2 entries: 4
   - Activities per DS2 entry: 205 / 4 = 51.3

3. Cole Ingram
   - Emails: 96
   - Calls: 14
   - Meetings: 1
   - Total activities: 96 + 14 + 1 = 111
   - Activity mix:
     - Emails: 96 / 111 = 86.5%
     - Calls: 14 / 111 = 12.6%
     - Meetings: 1 / 111 = 0.9%
   - DS2 entries: 2
   - Activities per DS2 entry: 111 / 2 = 55.5

4. Farid Osman
   - Emails: 38
   - Calls: 0
   - Meetings: 34
   - Total activities: 38 + 0 + 34 = 72
   - Activity mix:
     - Emails: 38 / 72 = 52.8%
     - Calls: 0 / 72 = 0.0%
     - Meetings: 34 / 72 = 47.2%
   - DS2 entries: 1
   - Activities per DS2 entry: 72 / 1 = 72.0

5. Dana Mercer
   - Emails: 84
   - Calls: 18
   - Meetings: 11
   - Total activities: 84 + 18 + 11 = 113
   - Activity mix:
     - Emails: 84 / 113 = 74.3%
     - Calls: 18 / 113 = 15.9%
     - Meetings: 11 / 113 = 9.7%
   - DS2 entries: 1
   - Activities per DS2 entry: 113 / 1 = 113.0

Excluded from efficiency ranking:

- Elena Sinclair: 0 emails, 0 calls, 0 meetings; 0 DS2 entries. Ratio is undefined because 0 / 0.
- Gavin Porter: archived; no deals or engagement rows provided.
- Hugo Lindqvist: archived; no deals or engagement rows provided.

Most efficient rep: Alex Franklin, at 21.3 activities per DS2 entry.

Highest-volume rep: Alex Franklin, with 384 total activities.

They do not differ.
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 $0.0165 · 66s · in 12 / out 7,381 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
QTD scorecard — Alex Franklin
As of 2026-09-05

Bookings vs quota
- Quota: $200,000
- QTD closed-won bookings: $150,000
  - $40,000 + $20,000 + $35,000 + $21,000 + $12,000 + $11,000 + $6,500 + $4,500 = $150,000
- Attainment: $150,000 / $200,000 = 75.0%
- Remaining to quota: $200,000 − $150,000 = $50,000

New vs. expansion
- New: $113,500
  - $40,000 + $35,000 + $21,000 + $11,000 + $6,500 = $113,500
- Expansion: $36,500
  - $20,000 + $12,000 + $4,500 = $36,500
- Split: 75.7% new / 24.3% expansion

Active pipeline by stage
- DS1: 20 deals, $284,621
- DS2: 28 deals, $353,760
- DS3: 54 deals, $445,063
- DS4: 5 deals, $23,574
- DS5: 5 deals, $45,730
- Total active pipeline: 112 deals, $1,152,748

Rolling 90-day DS2-to-won rate
Using deals that entered DS2 from 2026-06-07 through 2026-09-05 and reached a closed outcome:
- Won: 8
- Lost: 27
- Rate: 8 / (8 + 27) = 8 / 35 = 22.9%

Win and loss counts
- QTD wins: 8
- QTD losses: 27
- Total closed deals: 8 + 27 = 35
- Win rate: 8 / 35 = 22.9%
- Top loss reason: Lost- Timing (1 year or more), 13 losses
  - 13 / 27 = 48.1%

Activity volume in the last 30 days
The provided engagement data is deal-level. Summing activity for the 35 QTD closed deals:
- Emails: 208
  - 109 on lost deals + 99 on won deals = 208
- Calls: 58
  - 25 + 33 = 58
- Meetings: 38
  - 13 + 25 = 38
- Notes: 49
  - 25 + 24 = 49

Coaching observations
1. Alex is at 75.0% attainment with $50,000 remaining, so closing at least the largest near-term opportunities is necessary to reach quota.
2. Timing is the dominant loss driver: 13 of 27 losses, or 48.1%. Qualification should more aggressively test budget, urgency, and a credible purchase timeline before opportunities advance.
3. The DS2-to-won rate is 22.9%, while active DS2 pipeline totals $353,760. Improving DS2 conversion is the clearest leverage point; at the observed rate, that stage alone would imply approximately $81,000 of expected bookings, calculated as $353,760 × 22.9%.
multithreading-gap0.75

? 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 $0.0065 · 18s · in 6 / out 1,791 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
Open status, amount, and stage are not provided, so amount and stage are unavailable and stage-based persona prioritization cannot be determined. Using 2026-09-05 as the reference date, active means engaged on or after 2026-07-07 and not former.

Deal-EC3025
- Amount: not provided
- Stage: not provided
- Active contact count: 1 — 2 total listed contacts − 1 former = 1
- Personas present: champion
- Personas missing: economic buyer, HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-6827DB, Chief People Officer, economic buyer

Deal-92D97D
- Amount: not provided
- Stage: not provided
- Active contact count: 1 — CT-01F5B4 is active; CT-A902AE last engaged 2026-06-01, outside the 60-day window
- Personas present: HR admin
- Personas missing: economic buyer, champion, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: none on file

Deal-50D386
- Amount: not provided
- Stage: not provided
- Active contact count: 2
- Personas present: champion, HR admin
- Personas missing: economic buyer, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-A1C4B3, Chief People Officer, economic buyer

Deal-D0D6B5
- Amount: not provided
- Stage: not provided
- Active contact count: 3
- Personas present: champion only — 3 active contacts, all champion
- Personas missing: economic buyer, HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-1FA4DB, Chief People Officer, economic buyer

Deal-5BFE3B
- Amount: not provided
- Stage: not provided
- Active contact count: 2
- Personas present: champion only
- Personas missing: economic buyer, HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: none on file

Deal-36C33F
- Amount: not provided
- Stage: not provided
- Active contact count: 1 — 2 former contacts excluded; 1 IT security contact remains active
- Personas present: IT security
- Personas missing: economic buyer, champion, HR admin, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-1DB73E, Chief People Officer, economic buyer

Deal-885F45
- Amount: not provided
- Stage: not provided
- Active contact count: 2
- Personas present: economic buyer, champion
- Personas missing: HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-B3F25D, IT Security Lead, IT security

Deal-FCBE5B
- Amount: not provided
- Stage: not provided
- Active contact count: 1
- Personas present: champion
- Personas missing: economic buyer, HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: none on file

Deal-5408B0
- Amount: not provided
- Stage: not provided
- Active contact count: 2
- Personas present: champion, HR admin
- Personas missing: economic buyer, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-07FA76, Chief People Officer, economic buyer

Deal-C6D97A
- Amount: not provided
- Stage: not provided
- Active contact count: 3
- Personas present: champion only — 3 active contacts, all champion
- Personas missing: economic buyer, HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: none on file

Deal-F9A08A
- Amount: not provided
- Stage: not provided
- Active contact count: 1 — CT-913581 last engaged 2026-06-20, outside the 60-day window
- Personas present: champion
- Personas missing: economic buyer, HR admin, IT security, finance
- Most valuable persona to add given the stage: cannot determine; stage not provided
- Matching unengaged contact on file: CT-697541, Chief People Officer, economic buyer
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 $0.0051 · 9s · in 3 / out 736 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
- First five minutes: They lead with a customer proof point about a 400-person retailer reducing regretted turnover through automated milestone awards in 8 of 10 calls (80%). The other two starts were an agenda/security-and-pricing opener (TT-004) and a pricing-first opener (TT-009). 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.”

- Objection 1 — budget locked: 3 of 10 calls. They reframe the purchase as funded by turnover savings and use avoided-backfill savings as financial proof. Quote: “Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off.”

- Objection 2 — revisit next quarter/timing: 3 of 10 calls. They propose a limited 90-day, one-department pilot to generate internal data before planning. Quote: “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?”

- Objection 3 — current spreadsheet and gift-card process is sufficient: 3 of 10 calls. They position automation and recognition analytics as the reason to change. Quote: “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 agreed: 7 of 10 calls = 70%. Agreements occurred in TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, and TT-009. In each, the prospect agreed to a Thursday 2pm working session and to bring the HRIS manager.

- Competitors raised by prospects: Awardco (TT-003) and Kudos (TT-007). Workhuman was mentioned by Alex Franklin, not raised by a prospect.

