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openai/gpt-6-sol-pro

Mean 0.921 · 29/40 perfect tests · $15.93 total · modalities in: text, image, file · out: text · each card: the prompt → the correct answer (gold) → this model's actual answer

What do the modalities mean?

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

data-crm 5 tests
stage-hygiene-audit0.83
Plain-English question: How much of the open DS2+ pipeline is missing a substantive why-buys, and is anything missing DS1 timestamps or out of order?
Prompt — what the model saw
Audit stage hygiene on the open pipeline below. Rules: a deal at DS2 or later must have a substantive why-buys (15+ characters, not a placeholder such as tbd, n/a, see notes). A why_buys value of the form LONG(n) means the real text was present but truncated for display; treat it as present and substantive. Every deal should have a DS1 entry timestamp. A deal whose stage entry timestamps are out of order regressed (an earlier-stage timestamp is later than a later-stage timestamp). Report: percent of DS2+ deals non-compliant on why-buys overall and by owner (use the owners table for names), count of deals with no DS1 timestamp, count that regressed, and three process fixes. State definitions before numbers.
cost $0.6442 · 98s · in 218,327 / out 6,151 tok
✓ noncomp_pct✓ no_ds1_0✗ regressed_0✓ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

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

Model's actual answer

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Definitions: DS2+ means a deal currently at DS2, DS3, DS4, or DS5. A why-buys entry is compliant if it has substantive text of at least 15 characters; `LONG(n)` counts as substantive. Blank or placeholder entries are non-compliant. “No DS1 timestamp” means blank `t_ds1`. A regression means a populated earlier-stage entry timestamp is later than a populated later-stage entry timestamp; equal dates are not regressions.

Why-buys non-compliance:
- Overall: 101 / 124 × 100 = 81.5%
- Bryce Harmon: 15 / 18 × 100 = 83.3%
- Alex Franklin: 48 / 61 × 100 = 78.7%
- Dana Mercer: 18 / 22 × 100 = 81.8%
- Cole Ingram: 19 / 22 × 100 = 86.4%
- Farid Osman: 1 / 1 × 100 = 100.0%

Deals with no DS1 timestamp: 0. Deals with out-of-order stage entry timestamps: 0.

Three process fixes:
1. Require a 15+ character, non-placeholder why-buys entry before a deal can advance to DS2.
2. Make DS1 entry timestamp capture automatic and required when a deal enters DS1.
3. Run a recurring audit that flags missing why-buys and out-of-order stage timestamps for owner correction.
owner-resolutionPASS 1.00
Plain-English question: Every open deal has a blank owner name — resolve each one to a real person using the owners table.
Prompt — what the model saw
The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.
cost $0.2066 · 37s · in 77,138 / out 3,153 tok
✓ resolved_count✓ unmatched_none✓ deactivated_flagged✓ jake_amount✓ justin_amount✓ no_invented

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
All 156 open deals resolve to an owner in `owners.csv`: 35 + 67 + 24 + 22 + 7 + 1 = 156. No deals have an unmatched owner ID.

| Owner ID | Resolved owner | Deals | Total pipeline amount |
|---|---|---:|---:|
| 119337721 | Bryce Harmon | 35 | 1,054,144 |
| 84342457 | Alex Franklin | 67 | 624,310 |
| 83155923 | Dana Mercer | 24 | 341,195 |
| 83155924 | Cole Ingram | 22 | 288,161.43 |
| 716654662 | Farid Osman | 7 | 4,134 |
| 701163055 | Elena Sinclair | 1 | 2,100 |

Total amount: 1,054,144 + 624,310 + 341,195 + 288,161.43 + 4,134 + 2,100 = **2,314,044.43**.

Archived owner IDs in the owners table: **1520255671 — Gavin Porter** and **77260721 — Hugo Lindqvist**. Neither owns a deal in this extract.
gong-hubspot-join-integrityPASS 1.00
Plain-English question: How many call records point at deal ids that do not exist in the open pipeline?
Prompt — what the model saw
Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.5053 · 73s · in 107,676 / out 5,621 tok
✓ orphan_count✓ dup_keys_0✓ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

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

Model's actual answer

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(a) Orphans: 30 Gong rows have no deal-id match in `deals_open` (67 Gong rows − 37 matched rows = 30). Sample aliases: Deal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265.

(b) Duplicate conversation keys: 0 rows. In every Gong row, `calls_90d` equals `distinct_conversation_keys`.