Coaching notes:
1. Preserve the strong, repeatable financial and pilot-based objection handling, but tailor the opening more often to the prospect’s stated context instead of using the same retailer proof point.
2. When a prospect signals no urgency, committee dependency, or a desire to wait, replace “I'll leave it with you” or “Fair enough” with a specific low-commitment follow-up or decision date.
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 $0.0148 · 63s · in 18 / out 6,299 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

Quarter: 2026-07-01 through 2026-09-30, inclusive.

| Category | Deal count | Total amount |
|---|---:|---:|
| COMMIT | 7 | 44,729 |
| BEST_CASE | 24 | 203,565 |
| PIPELINE | 23 | 201,637.40 |

Weighted forecast rule:

- COMMIT: 100% × 44,729 = 44,729
- BEST_CASE: 35% × 203,565 = 71,247.75
- PIPELINE: 0% × 201,637.40 = 0

Weighted forecast = 44,729 + 71,247.75 + 0 = 115,976.75

## Excluded outside-quarter deals

32 deals were excluded because their close dates were after 2026-09-30.

Total excluded amount:

43,875 + 18,000 + 17,000 + 13,770 + 10,800 + 9,000 + 9,000 + 7,920 + 7,690 + 7,500 + 7,200 + 5,700 + 5,400 + 5,400 + 5,400 + 5,400 + 5,160 + 4,800 + 4,400 + 4,300 + 4,000 + 3,600 + 3,600 + 3,600 + 3,300 + 2,400 + 1,800 + 1,800 + 1,680 + 1,600 + 1,400 + 1,080 = 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
- Deal-F5A622

## Top 5 BEST_CASE deals inside the quarter

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

Top 5 total = 38,935 + 24,000 + 19,656 + 16,250 + 11,116 = 109,957

## Data quality

85 of 86 deals have a blank owner, so ownership and accountability cannot be reliably analyzed. The `why_buys_chars` field is zero for most deals, indicating largely missing or unpopulated qualification data. No currency field is provided, so the forecast amount’s currency is unspecified. The extract contains no explicit completeness or validation indicators for forecast category, stage, amount, or date values.
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 $0.0217 · 31s · in 12 / out 2,539 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
| First-month signal cohort | Cohort size | Retained at 24 months (`active`) | 24-month retention rate |
|---|---:|---:|---:|
| Both signals: `m1_users >= 5` and `m1_redemptions >= 1` | 47 | 31 | `31 / 47 = 66.0%` |
| Givers-only: `m1_users >= 5` and `m1_redemptions = 0` | 49 | 23 | `23 / 49 = 46.9%` |
| Redemption-only: `m1_users < 5` and `m1_redemptions >= 1` | 29 | 9 | `9 / 29 = 31.0%` |
| Neither: `m1_users < 5` and `m1_redemptions = 0` | 95 | 38 | `38 / 95 = 40.0%` |

Total denominator:

`47 + 49 + 29 + 95 = 220 companies`

Excluded from denominator: 0 companies. No required signal or status fields are missing, and all companies are stated to be at least 25 months old. Only `current_status = 'active'` is counted as retained; `cancelled` and `non_renewing` are not retained.

Hypothesis result: descriptively supported. The both-signals cohort retained at `66.0%`, compared with `40.0%` for neither:

`66.0% - 40.0% = +26.0 percentage points`

Single signal with the largest retention lift: 5+ unique givers (`m1_users >= 5`).

- Giver signal: `(31 + 23) / (47 + 49) = 54 / 96 = 56.2%`
- No giver signal: `(9 + 38) / (29 + 95) = 47 / 124 = 37.9%`
- Giver-signal lift: `56.2% - 37.9% = +18.3 percentage points`

For comparison:

- Redemption signal: `(31 + 9) / (47 + 29) = 40 / 76 = 52.6%`
- No redemption signal: `(23 + 38) / (49 + 95) = 61 / 144 = 42.4%`
- Redemption-signal lift: `52.6% - 42.4% = +10.3 percentage points`

This proves an association in this extract: both first-month signals coincide with the highest observed 24-month retention. It does not prove that either signal causes retention, that the thresholds are optimal, that the differences are statistically significant, or that the result generalizes beyond these 220 companies.
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 $0.0086 · 27s · in 6 / out 3,214 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
ARR reconciliation as of 2026-09-05

Basis: Billing ARR includes active subscriptions only. Billing ARR = MRR × 12.

Totals

- CRM company ARR: $603,581.76
- Billing active-subscription ARR: $604,739.28
- Variance: $1,157.52 favorable to billing

Arithmetic:

$604,739.28 − $603,581.76 = $1,157.52

Variance decomposition

| Bucket | Arithmetic | Variance |
|---|---:|---:|
| Status mismatch | −$4,905.24 − $8,253.24 | −$13,158.48 |
| Rounding | −$16.00 − $20.00 | −$36.00 |
| Missing records | $28,449.24 − $16,497.24 | $11,952.00 |
| Other | $2,400.00 | $2,400.00 |
| Total | −$13,158.48 − $36.00 + $11,952.00 + $2,400.00 | $1,157.52 |

Mismatched accounts

| Company alias | Subscription | Issue | ARR impact | Suggested owner |
|---|---|---|---:|---|
| C-0C8323BF | SUB-000E | Billing subscription is cancelled; CRM ARR is still present | −$4,905.24 | RevOps / CRM Ops |
| C-0DC4FB8C | SUB-000F | Billing subscription is cancelled; CRM ARR is still present | −$8,253.24 | RevOps / CRM Ops |
| C-21629AA4 | SUB-0004 | Billing record exists; CRM company ARR record is missing | +$28,449.24 | CRM Ops |
| C-0D5BBE3A | None | CRM company ARR record exists; billing subscription is missing | −$16,497.24 | Billing Ops |
| C-0F7269D7 | SUB-0006 | Billing ARR exceeds CRM ARR: $26,796.00 − $24,396.00 | +$2,400.00 | RevOps / Finance |
| C-0D66DF9E | SUB-0005 | Billing ARR is below CRM ARR: $23,184.00 − $23,200.00 | −$16.00 | RevOps / Finance |
| C-14D70CE0 | SUB-0008 | Billing ARR is below CRM ARR: $18,180.00 − $18,200.00 | −$20.00 | RevOps / Finance |

Term-date violations

Any subscription with a term other than 12 months must have cf_agreement_end_date populated.

| Subscription | Company alias | Term | cf_agreement_end_date |
|---|---|---:|---|
| SUB-0002 | C-1794A52C | 24 months | Missing |
| SUB-0019 | C-22170CA1 | 36 months | 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 $0.0073 · 18s · in 6 / out 1,944 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
Unweighted averages across the 30 company_alias records. Relative change = (2026-08 − 2026-07) / 2026-07.

| KVM | 2026-08 value | 2026-07 value | Absolute change | Relative change | Direction |
|---|---:|---:|---:|---:|---|
| Giving rate | 0.602713 | 0.602297 | 0.602713 − 0.602297 = +0.000417 | +0.000417 / 0.602297 = +0.0692% | Up |
| Redemptions per user | 1.730163 | 1.729983 | 1.730163 − 1.729983 = +0.000180 | +0.000180 / 1.729983 = +0.0104% | Up |
| 1:1 meetings engagement | 0.447177 | 0.446887 | 0.447177 − 0.446887 = +0.000290 | +0.000290 / 0.446887 = +0.0649% | Up |
| Pulse check engagement | 0.508610 | 0.600587 | 0.508610 − 0.600587 = −0.091977 | −0.091977 / 0.600587 = −15.3145% | Down |

Largest relative move: pulse check engagement, down 15.3145%. The enterprise size_band drives this: its average changed by −0.2757, versus −0.00148 for smb and +0.00125 for mid_market.
rewards-ytd-sectionPASS 1.00

? 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 $0.0108 · 15s · in 9 / out 1,038 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 section — through 2026-08

Last completed month: 2026-08

- Redemption count: 378
- Spend: $27,846.00
- Unique redeemers: 235
- Redemptions per redeemer: 378 ÷ 235 = 1.6085 ≈ 1.61

Provider mix by spend:

- custom: $10,873.00 ÷ $27,846.00 = 39.05%
- Tremendous: $8,505.00 ÷ $27,846.00 = 30.54%
- Snappy: $5,238.00 ÷ $27,846.00 = 18.81%
- TangoCard: $3,230.00 ÷ $27,846.00 = 11.60%
- Total: 39.05% + 30.54% + 18.81% + 11.60% = 100.00%

Top 5 countries by redemptions:

1. US: 244
2. CA: 24
3. AU: 21
4. GB: 17
5. NL: 17
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 $0.0082 · 32s · in 6 / out 3,293 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
Snapshot: 2026-09-05  
R3 window: 2026-09-05 through 2027-01-03.