(c) DS3-or-later call coverage: 25 of 85 open deals have at least one logged Gong call = 25 ÷ 85 × 100 = 29.4%. This uses current stage and matches `hs_deal_id` to `deal_id`, not Gong owner ID to HubSpot owner ID.
snowflake-sql-trapped-catalog0.50
Plain-English question: Write one Snowflake query for first-month usage per customer using only the provided, trap-laden catalog.
Prompt — what the model saw
Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.2056 · 38s · in 77,103 / out 2,089 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✗ uses_cohort_summary✗ succeeded_filter✓ no_giver_deletion_filter✗ returns_sql

✓ Correct Answer (ground truth)

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

Model's actual answer

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```sql
-- The provided catalog is insufficient to write this query without inventing columns.
-- COMPANY_COHORT_SUMMARY covers only self-serve companies, and its M1 metrics
-- measure days 0–30, not the first calendar month; M1_USERS is not unique givers.
-- The catalog does not provide a first-customer-date definition for every company,
-- or the company IDs, giver IDs, and event timestamps needed to count recognitions
-- and successful redemptions in that calendar month.
```
crm-completeness-enrichmentPASS 1.00
Plain-English question: Audit the CRM for missing and invalid fields, and propose the fixes.
Prompt — what the model saw
Audit the CRM extract for completeness and propose fixes. Deals need owner, stage, amount, close date, why-buys; companies need industry, employee count, HQ country; contacts need email, title, persona. Report completeness percent per field. Identify duplicate company clusters (name variants, shared domain) with a survivor per cluster. List invalid emails and domain mismatches. Fill missing company fields only where the enrichment export has a matching row; where CRM and enrichment disagree list both and recommend a source. Never invent a value. End with the 10 fixes with the most pipeline amount at stake.
cost $1.2171 · 677s · in 192,222 / out 75,711 tok
✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

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

Model's actual answer

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The extract contains 34 companies and 52 contacts. No deals file was provided, so deal-field completeness and pipeline amount at stake cannot be calculated.

Completeness (nonblank values ÷ rows):
- Deals — owner, stage, amount, close date, why-buys: not measurable; no deal rows supplied.
- Companies — industry: 34/34 = 100%; employee count: 25/34 = 73.5%; HQ country: 28/34 = 82.4%.
- Contacts — email: 52/52 = 100% nonblank, but only 48/52 = 92.3% syntactically valid; title: 39/52 = 75.0%; persona: 37/52 = 71.2%.

Duplicate-company candidates, based on shared domain (no company-name field was supplied):
- `acme-corp.com`: `C-0A092931` and `C-0A092932`. Proposed survivor: `C-0A092931` (first listed); verify associations before merging. Employee counts conflict: 500 versus 510. Neither value can be chosen from this extract.
- `globex.io`: `C-0A092933` and `C-0A092934`. Proposed survivor: `C-0A092933` (first listed); verify associations before merging. Industries differ: SaaS versus Technology. Preserve both for review rather than silently overwriting one.

Contact exceptions:
- Invalid emails: `CT-0010` `user0@`; `CT-0080` `user0@`; `CT-0081` `user1@`; `CT-0192` `user2@`. Obtain the correct addresses; the company domain is not proof of an address.
- Domain mismatch: `CT-0011` has `user1@other-domain.com`, while its associated company `C-66D1FC` has `66d1fc.com`. Verify the contact’s company association or email; do not automatically replace either.

Enrichment is matched by exact company domain. It supplies employee count 400 for each of `C-EC3025`, `C-96039F`, `C-44EA29`, `C-D04904`, `C-B23205`, `C-60C75F`, `C-7BBDFA`, and `C-50D386`. That would raise employee-count completeness to (25 + 8)/34 = 33/34 = 97.1%. `C-93C8BF` has no matching enrichment row, so its count remains unknown. None of the six missing HQ countries is filled by a matching nonblank enrichment value.

Populated CRM/enrichment differences should not be treated as missing-field fills:
- Industry: CRM `tech` or `Technology` versus enrichment `Computer Software` for `C-66D1FC`, `C-EC3025`, `C-44EA29`, `C-92D97D`, `C-D04904`, `C-77A95A`, `C-AA8DDA`, `C-B25F40`, `C-60C75F`; CRM `Tech ` versus enrichment `Computer Software` for `C-425E2A`. Recommend retaining the populated CRM value pending an agreed industry taxonomy; the enrichment label is more specific, not proof that CRM is wrong.
- HQ country: CRM `US` versus enrichment `United States` for `C-66D1FC`, `C-950043`, `C-77A95A`, `C-B23205`, `C-D0662E`; CRM `USA` versus enrichment `United States` for `C-EC3025`, `C-96039F`, `C-E51FB7`, `C-425E2A`, `C-2D7423`. These are formatting differences; recommend normalizing to one CRM country convention, not changing the underlying country.
- No populated CRM employee count differs from its matching enrichment row.

Ten fixes to queue (unranked): no deal amounts or deal-to-company associations were supplied, so the “most pipeline amount at stake” ordering cannot be determined.
1. Resolve and, after association review, merge the `acme-corp.com` cluster into proposed survivor `C-0A092931`; resolve 500 versus 510.
2. Resolve and, after association review, merge the `globex.io` cluster into proposed survivor `C-0A092933`; resolve SaaS versus Technology.
3. Fill the eight missing employee counts listed above from their matching enrichment rows.
4. Obtain an employee count for `C-93C8BF`; enrichment has no match.
5. Research HQ country for `C-2D1F1B`, `C-D73B89`, `C-44EA29`, `C-D04904`, `C-2C60E5`, and `C-EE9FFB`; leave blank until verified.
6. Correct the four invalid contact emails listed above from verified addresses.
7. Verify the email or company association for `CT-0011`.
8. Obtain titles for `CT-0000`, `CT-0022`, `CT-0072`, `CT-0080`, `CT-0081`, `CT-0092`, `CT-0120`, `CT-0121`, `CT-0122`, `CT-0132`, `CT-0141`, `CT-0162`, and `CT-0170`.
9. Obtain personas for `CT-0000`, `CT-0022`, `CT-0041`, `CT-0060`, `CT-0070`, `CT-0081`, `CT-0082`, `CT-0092`, `CT-0110`, `CT-0132`, `CT-0162`, `CT-0171`, `CT-0172`, `CT-0180`, and `CT-0181`.
10. Supply the deals extract, including owner, stage, amount, close date, why-buys, and company association, to audit deal completeness and rank these fixes by pipeline amount.
deal-intelligence 5 tests
closed-lost-classification0.80
Plain-English question: Why did we lose the last six months of deals — one primary category per deal, and whose side is the reason on?
Prompt — what the model saw
Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.
cost $0.5942 · 153s · in 137,244 / out 13,802 tok
✓ total_90✗ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

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

Model's actual answer

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Classification of the 90 supplied deals is below. Each deal has one primary category; the side shown applies to every alias on that line. “Unknown” means the tag and text do not establish which side drove the loss. A competitor tag is retained where the text is nonspecific, but it does not establish which competitor won.

| Primary category | Side | Count | Deal aliases |
|---|---|---:|---|
| Competitor | Buyer | 25 | Deal-F7F635, Deal-F97C37, Deal-422BA6, Deal-381C8C, Deal-F1E8A6, Deal-DDAB52, Deal-ACE061, Deal-2D2F8D, Deal-0F96AA, Deal-1BCA50, Deal-7CC678, Deal-242273, Deal-A2C349, Deal-C7156E, Deal-8A0992, Deal-D0C698, Deal-EECC02, Deal-5AD03E, Deal-47F1A1, Deal-BF2A98, Deal-1E7DA9, Deal-286F9C, Deal-369281, Deal-9FCD0D, Deal-64B19A |
| Timing | Buyer | 23 | Deal-DB0AAC, Deal-91A056, Deal-29326C, Deal-831B7B, Deal-39E25C, Deal-B3ABED, Deal-B6AC09, Deal-E6E80A, Deal-B038F0, Deal-175756, Deal-BB78F3, Deal-15DA99, Deal-F4AF5D, Deal-79B7A1, Deal-9F176A, Deal-5E64CE, Deal-69CF3D, Deal-ECBF89, Deal-D1A623, Deal-FEDBCB, Deal-55867E, Deal-DAFB82, Deal-2FEDDB |
| No decision | Buyer | 10 | Deal-13E9CF, Deal-E74A73, Deal-8E27DA, Deal-FAC17C, Deal-50E5D8, Deal-413C56, Deal-2A292B, Deal-7FBAC6, Deal-F325A5, Deal-ABD14C |
| Pricing | Buyer | 4 | Deal-7ED004, Deal-7B2236, Deal-C33D91, Deal-8A119B |
| Product gap | Bonusly | 4 | Deal-9048EB, Deal-3618CC, Deal-981AD4, Deal-DC77FE |
| Champion left | — | 0 | — |
| Other | Unknown | 24 | Deal-AC944F, Deal-214060, Deal-5DB9B0, Deal-21B045, Deal-ED9AE7, Deal-988493, Deal-F308CA, Deal-70F704, Deal-4664E1, Deal-D48E0B, Deal-583ADB, Deal-E0441F, Deal-7CB44D, Deal-AFA56C, Deal-D1AABF, Deal-2BBA21, Deal-386F6E, Deal-3F86A0, Deal-096750, Deal-79E61A, Deal-AE7C4E, Deal-DAB4F1, Deal-B4B50F, Deal-5885B9 |