Eligibility requires all three:
- Health score < 60
- Churn-save eligible amount > $0
- Renewal within the 120-day window

The provided rules determine eligibility, but do not formally assign plays. The plays below are best-fit recommendations based only on the available signals.

Qualifying at-risk accounts

1. C-0F6C0F34 — $49,707 at stake
   - Arithmetic: health 51 < 60; eligible amount $49,707 > $0; renewal 2026-10-03 is within the window.
   - Play: Executive touch
   - Signal: champion_active=false, while usage_trend_3m=growing and seats used are 308/395. The engagement risk is stronger than the usage risk.

2. C-0B827671 — $25,365 at stake
   - Arithmetic: health 56 < 60; $25,365 > $0; renewal 2026-11-14 is within the window.
   - Play: Usage revival
   - Signal: usage_trend_3m=declining and 113/202 seats are used.

3. C-0B360C78 — $35,748 at stake
   - Arithmetic: health 57 < 60; $35,748 > $0; renewal 2026-10-28 is within the window.
   - Play: Commercial concession
   - Signal: usage_trend_3m=growing, champion_active=true, and 246/327 seats are used. No clear usage or champion-engagement problem is present, leaving the commercial lever as the best fit among the listed plays.

4. C-0B0F1BAB — $5,494 at stake
   - Arithmetic: health 38 < 60; $5,494 > $0; renewal 2026-09-23 is within the window.
   - Play: Executive touch
   - Signal: champion_active=false. Usage is flat, not declining.

5. C-0CA21961 — $16,829 at stake
   - Arithmetic: health 58 < 60; $16,829 > $0; renewal 2026-12-28 is within the window.
   - Play: Usage revival
   - Signal: only 84/325 seats are used, with usage_trend_3m=flat.

6. C-0E9C27D1 — $41,235 at stake
   - Arithmetic: health 39 < 60; $41,235 > $0; renewal 2026-09-24 is within the window.
   - Play: Commercial concession
   - Signal: usage_trend_3m=flat, champion_active=true, and 134/157 seats are used. The data does not show a usage or champion-engagement gap; the eligible commercial amount is the clearest available lever.

7. C-0CEF69FD — $32,621 at stake
   - Arithmetic: health 53 < 60; $32,621 > $0; renewal 2026-11-21 is within the window.
   - Play: Executive touch
   - Signal: champion_active=false. Usage is growing, with 97/136 seats used.

8. C-0D3278C7 — $17,602 at stake
   - Arithmetic: health 54 < 60; $17,602 > $0; renewal 2026-11-12 is within the window.
   - Play: Usage revival
   - Signal: usage_trend_3m=declining and only 126/380 seats are used.

Total amount at stake

$49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = $224,601

At-risk accounts that do not qualify

These accounts have health scores below 60 but fail at least one other eligibility rule:

- C-0BC71BDD
  - Health: 55, so R1 passes.
  - Renewal: 2026-10-27, within 120 days, so R3 passes.
  - Churn-save eligible amount: $0.00, so R2 fails.
  - Reason: no eligible amount.

- C-0BA71F12
  - Health: 52, so R1 passes.
  - Churn-save eligible amount: $6,824, so R2 passes.
  - Renewal: 2027-04-11, outside the 2026-09-05 to 2027-01-03 window, so R3 fails.
  - Reason: renewal is outside the eligibility window.

- C-0F6694C3
  - Health: 43, so R1 passes.
  - Churn-save eligible amount: $0.00, so R2 fails.
  - Renewal: 2027-03-21, outside the window, so R3 fails.
  - Reason: no eligible amount and renewal is outside the window.

- C-0BE96399
  - Health: 54, so R1 passes.
  - Renewal: 2026-10-29, within the window, so R3 passes.
  - Churn-save eligible amount: $0.00, so R2 fails.
  - Reason: no eligible amount.

- C-0F876796
  - Health: 47, so R1 passes.
  - Churn-save eligible amount: $19,958, so R2 passes.
  - Renewal: 2027-02-06, outside the window, so R3 fails.
  - Reason: renewal is outside the eligibility window.

- C-0FCCD2DF
  - Health: 43, so R1 passes.
  - Churn-save eligible amount: $0.00, so R2 fails.
  - Renewal: 2027-04-23, outside the window, so R3 fails.
  - Reason: no eligible amount and renewal is outside the window.

- C-10A56B0F
  - Health: 54, so R1 passes.
  - Renewal: 2026-12-12, within the window, so R3 passes.
  - Churn-save eligible amount: $0.00, so R2 fails.
  - Reason: no eligible amount.
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 $0.0048 · 10s · in 6 / out 647 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
- Licensed seats / headcount: 150 / 400 = 37.5%
- Unlicensed headcount headroom: 400 − 150 = 250 seats

Usage health
- Monthly active users increased from 88 in 2026-03 to 126 in 2026-08: 126 − 88 = 38 users, or 43.2%.
- 2026-08 active users represent 126 / 150 = 84.0% of licensed seats, indicating limited current usage headroom.

ARR headroom
- Current per-seat ARR: $9,000 / 150 = $60 per seat
- ARR headroom at current rate: 250 × $60 = $15,000

Reply and buying authority
- Maria S., People Operations Coordinator, replied.
- Maria S. cannot make the purchasing decision; she stated that budget and seat expansion sit with Dana R.
- Right buyer: Dana R., VP People.

Reply email

Subject: Re: Growing your team's recognition program

Hi Maria,

Thanks for the note—and glad to hear the team genuinely loves Bonusly. Usage has grown from 88 monthly active users in March to 126 in August.

I appreciate the context that Dana R. owns budget and seat expansion. Since she’s been asking about usage numbers, would you be open to introducing us? I can send a concise usage snapshot and let her decide whether a conversation makes sense.

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 $0.0049 · 11s · in 6 / out 758 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 prep

Complete
- Slack integration: complete — `integration_slack=2026-08-12`
- Allowance: set — `allowance_set=2026-08-13`
- Admins: added — `admins_added=2`
- First recognition: given — `first_recognition_at=2026-08-15 14:22`

Not complete / missing
- HRIS integration: not complete — `integration_hris` is blank
- First redemption: not complete — `first_redemption_at` is blank

Early engagement signals
- Active givers increased from 3 on 2026-08-11 to 15 on 2026-09-04.
- Arithmetic: `15 - 3 = 12` additional active givers; `15 / 3 = 5x` the starting level.
- Active givers averaged `214 / 25 = 8.56` per day.
- The first 7 days averaged `30 / 7 = 4.29` active givers; the last 7 days averaged `91 / 7 = 13`.
- Engagement is increasing, but no redemption has been recorded.

Three things to cover
1. Complete the HRIS integration and identify what is blocking it.
2. Understand why no redemption has occurred and agree on the next step to generate the first redemption.
3. Build on the rising giver activity by confirming how the two admins will sustain recognition activity.
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 $0.0107 · 36s · in 6 / out 4,306 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

show the model's full answer
90-Day Renewal Risk Brief

Date-source rule:
- Trust Chargebee for multi-year contracts because multi-year dates in ChurnZero are known to be wrong.
- For non-multi-year contracts, Chargebee and ChurnZero agree; use the Chargebee date.
- 4 date disagreements are flagged below.

| Company | CSM | ARR | Date used | Date decision / disagreement | Seat utilization | 3-month usage trend | Risk | Evidence |
|---|---|---:|---|---|---|---|---|---|
| C-0B7D2C30 | Dana Mercer | $65,901.00 | 2026-09-15 | Chargebee; disagreement: ChurnZero 2026-09-10 vs. Chargebee 2026-09-15; multi-year | 274 / 476 = 57.6% | 97 → 84 users, -13 (-13.4%) | High | Utilization is 57.6% and usage declined 13.4% from June to August. |
| C-0BCDB8C2 | Cole Ingram | $54,427.00 | 2026-09-18 | Chargebee; disagreement: ChurnZero 2027-09-18 vs. Chargebee 2026-09-18; multi-year | 232 / 424 = 54.7% | 127 → 110 users, -17 (-13.4%) | High | Utilization is 54.7% and usage declined 13.4% from June to August. |
| C-0D2AB865 | Elena Sinclair | $38,022.00 | 2026-09-22 | Chargebee; disagreement: ChurnZero 2026-09-10 vs. Chargebee 2026-09-22; multi-year | 250 / 407 = 61.4% | 125 → 109 users, -16 (-12.8%) | Medium | Utilization is 61.4% and usage declined 12.8% from June to August. |
| C-0BBE3E60 | Dana Mercer | $30,993.00 | 2026-09-26 | Chargebee; no disagreement: both systems show 2026-09-26 | 74 / 114 = 64.9% | 39 → 33 users, -6 (-15.4%) | Medium | Usage declined 15.4% from June to August despite 64.9% seat utilization. |
| C-0F5D2323 | Cole Ingram | $90,647.00 | 2026-09-29 | Chargebee; disagreement: ChurnZero 2026-09-10 vs. Chargebee 2026-09-29; multi-year | 111 / 390 = 28.5% | 20 → 18 users, -2 (-10.0%) | High | Utilization is only 28.5% and usage declined 10.0% from June to August. |
| C-0EC6999D | Elena Sinclair | $79,419.00 | 2026-10-03 | Chargebee; no disagreement: both systems show 2026-10-03 | 31 / 112 = 27.7% | 17 → 15 users, -2 (-11.8%) | High | Utilization is only 27.7% and usage declined 11.8% from June to August. |
| C-0B20DB64 | Dana Mercer | $21,770.00 | 2026-10-07 | Chargebee; no disagreement: both systems show 2026-10-07 | 214 / 378 = 56.6% | 294 → 294 users, 0 (0.0%) | Medium | Usage is flat, but seat utilization is only 56.6%. |
| C-0BBC4E7A | Cole Ingram | $56,374.00 | 2026-10-10 | Chargebee; no disagreement: both systems show 2026-10-10 | 228 / 337 = 67.7% | 142 → 139 users, -3 (-2.1%) | Medium | Utilization is 67.7% and usage declined 2.1% from June to August. |
| C-0FD551AB | Elena Sinclair | $48,815.00 | 2026-10-14 | Chargebee; no disagreement: both systems show 2026-10-14 | 210 / 376 = 55.9% | 123 → 126 users, +3 (+2.4%) | Medium | Usage increased 2.4%, but seat utilization remains 55.9%. |
| C-0F9F8F13 | Dana Mercer | $46,230.00 | 2026-10-18 | Chargebee; no disagreement: both systems show 2026-10-18 | 199 / 352 = 56.5% | 185 → 182 users, -3 (-1.6%) | Medium | Utilization is 56.5% and usage declined slightly by 1.6%. |
| C-0BC34584 | Cole Ingram | $16,740.00 | 2026-10-22 | Chargebee; no disagreement: both systems show 2026-10-22 | 327 / 494 = 66.2% | 104 → 106 users, +2 (+1.9%) | Low | Usage increased 1.9% and utilization is 66.2%. |
| C-0B7A7546 | Elena Sinclair | $35,062.00 | 2026-10-25 | Chargebee; no disagreement: both systems show 2026-10-25 | 182 / 205 = 88.8% | 64 → 63 users, -1 (-1.6%) | Medium | Utilization is strong at 88.8%, but usage declined 1.6%. |
| C-0B369871 | Dana Mercer | $85,128.00 | 2026-10-29 | Chargebee; no disagreement: both systems show 2026-10-29 | 317 / 422 = 75.1% | 326 → 333 users, +7 (+2.1%) | Low | Utilization is 75.1% and usage increased 2.1%. |
| C-0B144C78 | Cole Ingram | $30,899.00 | 2026-11-02 | Chargebee; no disagreement: both systems show 2026-11-02 | 169 / 224 = 75.4% | 101 → 106 users, +5 (+5.0%) | Low | Utilization is 75.4% and usage increased 5.0%. |
| C-0FC4DBB8 | Elena Sinclair | $94,732.00 | 2026-11-05 | Chargebee; no disagreement: both systems show 2026-11-05 | 356 / 464 = 76.7% | 189 → 193 users, +4 (+2.1%) | Low | Utilization is 76.7% and usage increased 2.1%. |
| C-0D5BBE3A | Dana Mercer | $39,740.00 | 2026-11-09 | Chargebee; no disagreement: both systems show 2026-11-09 | 85 / 102 = 83.3% | 88 → 91 users, +3 (+3.4%) | Low | Utilization is 83.3% and usage increased 3.4%. |
| C-0FB9D5AF | Cole Ingram | $63,158.00 | 2026-11-13 | Chargebee; no disagreement: both systems show 2026-11-13 | 144 / 199 = 72.4% | 173 → 176 users, +3 (+1.7%) | Low | Utilization is 72.4% and usage increased 1.7%. |
| C-0B344485 | Elena Sinclair | $64,384.00 | 2026-11-16 | Chargebee; no disagreement: both systems show 2026-11-16 | 224 / 287 = 78.0% | 238 → 244 users, +6 (+2.5%) | Low | Utilization is 78.0% and usage increased 2.5%. |
| C-0CB2C1B4 | Dana Mercer | $40,628.00 | 2026-11-20 | Chargebee; no disagreement: both systems show 2026-11-20 | 386 / 473 = 81.6% | 47 → 49 users, +2 (+4.3%) | Low | Utilization is 81.6% and usage increased 4.3%. |
| C-22170CA1 | Cole Ingram | $45,646.00 | 2026-11-24 | Chargebee; no disagreement: both systems show 2026-11-24 | 251 / 294 = 85.4% | 143 → 146 users, +3 (+2.1%) | Low | Utilization is 85.4% and usage increased 2.1%. |

Summary

- Total ARR renewing:
  $65,901 + $54,427 + $38,022 + $30,993 + $90,647 + $79,419 + $21,770 + $56,374 + $48,815 + $46,230 + $16,740 + $35,062 + $85,128 + $30,899 + $94,732 + $39,740 + $63,158 + $64,384 + $40,628 + $45,646 = $1,048,715.00

- ARR at risk, High + Medium:
  - High: $65,901 + $54,427 + $90,647 + $79,419 = $290,394.00
  - Medium: $38,022 + $30,993 + $21,770 + $56,374 + $48,815 + $46,230 + $35,062 = $277,266.00
  - Total ARR at risk: $290,394 + $277,266 = $567,660.00
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 $0.0095 · 33s · in 9 / out 3,413 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

show the model's full answer
Quarter total: 80 tickets.

Ranked by ARR exposure. Theme classification is based on ticket text, not existing tags.

1. HRIS provisioning failures — broad pattern
- Count: 12
- Share: 12 / 80 = 15.0%
- Distinct accounts: 3
- ARR affected: $36,000 + $30,000 + $48,000 = $114,000
- Ticket IDs: IC-460059, IC-460062
- Recommendation: Prioritize investigation of HRIS sync and provisioning failures, including skipped hires and missing provisioning errors.

2. Redemption and gift-card failures — broad pattern
- Count: 18
- Share: 18 / 80 = 22.5%
- Distinct accounts: 7
- ARR affected: $8,900 + $10,700 + $9,600 + $8,700 + $11,000 + $9,600 + $10,300 = $68,800
- Ticket IDs: IC-460025, IC-460024
- Recommendation: Stabilize checkout and gift-card fulfillment, including cases where points are deducted but the order or delivery fails.

3. Billing and invoice discrepancies — single-account noise
- Count: 16
- Share: 16 / 80 = 20.0%
- Distinct accounts: 1
- ARR affected: $52,000 = $52,000
- Ticket IDs: IC-460071, IC-460078
- Recommendation: Treat this as a concentrated account issue for C-0E9C27D1 and resolve the recurring seat-count and renewal-tier discrepancies directly.