Category check: 25 + 23 + 10 + 4 + 4 + 0 + 24 = 90. Side split: buyer 62 + Bonusly 4 + unknown 24 = 90.

Clear tag/text disagreements: 2 — Deal-8E27DA (“Feature Request” versus a decision to use only a swag provider and not R&R) and Deal-3618CC (“Lost DM” versus “Wanted Surveys”). Mixed or underspecified tags were not counted as clear contradictions.

Two patterns worth acting on:
1. Competitive losses are the largest identified category (25). Capture the winning vendor and deciding requirement consistently: several competitor-tagged entries say only “another direction” or provide no specific reason, while Deal-422BA6, Deal-DDAB52, and Deal-DC77FE give actionable differentiators.
2. Timing and no-decision losses total 23 + 10 = 33. Separate dated deferrals (such as Deal-91A056 and Deal-B6AC09) from paused or deprioritized initiatives (such as Deal-13E9CF and Deal-7FBAC6) when setting follow-up plans.
pipeline-tieringPASS 1.00
Plain-English question: Score all 156 open deals into exactly one of LOCK / ACTION / BUILD / REVIVE / WATCH / RISKY.
Prompt — what the model saw
Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $1.2607 · 156s · in 341,594 / out 7,381 tok
✓ json_returned✓ tier_vocabulary✓ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
{
  "tier_counts": {
    "LOCK": 6,
    "ACTION": 38,
    "BUILD": 21,
    "REVIVE": 17,
    "WATCH": 48,
    "RISKY": 26
  },
  "tier_examples": {
    "LOCK": ["Deal-25F752", "Deal-D348E1", "Deal-C26D20"],
    "ACTION": ["Deal-1FC049", "Deal-C6FE92", "Deal-D73B89"],
    "BUILD": ["Deal-012CB1", "Deal-523604", "Deal-C9C286"],
    "REVIVE": ["Deal-2D1F1B", "Deal-66D1FC", "Deal-950043"],
    "WATCH": ["Deal-6787C2", "Deal-E25A09", "Deal-FC22A3"],
    "RISKY": ["Deal-E53952", "Deal-5408B0", "Deal-9AAE5F"]
  },
  "risky_deals": [
    "Deal-E53952", "Deal-5408B0", "Deal-9AAE5F", "Deal-547B2B",
    "Deal-B7EBD1", "Deal-A2B47C", "Deal-2465CE", "Deal-C61CF7",
    "Deal-62D607", "Deal-584EE5", "Deal-C6D97A", "Deal-7B3B0F",
    "Deal-A5E80A", "Deal-F9A08A", "Deal-0660B4", "Deal-FD9F4E",
    "Deal-499BF6", "Deal-BA571A", "Deal-7BBDFA", "Deal-4A13AD",
    "Deal-9D0060", "Deal-690476", "Deal-635B8E", "Deal-5FDCE4",
    "Deal-F336B6", "Deal-BA3DDC"
  ],
  "lock_violations": 0,
  "pipeline_shape": "Only 6 deals qualify as LOCK; WATCH and RISKY account for 48 + 26 = 74. Counts reconcile: 6 + 38 + 21 + 17 + 48 + 26 = 156. Engagement rows are missing for Deal-3EED2C and Deal-57FF13, so both remain WATCH rather than being scored as having zero meetings."
}
call-transcript-extractionPASS 1.00
Plain-English question: Extract the CRM write-back fields from each call transcript as JSON.
Prompt — what the model saw
For each transcript, extract CRM write-back fields as JSON: why-buys (prospect statements only), pain points, stakeholders from the speaker list, budget signal (prospect-stated or null), timeline signal, competitor mentioned (only if the prospect raised it), next step (only if explicitly agreed), objections, confidence. Never fill a field from what the rep said or hoped.
cost $0.2600 · 34s · in 73,884 / out 3,234 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
[
  {
    "transcript_id": "TX-001",
    "deal_alias": "Deal-CFE7F4",
    "why_buys": ["Automate anniversary and birthday awards."],
    "pain_points": ["An HR team of three cannot keep up with awards manually.", "Spreadsheet tracking causes people to slip through the cracks."],
    "stakeholders": ["Prospect (VP People)", "Prospect (HR Admin)"],
    "budget_signal": "About $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": ["SSO and audit logs are needed 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": "Finance approved a $25k pilot budget for this quarter.",
    "timeline_signal": "Decision wanted by end of September; pilot agreement to be routed to legal this week.",
    "competitor_mentioned": null,
    "next_step": "Send the pilot agreement; the prospect will route it to legal this week.",
    "objections": ["Workday integration must 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."],
    "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 must be sold first and decides people-related purchases."],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-004",