4. Recognition points not posting — broad pattern
- Count: 20
- Share: 20 / 80 = 25.0%
- Distinct accounts: 9
- ARR affected: $3,500 + $4,500 + $4,500 + $2,700 + $3,400 + $4,200 + $2,900 + $2,500 + $2,900 = $31,100
- Ticket IDs: IC-460004, IC-460016
- Recommendation: Investigate the points-posting pipeline for both individual recognitions and team-wide posting delays.

5. Slack integration and command failures — broad pattern
- Count: 14
- Share: 14 / 80 = 17.5%
- Distinct accounts: 4
- ARR affected: $4,400 + $5,400 + $3,900 + $5,200 = $18,900
- Ticket IDs: IC-460041, IC-460047
- Recommendation: Fix Slack authentication persistence, recognition syncing, and slash-command errors across affected accounts.

Total ARR exposure calculated from distinct accounts within each theme: $114,000 + $68,800 + $52,000 + $31,100 + $18,900 = $284,800.
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 $0.0043 · 8s · in 3 / out 457 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 matching fields
   - Industry: Technology = Technology
   - Size band: Mid-Market = Mid-Market
   - Use case: employee_recognition = employee_recognition
   - Region: NA-East ≠ NA-West
   - Case study: true

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

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

Arithmetic: Each listed customer matches 3 of 4 similarity fields: 3 ÷ 4 = 75%.

These three are tied based on the provided data.
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 $0.0082 · 28s · in 6 / out 2,803 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 channel performance

| Channel | Spend | SQMs | SQOs | Cost/SQM | Cost/SQO | SQM-to-SQO | Pipeline | Pipeline / $ |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| paid_search | $36,000 | 34 | 18 | $36,000 ÷ 34 = $1,058.82 | $36,000 ÷ 18 = $2,000.00 | 18 ÷ 34 = 52.94% | $720,000 | $720,000 ÷ $36,000 = 20.00x |
| linkedin_ads | $24,000 | 25 | 8 | $24,000 ÷ 25 = $960.00 | $24,000 ÷ 8 = $3,000.00 | 8 ÷ 25 = 32.00% | $96,000 | $96,000 ÷ $24,000 = 4.00x |
| paid_social | $18,000 | 0 | 0 | Undefined: $18,000 ÷ 0 | Undefined: $18,000 ÷ 0 | Undefined: 0 ÷ 0 | $0 | $0 ÷ $18,000 = 0.00x |
| webinars | $9,000 | 12 | 5 | $9,000 ÷ 12 = $750.00 | $9,000 ÷ 5 = $1,800.00 | 5 ÷ 12 = 41.67% | $60,000 | $60,000 ÷ $9,000 = 6.67x |

Paid total

- Spend: $36,000 + $24,000 + $18,000 + $9,000 = $87,000
- SQMs: 34 + 25 + 0 + 12 = 71
- SQOs: 18 + 8 + 0 + 5 = 31
- Cost per SQM: $87,000 ÷ 71 = $1,225.35
- Cost per SQO: $87,000 ÷ 31 = $2,806.45
- SQM-to-SQO rate: 31 ÷ 71 = 43.66%
- Pipeline: $720,000 + $96,000 + $0 + $60,000 = $876,000
- Pipeline per dollar: $876,000 ÷ $87,000 = 10.07x

Organic performance

| Channel | Volume | SQOs | SQO rate | Pipeline |
|---|---:|---:|---:|---:|
| organic_search | 29 | 10 | 10 ÷ 29 = 34.48% | $90,000 |

Organic pipeline arithmetic:

- 10 SQOs × $9,000 = $90,000

Date-order flags

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

Recommendation

Reallocate spend away from paid_social first because it produced 0 SQMs, 0 SQOs, $0 pipeline, and 0.00x pipeline per dollar. Reallocate linkedin_ads next, or reduce it while testing additional spend in paid_search and webinars:

- paid_search had the strongest pipeline efficiency at 20.00x and the highest SQM-to-SQO rate at 52.94%.
- webinars had the lowest cost per SQM at $750 and cost per SQO at $1,800, with 6.67x pipeline per dollar.
- linkedin_ads generated 4.00x pipeline per dollar and includes two date-order anomalies.

Confidence: Moderate for stopping or reducing paid_social because its sample includes 0 SQMs across $18,000 of spend. Low-to-moderate for broader reallocation because SQO samples are small: 18 for paid_search, 8 for linkedin_ads, and 5 for webinars.
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 $0.0077 · 28s · in 6 / out 2,772 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

Rivally is a points-based recognition platform with an engaging recognition feed, focused on distributed and EU teams. [S02, S12, S16]

## Pricing

- Current public price: $7 per user/month for Recognition Starter, with annual billing required, as of 2026-08-12. [S17]
- Pricing history/conflict: the public price was previously listed at $5 per user/month on 2025-11-03 and 2026-04-01; the newer 2026-08-12 source supersedes those prices. [S03, S08, S17]
- Deal-specific quote: Rivally quoted $6.50 per user/month to a 500-seat prospect for an annual term on 2026-06-02. [S13]
- Deal-specific quote: a prospect reported a $7 per user/month list price with a 15% discount for a three-year term on 2026-08-14. [S18]
- Rivally Pulse is priced as a separate add-on rather than bundled with the core product. [S23]

## Where they win

- EU and distributed teams: EU reviewers praised Rivally’s strength with distributed EU teams and its multi-language support. [S12]
- EU data residency: Rivally announced EU data residency as generally available and opened a Dublin office. [S15]
- Fast implementation: a mid-market reviewer reported setup took under a week. [S04]
- Slack deployment: a reviewer reported that the Slack integration worked out of the box. [S04]
- User engagement: reviewers praised Rivally’s points-based recognition feed and described it as engaging. [S02, S16]
- Support: a G2 review praised support response times of under four hours. [S22]

## Where we win

- Analytics depth: an 800-seat prospect chose Bonusly over Rivally, citing analytics depth. [S25]
- Rivally’s analytics and reporting are identified as weaknesses: reviewers described its analytics as limited, its dashboards as basic compared with enterprise tools, and its exports as CSV-only. [S02, S07, S20]

## Objections and responses

- Objection: “Rivally supports EU teams better.”
  - Response: Confirm whether EU data residency and multilingual support are requirements, then position against those requirements. Rivally has EU data residency generally available and has received positive feedback for distributed EU teams and multilingual support. [S12, S15]

- Objection: “Rivally is quick to deploy.”
  - Response: Acknowledge the reported under-one-week setup, then test whether the prospect also requires enterprise administration, provisioning, and analytics. [S04, S10]

- Objection: “Rivally’s recognition experience is engaging.”
  - Response: Acknowledge the positive feedback on the recognition feed, then evaluate whether analytics depth and administrative controls carry greater weight. [S02, S16, S25]

- Objection: “Rivally has strong integrations.”
  - Response: Rivally’s Slack integration was reported to work out of the box, while its Microsoft Teams app v2 is in public preview. Validate the prospect’s required collaboration platforms and deployment status. [S04, S19]

- Objection: “Rivally is competitively priced.”
  - Response: Use the current public price of $7 per user/month as the baseline, confirm annual-term requirements, and distinguish public pricing from deal-specific quotes or discounts. [S13, S17, S18]

## Recent changes

- Rivally launched Rivally Pulse, a lightweight engagement-survey add-on. [S06]
- Rivally hired a former Workday VP for EMEA to lead European expansion. [S11]
- Rivally opened a Dublin office and announced EU data residency as generally available. [S15]
- Rivally updated Recognition Starter pricing from the previously listed $5 per user/month to $7 per user/month. The newer pricing-page source wins. [S08, S17]
- Rivally announced Microsoft Teams app v2 in public preview. [S19]
- Rivally Pulse exited beta and became a separately priced add-on rather than a bundled feature. [S23]

## 12-month win/loss record against Rivally

Coverage: 2025-09 through 2026-08. [Deal-7767F5, Deal-A9FD43, Deal-7AA785, Deal-44C524, Deal-0D0CD6, Deal-D5B790, Deal-5C636E, Deal-5645A5, Deal-C6FFAA, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-D263E0, Deal-935746, Deal-E46EAB, Deal-1D2392, Deal-9066A6, Deal-72A02F]

- Wins: 13
- Losses: 7
- Total: 20
- Arithmetic: 13 wins + 7 losses = 20 deals
- Win rate: 13 / 20 = 65%

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

Deal records were provided without snippet IDs; the deal aliases above are the citations for those claims.

## Unverified items from the old card

- “Rivally was acquired by WorkHuman in 2025.” Unverified; no provided snippet supports this claim.
- “Rivally lacks a Slack integration.” Unverified and contradicted by a review reporting that the Slack integration worked out of the box. [S04]
- “Points-based recognition for mid-market.” Partially supported: points-based recognition is supported, and a mid-market reviewer is present, but the provided data does not establish that Rivally’s overall positioning is specifically limited to mid-market. [S02, S04]
- “Strong in EU enterprise with multi-language support.” “Strong for distributed EU teams” and positive multilingual-support feedback are supported; the broader “EU enterprise” characterization is not fully sourced. [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 $0.0110 · 28s · in 9 / out 2,696 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
New Logo Nurture
- Sent: 1,386; opens: 490; replies: 90; meetings: 27.
- Rates: open = 490/1,386 = 35.35%; reply = 90/1,386 = 6.49%; meeting = 27/1,386 = 1.95%.
- Weakest step: Step 3 — 120/428 = 28.04% open, 18/428 = 4.21% reply, 6/428 = 1.40% meeting.
- Change: Replace Step 3 with a shorter, direct meeting CTA.

Expansion Nurture
- Sent: 875; opens: 565; replies: 59; meetings: 12.
- Rates: 565/875 = 64.57%; 59/875 = 6.74%; 12/875 = 1.37%.
- Weakest step: Step 3 — 95/275 = 34.55% open, 12/275 = 4.36% reply, 3/275 = 1.09% meeting.
- Change: Make Step 3 explicitly expansion-focused with a customer-outcome proof point.

Cold Outbound - HR Leaders
- Sent: 1,785; opens: 545; replies: 8; meetings: 0.
- Rates: 545/1,785 = 30.53%; 8/1,785 = 0.45%; 0/1,785 = 0.00%.
- Weakest step: Step 3 — 130/590 = 22.03% open, 1/590 = 0.17% reply.
- Change: Rebuild targeting and messaging before sending another Step 3.
- Failure mode: Every step is under 2% reply: 5/600 = 0.83%, 2/595 = 0.34%, 1/590 = 0.17%; zero meetings.

Cold Outbound - People Ops
- Sent: 1,163; opens: 340; replies: 29; meetings: 6.
- Rates: 340/1,163 = 29.23%; 29/1,163 = 2.49%; 6/1,163 = 0.52%.
- Weakest step: Step 3 — 80/377 = 21.22% open, 6/377 = 1.59% reply.
- Change: Replace Step 3 with a new value proposition and CTA.
- Failure mode: Step 3 is below 2% reply, indicating late-sequence message fatigue or weak relevance.

Tracking error
- Expansion Nurture Step 2: 340 opened / 300 sent = 113.33%; opens exceed sends.

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

Fix first: Cold Outbound - HR Leaders, because all three steps are below 2% reply and produced 0 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 $0.0054 · 13s · in 6 / out 1,102 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
Weekly marketing goals update — Q3-2026

Pace calculation: 66 days elapsed ÷ 92 days = 71.7% of the quarter. Pace-to-date targets are calculated as full-quarter target × 71.7%.

- SQMs: QTD actual 230; target 300; delta = 230 − 300 = −70; pace-to-date target = 300 × 66 ÷ 92 = 215.2; pace: ahead.
- SQOs: QTD actual 84; target 120; delta = 84 − 120 = −36; pace-to-date target = 120 × 66 ÷ 92 = 86.1; pace: behind.
- DS2s: QTD actual 40; target 75; delta = 40 − 75 = −35; pace-to-date target = 75 × 66 ÷ 92 = 53.8; pace: behind.
- Closed-lost MIA rate: QTD actual = 5 ÷ 25 = 20.0%; target 10.0%; delta = 20.0% − 10.0% = +10.0 percentage points; pace: behind because lower is better.
- Same-quarter closes: QTD actual 10; target 20; delta = 10 − 20 = −10; pace-to-date target = 20 × 66 ÷ 92 = 14.3; pace: behind.
- Active pipeline coverage against target: QTD actual = $3,000,000; target = $4,000,000; delta = $3,000,000 − $4,000,000 = −$1,000,000; pace-to-date target = $4,000,000 × 66 ÷ 92 = $2,869,565; pace: ahead.

What moved this week: The provided data contains only QTD totals and no prior-week totals, so week-over-week movement cannot be determined. Based on QTD pacing, SQMs and active pipeline are ahead, while SQOs, DS2s, closed-lost MIA rate, and same-quarter closes are behind.
communication
ceo-slack-compressionPASS 1.00

? 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 $0.0043 · 7s · in 3 / out 480 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
Use $115,976.75 as the mechanical Q3 weighted forecast: $44,729 COMMIT + 35% of $203,565 BEST_CASE ($71,247.75), with PIPELINE at $0. Do not treat it as reliable: 85 of 86 deals lack owners and 71 of 86 lack Why-buys, including all 7 COMMIT deals, so it cannot be rolled up by rep or checked against quota. Re-qualify 32 post-quarter deals totaling $227,575, including COMMIT Deal-D348E1 at $13,770.
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 $0.0038 · 5s · in 3 / out 137 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
Hi,

Following up on the August 5 recap with pricing for 150 seats. During the July 28 demo, your team reacted strongly to the automated milestone awards and points catalog.

Could you reply with whether this is still under consideration?

Best,
Alex
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 $0.0039 · 5s · in 3 / out 267 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 delivered 46 SQMs against a weekly target of 52, a shortfall of 6 SQMs (52 − 46 = 6). The webinar channel contributed 18 SQMs, while paid search held at 150 dollars per SQM.

Sales converted 14 SQOs and set 9 DS2 meetings, creating 310000 dollars in new pipeline. The team also recorded 3 same-quarter closes during the week.

CS saved 2 renewals this week, with Team NPS moving to 61. Three open red-flag accounts are heading into next week.

PLG added 412 new signups, with activation at 31 percent. A total of 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 $0.0048 · 8s · in 6 / out 626 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 — Active: 2 sourced opps totaling $275,000 ($180,000 + $95,000). Co-webinar locked for 09-15; both opps logged with UTM Source = Partner.
- HRCloud Partners — Active: 1 sourced opp totaling $140,000. Integration referral moved to DS2 after completing security review.
- CultureBridge — Active: 2 sourced opps totaling $135,000 ($60,000 + $75,000). Both are early stage.
- WorkWell Group — Active: 0 sourced opps totaling $0. Joint playbook restart planned for Q4; planning call booked for 09-09.
- Recogniq — Quiet.
- KudosWave — Quiet.
- PeopleFirst Advisors — Quiet.
- TotalPerk — Quiet.

Total partner-sourced pipeline: 5 opps totaling $550,000 ($180,000 + $95,000 + $140,000 + $60,000 + $75,000).
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 $0.0042 · 6s · in 3 / out 424 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. We maintain a SOC 2 Type II report, renewed annually. The report is available to prospects under NDA. [P2]

Q4: Customer data is retained for the duration of the contract plus 90 days. After that, it is 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 certification document or certification statement would be required. [P1–P8]

Q9: Unanswerable from the provided excerpts. The contractual uptime SLA or service-level agreement would be required. [P1–P8]

Q10: Unanswerable from the provided excerpts. A HIPAA compliance document or Business Associate Agreement policy would be required. [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 $0.0272 · 46s · in 6 / out 4,742 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
Findings

1. WARNING — REVIEW — ALWAYS-trigger overlap: `comms-drafter` and `email-drafter`
- Both trigger on writing, drafting, reviewing, or improving external/customer-facing communications.
- Overlapping phrases include `"write me an email"`, `"draft a follow-up"`, `"help me reply"`, `"what should I say"`, `"review"`, and `"rewrite"`.
- Proposal: designate `email-drafter` as the email-specific skill and `comms-drafter` as the non-email communications skill, or merge them.