    "deal_alias": "Deal-180D02",
    "why_buys": ["Consolidate three separate recognition tools into one."],
    "pain_points": ["They pay for three tools, none of which connects to their HRIS."],
    "stakeholders": ["Prospect (VP People)", "Prospect (IT Security Lead)"],
    "budget_signal": "The VP People can approve a purchase under $15k annually without going to the board.",
    "timeline_signal": "Procurement takes six to eight weeks minimum.",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": ["The IT Security Lead hesitates because the last vendor's security review took three months."],
    "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; their engagement scores run 20 points lower."],
    "stakeholders": ["Prospect (HR Director)", "Prospect (People Ops Coordinator)"],
    "budget_signal": "$12k approved under the engagement line.",
    "timeline_signal": "Running before the January all-hands.",
    "competitor_mentioned": "Nectar",
    "next_step": "Present to the exec team on October 2.",
    "objections": ["The prospect is mid-pilot with Nectar and says the experience must be beaten.", "The exec team is skeptical after a failed rollout two years ago."],
    "confidence": "high"
  },
  {
    "transcript_id": "TX-006",
    "deal_alias": "Deal-EE195F",
    "why_buys": ["Cut administrative time spent on service awards."],
    "pain_points": ["The HR Manager spends five hours a month ordering and shipping plaques."],
    "stakeholders": ["Prospect (HR Manager)"],
    "budget_signal": "Budget is not the issue; no amount stated.",
    "timeline_signal": "Fiscal year resets in February; a Q1 start is realistic.",
    "competitor_mentioned": null,
    "next_step": "Send a 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-close0.50
Plain-English question: Which 3 deals are closest to signature right now?
Prompt — what the model saw
Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.2345 · 36s · in 52,994 / out 1,940 tok
✓ exactly_three✗ top3_named✓ slack_first✗ blocked_excluded

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
The Slack thread is dated September 4, so it does not establish signature status *right now*. Based only on the supplied CRM export and that thread, these are the three closest:

1. **Deal-547B2B — $11,200.** DS5/COMMIT. Redlines were clean, the signing page was out, and the buyer’s VP People said they would sign the next day. **Left:** confirm whether the signature actually happened; the supplied data does not say.

2. **Deal-403845 — $9,000.** DS5/COMMIT. The order form was with the buyer’s finance team, a more concrete step toward signature than a general “warm” update. **Left:** finance review and signature; the thread gives no outcome. Slack’s Dana Mercer describes it as “on my side,” while the CSV lists Alex Franklin as owner.

3. **Deal-A2B47C — $6,360.** DS5/COMMIT. Slack says it was still warm and moving at a normal legal-review pace; its CRM close date is earlier than the similarly described Deal-D348E1. **Left:** legal review and signature; no completion is documented.

Listed amounts: $11,200 + $9,000 + $6,360 = **$26,560**. Deal-2465CE does not make the cut despite DS5/COMMIT: Slack says procurement froze new vendors.
product-gap-detectionPASS 1.00
Plain-English question: Which prospect complaints are true product gaps versus rollout or enablement issues?
Prompt — what the model saw
Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.
cost $0.2407 · 29s · in 59,450 / out 1,566 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
| Deal | Prospect line | Classification | Amount |
|---|---|---|---|
| Deal-EC3025 | “We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.” | Real gap — SCIM is not listed as supported. | Not provided |
| Deal-D0D6B5 | “Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.” | Real gap — ADP Workforce Now is not listed as supported. | Not provided |
| Deal-CFE7F4 | “I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?” | Plan gate — the custom report builder is Enterprise-only. The excerpt does not establish whether that specific report is possible. | Not provided |
| Deal-84DBA6 | “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 training. | Not provided |

Summary — real gaps: SCIM provisioning (Deal-EC3025) and ADP Workforce Now integration (Deal-D0D6B5). Deal-36C33F is excluded: the mobile-app limitation came from the rep, while the prospect said the web version should be fine for now. No deal amounts were supplied, so there is no amount arithmetic to show.
rep-performance 5 tests
stale-pipeline-by-rep0.67
Plain-English question: Which open deals have had no email, call, or meeting in the last 7 days?
Prompt — what the model saw
The snapshot date is 2026-09-05. Using the open deals and the engagements-by-deal table, list every open deal with no logged email, call, or meeting in the last 7 days. Compute recency from the engagements table's last_email/last_call/last_meeting fields, NOT from any date field on the deal (the deal's last_contacted_field is unreliable). For each stale deal give: deal alias, owner name, stage, amount, days since last contact. Group by owner, order by amount descending within each owner. Then state, per owner, how many stale deals they carry and the total stale amount. (Do not write outreach text.)
cost $0.3296 · 89s · in 66,970 / out 8,662 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✓ kevin_stale_amt✗ jenn_stale_amt✗ stale_count

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
As of 2026-09-05, a deal is stale if the latest dated email, call, or meeting in the engagements table was before 2026-08-29. Days since last contact = 2026-09-05 minus that latest date. Future-dated meetings are not counted as contact by the snapshot date. The deal’s `last_contacted_field` was not used.

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

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

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

15 stale deals; $60,000 + $43,875 + $27,000 + $23,400 + $20,000 + $18,900 + $16,250 + $15,000 + $10,500 + $9,000 + $8,100 + $7,350 + $5,000 + $4,200 + $1,920 = $270,495.

Alex Franklin
Deal alias | Stage | Amount | Days since last contact
Deal-CC08D1 | DS1 | $24,000 | 16
Deal-E73427 | DS3 | $18,000 | 10
Deal-885F45 | DS2 | $9,300 | 12
Deal-C2FF3C | DS1 | $8,316 | 10
Deal-0D2F7A | DS3 | $5,100 | 12
Deal-6C60D4 | DS3 | $4,800 | 12
Deal-13FEBD | DS2 | $4,680 | 12
Deal-819506 | DS1 | $4,400 | 8
Deal-9D0060 | DS3 | $3,840 | 12
Deal-690476 | DS2 | $3,600 | 18
Deal-EE195F | DS3 | $3,120 | 8
Deal-278DEC | DS3 | $2,700 | 8
Deal-635B8E | DS3 | $2,600 | 18
Deal-6883F3 | DS1 | $2,400 | 16
Deal-4A13AD | DS3 | $2,160 | 26
Deal-F67D31 | DS2 | $1,800 | 8
Deal-5FDCE4 | DS3 | $1,600 | 12
Deal-BA571A | DS4 | $1,080 | 18

18 stale deals; $24,000 + $18,000 + $9,300 + $8,316 + $5,100 + $4,800 + $4,680 + $4,400 + $3,840 + $3,600 + $3,120 + $2,700 + $2,600 + $2,400 + $2,160 + $1,800 + $1,600 + $1,080 = $103,496.

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

17 stale deals; $58,529.25 + $40,000 + $32,175 + $31,750 + $18,000 + $12,168 + $11,193 + $7,781.20 + $7,225.40 + $6,947.50 + $5,616 + $4,779.88 + $3,360 + $3,334.80 + $2,700 + $1,875 + $1,330 = $248,765.03.

Farid Osman
Deal alias | Stage | Amount | Days since last contact
Deal-8BA24E | DS1 | $2,880 | 8
Deal-8FDCD2 | DS1 | $1 | 15

2 stale deals; $2,880 + $1 = $2,881.

Elena Sinclair’s Deal-57FF13 has no row in `engagements_by_deal_90d.csv`. Its contact recency—and therefore whether it is stale—cannot be determined from the provided data.
activity-mix-vs-outcomePASS 1.00
Plain-English question: Per rep, what did the last 30 days of emails, calls, and meetings look like next to the deals that advanced?
Prompt — what the model saw
The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.9524 · 117s · in 289,333 / out 6,495 tok
✓ alex_ds2_30d✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Using the provided `emails_30d`, `calls_30d`, and `meetings_30d` counts, and counting DS2 entries dated 2026-08-06 through the 2026-09-05 snapshot (30 days or fewer before it):

| Rank | Rep | Emails | Calls | Meetings | Activity mix (email / call / meeting) | DS2 entries | Activities per DS2 entry |
|---:|---|---:|---:|---:|---|---:|---:|
| 1 | Alex Franklin | 307 | 36 | 41 | 79.9% / 9.4% / 10.7% | 18 | (307 + 36 + 41) / 18 = **21.33** |
| 2 | Bryce Harmon | 162 | 0 | 43 | 79.0% / 0.0% / 21.0% | 4 | (162 + 0 + 43) / 4 = **51.25** |
| 3 | Cole Ingram | 96 | 14 | 1 | 86.5% / 12.6% / 0.9% | 2 | (96 + 14 + 1) / 2 = **55.50** |
| 4 | Farid Osman | 38 | 0 | 34 | 52.8% / 0.0% / 47.2% | 1 | (38 + 0 + 34) / 1 = **72.00** |