2. WARNING — REVIEW — ALWAYS-trigger overlap: `pipeline-intelligence-report` and `weekly-pipeline-report`
- Overlapping phrases include `"pipeline update"`, `"pipeline report"`, `"pipeline performance"`, and `"what's the pipeline look like"`.
- `pipeline-intelligence-report` covers full scored/tiered pipeline intelligence; `weekly-pipeline-report` covers weekly pipeline performance reporting.
- Proposal: narrow `weekly-pipeline-report` to weekly performance/MTD reporting and reserve scored/tiered pipeline requests for `pipeline-intelligence-report`.

3. WARNING — REVIEW — ALWAYS-trigger overlap: `analysis-validator` and `signalforge-claim-compressor`
- Both trigger on SignalForge analysis/report outputs and quantitative or stakeholder-facing reports.
- `analysis-validator` is the QA gate; `signalforge-claim-compressor` is the final style pass.
- Proposal: retain both but make the sequencing boundary explicit: validation first, compression second.

4. WARNING — REVIEW — ALWAYS-trigger overlap: `signalforge-claim-compressor` and `signalforge-feedback`
- Both trigger on SignalForge reports, analyses, findings, intelligence reports, and forecast outputs.
- `signalforge-feedback` explicitly runs after `signalforge-claim-compressor`, so the overlap is intentional but broad.
- Proposal: define `signalforge-feedback` as a terminal post-delivery step rather than a general output trigger.

5. CRITICAL — REVIEW — ALWAYS-trigger overlap: `model-selection` and every other `ALWAYS`-triggered skill
- `model-selection` says `"ALWAYS run this skill at the start of every task"` and `"before any other skill"`.
- Other skills also claim unconditional execution for their domains, including `analysis-validator`, `next-to-close`, `partner-digest`, `pipeline-intelligence-report`, `sales-forecast`, `signalforge-claim-compressor`, `signalforge-feedback`, `stale-pipeline-report`, and `weekly-pipeline-report`.
- Proposal: define `model-selection` as a prerequisite/orchestrator rule, not a competing domain trigger.

6. CRITICAL — REVIEW — Circular delegation chain
- `deal-strategy-coach` → `email-drafter` for manager-to-prospect emails.
- `email-drafter` → `deal-strategy-coach` for strategic deal coaching.
- `comms-drafter` also routes deep deal strategy to `deal-strategy-coach`.
- Proposal: make `deal-strategy-coach` the strategy owner and `email-drafter` the execution owner; prohibit reverse delegation from `email-drafter` when only strategy is requested.

7. CRITICAL — UPDATE_BODY — Dangling delegation targets
The following referenced skills/files do not have manifest rows in the supplied manifest:

- `bonusly-brand`
- `prospect-research-multithreading`
- `bonusly-data-questions`
- `bonusly-product-questions`
- `bonusly-business-reporting-questions`
- `bonusly-rewards-questions`
- `bonusly-ppp-questions`
- `bonusly-feature-flag-questions`
- `bonusly-deal-desk-questions`
- `bonusly-datadog-questions`
- `CUSTOMER_DATA_REFERENCE`
- `HUBSPOT_CONNECTOR_REFERENCE`
- `SIGNALFORGE_PRODUCT_INSIGHT_SKILL`
- `skill-orchestrator`
- `signalforge-reports`

Proposal: add each target to the manifest or replace each reference with an existing manifest skill.

8. WARNING — UPDATE_BODY — Version conflict in `analysis-validator`
- Current version: `3.6`.
- The validation trail still says: `analysis-validator v3.2`.
- The footer in `pipeline-intelligence-report` also references `analysis-validator v3.6`.
- Proposal: retain `analysis-validator` v3.6 and update the stale v3.2 reference.

9. INFO — TRIM_DESC — Manifest descriptions exceeding 1,024 characters
Arithmetic:

`0` descriptions exceed `1,024` characters.

All 14 manifest descriptions are at or below the limit. Maximum listed length:

`max(656, 897, 996, 792, 965, 676, 945, 1004, 1006, 962, 1006, 708, 762, 656) = 1006`

10. WARNING — UPDATE_BODY — Hardcoded dates and person names in `analysis-validator`
- Dates include `April 26, 2026`, `May 4, 2026`, and `May 9, 2026`.
- Person names include `Manish` and `Amani`.
- Proposal: replace date- and roster-specific body content with live references or clearly labeled historical examples.

11. WARNING — UPDATE_BODY — Hardcoded dates and company aliases in `closed-lost-analysis`
- Date references include `May 2026`.
- Named aliases include `Softheon`, `Estee Lauder`, `LIFTOFF`, `Nestlé`, `Ozinga`, `MinIO`, `Aurora Innovation`, `GCash`, `Ethos Cannabis`, and `StickerYou`.
- Proposal: label these as historical examples or move them to a dated reference source.

12. WARNING — UPDATE_BODY — Hardcoded page ID, dates, and person names in `deal-strategy-coach`
- Page ID: `2257879045`.
- Dates include `2026`, `April 2026`, and `April 27, 2026`.
- Person name: `Alaina`.
- Proposal: replace the page ID with a maintained reference and remove or explicitly label dated/person-specific content.

13. WARNING — UPDATE_BODY — Hardcoded model-registry date in `model-selection`
- Date: `2026-05-19`.
- Proposal: make `last_checked` runtime-managed or explicitly mark the registry as historical.

14. WARNING — UPDATE_BODY — Hardcoded dates, page IDs, and person names in `partner-digest`
- Page IDs include `2286616609`, `2286321666`, `2265382925`, `2236940297`, `2237825028`, `2239365136`, and `2238283777`.
- Dates include `May 16, 2026`, `May 19, 2026`, `June 2, 2026`, and `2026`.
- Person names include `Amani Phipps`, `Kelli`, `Jen Lee`, `Hani`, `Bryce`, and `Sara`.
- Proposal: move destination IDs and partner contacts to maintained configuration; make dates runtime-derived.

15. WARNING — UPDATE_BODY — Hardcoded dates and person names in `pipeline-intelligence-report`
- Dates include `May 2026` and `May 4, 2026`.
- Person names include `Bryce Harmon`, `Dana Mercer`, `Cole Ingram`, `Alex Franklin`, and `Gavin Porter`.
- Proposal: resolve owners dynamically and replace dated roster content with runtime lookups.

16. WARNING — UPDATE_BODY — Hardcoded page IDs, dates, and person name in `sales-forecast`
- Page IDs include `2232811524` and `2232582148`.
- Dates include `April 1 – June 30, 2026`, `April 27, 2026`, and `2026`.
- Person name: `Alaina`.
- Proposal: derive quarter, owner, and Confluence destinations dynamically.

17. WARNING — UPDATE_BODY — Hardcoded page IDs and date placeholders in `signalforge-feedback`
- Page IDs include `2295136266`, `2232811524`, `2234417154`, and `2247295002`.
- Date placeholders include `[DATE]` and `[YYYY-MM-DD]`.
- Proposal: maintain page destinations in configuration and generate dates at runtime.

18. WARNING — UPDATE_BODY — Hardcoded dates in `stale-pipeline-report`
- Dates include `2026-06-10`.
- Proposal: make changelog dates historical metadata only and derive all operational dates from runtime.

19. WARNING — UPDATE_BODY — Hardcoded date, person name, and historical quarter in `weekly-pipeline-report`
- Date range: `April 1 – June 30, 2026`.
- Date references include `2026`.
- Person name: `Ben Lavin`.
- Proposal: derive the reporting quarter and recipient dynamically, or clearly label this as a Ben-specific version.

20. INFO — REVIEW — Manifest drift
- Files with no manifest row: none identifiable from the supplied file inventory.
- Manifest rows with no corresponding supplied file: none.
- Proposal: no change based on the supplied data; a directory-level check is required to detect files not included in the prompt.
runbook-from-slack-threadPASS 1.00

? 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 $0.0049 · 9s · in 3 / out 978 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
# Reward Queue Incident Runbook

1. Take incident ownership
   - Message: M01
   - Who: Bryce Harmon
   - Action: Acknowledge the PagerDuty alert and take IC.
   - Exact command/action: PagerDuty alert acknowledged; Bryce Harmon took IC.
   - Success verification: Not stated.
   - Rollback: Not applicable.

2. Check reward queue depth
   - Message: M02
   - Who: Farid Osman
   - Exact command:
     ```bash
     bundle exec rake sidekiq:queue_depth
     ```
   - Result: Reward queue reported 48,213 pending jobs.
   - Success verification: Command returned the queue depth.
   - Rollback: Not applicable.

3. Inspect the dead set
   - Message: M03
   - Who: Farid Osman
   - Action: Check the dead set.
   - Result: 112 jobs were present, all `Redis::TimeoutError` from around 13:58.
   - Success verification: Not stated.
   - Rollback: Not applicable.

4. Pause reward enqueue
   - Message: M04
   - Who: Farid Osman
   - Exact command:
     ```bash
     bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'
     ```
   - Action: Disable `:auto_recognition_enqueue` to stop the bleed.
   - Success verification: Not stated; needs confirmation.
   - Rollback:
     ```bash
     bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
     ```

5. Clear the dead set
   - Message: M05
   - Who: Elena Sinclair
   - Action: Clear out the dead set while in the console.
   - Exact command/action: The exact command or console action is not provided; needs confirmation.
   - Success verification: Not stated; needs confirmation.
   - Rollback: Not provided; needs confirmation.

6. Scale reward workers up
   - Message: M06
   - Who: Bryce Harmon
   - Exact command:
     ```bash
     kubectl scale deployment/reward-worker --replicas=6
     ```
   - Action: Increase `reward-worker` from 3 to 6 replicas.
   - Success verification: Not stated at the time of scaling; needs confirmation.
   - Rollback:
     ```bash
     kubectl scale deployment/reward-worker --replicas=3
     ```

7. Check queue recovery
   - Message: M07
   - Who: Farid Osman
   - Action: Check queue depth and rate of decrease.
   - Result: Queue depth was 9,400 and falling by approximately 1,200 per minute.
   - Success verification: Queue was decreasing.
   - Rollback: Not applicable.

8. Verify the queue is drained and errors have recovered
   - Message: M08
   - Who: Cole Ingram
   - Exact command:
     ```bash
     bundle exec rake sidekiq:queue_depth
     ```
   - Verification:
     - Command returned 0.
     - Error rate in Datadog was back to baseline.
   - Rollback: Not applicable.

9. Re-enable reward enqueue
   - Message: M09
   - Who: Bryce Harmon
   - Exact command:
     ```bash
     bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
     ```
   - Verification: 40 new jobs processed cleanly in the next 3 minutes.
   - Rollback: The thread does not provide a rollback command; needs confirmation.

10. Scale reward workers back down
    - Message: M10
    - Who: Bryce Harmon
    - Exact command:
      ```bash
      kubectl scale deployment/reward-worker --replicas=3
      ```
    - Verification: Queue was stable at 0.
    - Rollback: The thread does not provide a rollback command from 3 replicas; needs confirmation.
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 $0.0065 · 18s · in 6 / out 1,776 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
- Service: `reward-service`
- Error: `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`
- This is the first error in the log slice.

Failure cascade

1. Redis timeout affects `reward-service` at 14:01:12Z.
2. `reward-service` retries `RewardGiveJob`; retries are exhausted at 14:01:20Z, 14:01:30Z, and 14:01:40Z.
3. `sidekiq` reports `RewardGiveJob failed: Redis::TimeoutError; retrying in 60s` at 14:01:40Z.
4. The job data confirms `RewardGiveJob` failures, including:
   - `J-00005` at 14:01:46Z
   - `J-00001` at 14:01:51Z
   - `J-00003` at 14:01:54Z
   - `J-00006` at 14:01:55Z
   - `J-00002` at 14:01:56Z
   - `J-00004` at 14:01:57Z
5. `sidekiq` reports the reward queue above 10,000 at 14:02:30Z.
6. `api-gateway` returns `502 upstream timeout` calling `reward-service /gives` at 14:03:05Z.
7. `web-app` reports give-form submission failure from the upstream 502 at 14:03:30Z.
8. Additional `api-gateway` 502 errors and `web-app` submission failures continue.
9. `RecognitionDigestJob` also fails with `Redis::TimeoutError`, beginning with `J-00013` at 14:02:36Z and continuing through `J-00016` at 14:05:50Z.
10. `reward-service` reports Redis restored and resumes job processing at 14:22:10Z.
11. `sidekiq` reports the reward queue below 500 at 14:24:45Z.

Timing arithmetic from the first error:

- First Sidekiq retry: 14:01:40 − 14:01:12 = 28 seconds
- Queue above 10,000: 14:02:30 − 14:01:12 = 78 seconds
- First gateway 502: 14:03:05 − 14:01:12 = 113 seconds
- First web-app failure: 14:03:30 − 14:01:12 = 138 seconds
- Redis restored: 14:22:10 − 14:01:12 = 1,258 seconds, or 20 minutes 58 seconds
- Queue below 500: 14:24:45 − 14:01:12 = 1,413 seconds, or 23 minutes 33 seconds

Datadog query

Use this query and sort the results by timestamp ascending:

`service:reward-service level:ERROR "Redis::TimeoutError" @timestamp:[2026-09-03T14:00:00Z TO 2026-09-03T14:05:00Z]`

The first matching event should be the 2026-09-03T14:01:12Z timeout.

What the logs do not show

- They do not identify why the Redis connection timed out.
- They do not show whether `redis-primary` itself failed, became overloaded, or experienced a network issue.
- They do not show the number of successful or permanently lost rewards.
- They do not identify which users or reward transactions were affected.
- They do not show whether queued jobs were eventually completed after recovery.
- They do not establish that PostgreSQL caused or contributed to the failure; the PostgreSQL entries shown are only `checkpoint complete` messages.
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 $0.0049 · 11s · in 6 / out 753 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
- recognition_streaks_v2 — ON; controls recording recognition streaks via `StreakTracker.record(give)`. Target: `segment:beta_companies`. Export count: 42. Code reference: yes.
- points_budget_guardrails — ON; controls enforcement of points budgets via `BudgetService.new(company).enforce!(giver, points)`. Target: `all_companies`. Export count: 220. Code reference: yes.
- slack_dm_nudges — ON; controls Slack direct-message nudges via `SlackDm.send_nudge(user)`. Target: `segment:region_na`. Export count: 87. Code reference: yes.
- redeem_flow_redesign — OFF; controls whether the company receives `RedeemV2Component` instead of `RedeemV1Component`. Target: `targeted_list`. Export count: 12. Code reference: yes.
- analytics_dashboard_v3 — ON; controls use of `AnalyticsV3` for the dashboard. Target: `segment:tier_three`. Export count: 65. Code reference: yes.
- ms_teams_app_v2 — OFF; controls installation of `TeamsAppV2`. Target: `targeted_list`. Export count: 9. Code reference: yes.
- legacy_give_modal — OFF; targeting: `segment:legacy_plan`. Export count: 14. No code reference in the excerpt.
- survey_boosters_q3 — ON; targeting: `segment:legacy_plan`. Export count: 7. No code reference in the excerpt.
- paused_offboard_cleanup — OFF; no targeting rules; export count: 0. No code reference in the excerpt and no targets.

Company-count arithmetic:
42 + 220 + 87 + 12 + 65 + 9 + 14 + 7 + 0 = 456 total exported companies.

ON flags: 42 + 220 + 87 + 65 + 7 = 421.
OFF flags: 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 $0.0042 · 6s · in 3 / out 422 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 confidentiality with stated carve-outs, a two-year term, Delaware governing law, and no license, exclusivity, or obligation to proceed. Driving clauses: “Mutual confidentiality”; “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”; “Governing law: the laws of the State of Delaware, USA.”

NDA-2 — YELLOW: Counsel review is required because it contains an embedded 18-month employee non-solicitation and hiring restriction. Driving clause: “During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.”

NDA-3 — RED: Full legal review is required because it contains a broad three-year non-compete, one-way confidentiality favoring [PARTY C], no stated confidentiality carve-outs, and unusual Ireland governing law with exclusive court jurisdiction. Driving clauses: “For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business”; “One-way confidentiality in favor of [PARTY C]”; “No confidentiality carve-outs are stated in this Agreement”; “This Agreement shall be governed by and construed in accordance with the laws of the Republic of Ireland, and the parties submit to the exclusive jurisdiction of its courts.”