| 5 | Dana Mercer | 84 | 18 | 11 | 74.3% / 15.9% / 9.7% | 1 | (84 + 18 + 11) / 1 = **113.00** |

Each mix percentage is that activity count divided by the row’s email + call + meeting total; for example, Alex Franklin’s email share is 307 / 384 × 100 = 79.9%.

Alex Franklin is both the most efficient on the reported ratio and the highest-volume rep, with 384 recorded activities; they do not differ. His deal Deal-3EED2C has no row in `engagements_by_deal_90d.csv`, so his activity total and ratio may be incomplete. Elena Sinclair’s Deal-57FF13 also has no engagement row; she has no recorded DS2 entry, so her ratio is undefined. Gavin Porter and Hugo Lindqvist have no deals or engagements in the supplied tables and likewise cannot be ranked.
ae-qtd-scorecardPASS 1.00
Plain-English question: Build Alex Franklin's QTD scorecard: bookings versus quota, pipeline, and conversion.
Prompt — what the model saw
Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.7960 · 134s · in 208,980 / out 7,975 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

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

Model's actual answer

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

- Bookings vs quota: $150,000 / $200,000 = 75.0% attainment. This excludes Deal-B3E6F1 ($24,000), won on 2026-06-20 before Q3.
- Booking split: New $113,500 (5 wins); expansion $36,500 (3 wins). $113,500 + $36,500 = $150,000.
- Active pipeline: DS1 $284,621 (20 deals); DS2 $353,760 (28); DS3 $552,705 (67); DS4 $23,574 (5); DS5 $45,730 (5). Total: $284,621 + $353,760 + $552,705 + $23,574 + $45,730 = $1,260,390 across 125 deals.
- Rolling 90-day DS2-to-won rate: 8 / 111 = 7.2%. Cohort: deals entering DS2 from 2026-06-08 through 2026-09-05, inclusive; the denominator includes 76 still-open deals.
- QTD outcomes: 8 wins, 27 losses. Top recorded loss reason: “Lost- Timing (1 year or more)” — 13 / 27 losses = 48.1%.
- Last-30-day activity: 807 emails, 112 calls, 128 meetings, 50 notes (sums of the supplied 30-day engagement columns).

Coaching observations:
1. Prioritize DS3 progression: 67 deals and $552,705 sit there, the largest stage balance.
2. Review the 13 timing-related losses; they account for 48.1% of QTD losses. The supplied reason labels do not establish why those deals slipped.
3. Focus on converting the DS2 cohort: 76 of 111 deals remain open, while 8 have won.
multithreading-gapPASS 1.00
Plain-English question: Which open deals are single-threaded or under-threaded?
Prompt — what the model saw
Find every open deal that is single-threaded (fewer than 2 active contacts) or under-threaded (fewer than 3, or all contacts in one persona). Active means engaged in the last 60 days and not marked former. Personas: economic buyer, champion, HR admin, IT security, finance. For each flagged deal: amount, stage, active contact count, personas present, personas missing, the most valuable persona to add given the stage, and any on-file unengaged contact at that company who fits; otherwise say none on file.
cost $0.3449 · 69s · in 64,036 / out 5,624 tok
✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
The files do not provide deal status, amount, or stage. I therefore cannot confirm which deals are open, report their amounts or stages, or determine the most valuable persona to add *given the stage*. The table shows every deal that meets the contact-threading test, conditional on it being open.

Using September 24, 2026 as the reference date, the 60-day cutoff is July 26, 2026. Active count = contacts engaged on or after that date − contacts marked former. “Missing” means absent from the active contacts.

| Deal / company | Active-count arithmetic | Flag | Personas present | Personas missing | On-file unengaged contact in a missing persona |
|---|---:|---|---|---|---|
| Deal-EC3025 / C-FDD0C7 | 2 − 1 former = 1 | Single-threaded | champion | economic buyer, HR admin, IT security, finance | CT-6827DB — Chief People Officer, economic buyer |
| Deal-92D97D / C-E23238 | 2 − 1 outside window = 1 | Single-threaded | HR admin | economic buyer, champion, IT security, finance | None on file |
| Deal-50D386 / C-EB10E4 | 2 − 0 = 2 | Under-threaded | champion, HR admin | economic buyer, IT security, finance | CT-A1C4B3 — Chief People Officer, economic buyer |
| Deal-D0D6B5 / C-32918E | 3 − 0 = 3; all champion | Under-threaded | champion | economic buyer, HR admin, IT security, finance | CT-1FA4DB — Chief People Officer, economic buyer |
| Deal-5BFE3B / C-535D36 | 2 − 0 = 2 | Under-threaded | champion | economic buyer, HR admin, IT security, finance | None on file |
| Deal-36C33F / C-077A0E | 3 − 2 former = 1 | Single-threaded | IT security | economic buyer, champion, HR admin, finance | CT-1DB73E — Chief People Officer, economic buyer |
| Deal-885F45 / C-5E8EFB | 2 − 0 = 2 | Under-threaded | economic buyer, champion | HR admin, IT security, finance | CT-B3F25D — IT Security Lead, IT security |
| Deal-FCBE5B / C-737030 | 1 − 0 = 1 | Single-threaded | champion | economic buyer, HR admin, IT security, finance | None on file |
| Deal-5408B0 / C-2AE3AA | 2 − 0 = 2 | Under-threaded | champion, HR admin | economic buyer, IT security, finance | CT-07FA76 — Chief People Officer, economic buyer |
| Deal-C6D97A / C-5A8FC2 | 3 − 0 = 3; all champion | Under-threaded | champion | economic buyer, HR admin, IT security, finance | None on file |
| Deal-F9A08A / C-0D15DF | 2 − 1 outside window = 1 | Single-threaded | champion | economic buyer, HR admin, IT security, finance | CT-697541 — Chief People Officer, economic buyer |

That is 5 single-threaded + 6 under-threaded = 11 flagged deals in the supplied contact file. Amount, stage, open status, and a stage-specific persona priority are missing for every row.
talk-track-from-transcriptsPASS 1.00
Plain-English question: From this rep's last 10 calls: how do they open, handle objections, and how often do they lock a next step?
Prompt — what the model saw
From this rep's last 10 calls: what they lead with in the first five minutes, how they handle the three most common objections, how often a concrete next step is agreed (rate), and every competitor a prospect raised. Quote at most one line per finding and only lines that appear in the transcripts. End with two coaching notes.
cost $0.2920 · 41s · in 93,345 / out 2,961 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Across the 10 supplied calls:

- First five minutes: Alex led with the same retailer turnover case in 8/10 calls. Deal-403845 opened with a security-and-pricing agenda; Deal-1E2498 opened with pricing. He also introduced Workhuman in Deal-C61CF7; prospects raised Awardco and Kudos in the opening five minutes of their calls. “Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.” (TT-001, Deal-D348E1)

- Budget locked (4 calls: Deal-D348E1, Deal-547B2B, Deal-60C2C2, Deal-84DBA6): He framed turnover savings as the funding case, citing avoided backfills and finance sign-off. “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.” (TT-001, Deal-D348E1)
- Timing/capacity (3 calls: Deal-5408B0, Deal-C61CF7, Deal-D9A12F): He proposed a one-department, 90-day pilot before the next planning cycle. “Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?” (TT-002, Deal-5408B0)
- Existing spreadsheet and gift-card process (3 calls: Deal-403845, Deal-EDC141, Deal-1E2498): He contrasted it with automated milestones and recognition analytics. “Spreadsheets work until they scale — the difference is automation: milestones fire without HR lifting a finger, and you get analytics on who is being recognized.” (TT-004, Deal-403845)

- Concrete next step agreed: 7/10 = 70%. The prospect agreed to a Thursday working session in Deal-D348E1, Deal-5408B0, Deal-547B2B, Deal-C61CF7, Deal-60C2C2, Deal-D9A12F, and Deal-1E2498. No concrete next step was agreed in Deal-403845, Deal-EDC141, or Deal-84DBA6.

- Competitors raised by prospects: Awardco (Deal-547B2B) and Kudos (Deal-EDC141). Workhuman was raised by Alex, not a prospect.

Coaching notes:
1. When a committee blocks commitment, ask for a specific committee-prep meeting or decision date rather than leaving the call open-ended (Deal-403845, Deal-84DBA6).
2. When a prospect says there is no urgency, test what would make the issue worth revisiting and agree on a dated follow-up (Deal-EDC141).
reporting-analytics 5 tests
q3-weighted-forecastPASS 1.00
Plain-English question: Build the Q3 2026 weighted forecast from the open pipeline, and show the math deal by deal.
Prompt — what the model saw
Below are the open deals in the default sales pipeline with a close date between 2026-07-01 and 2026-10-15, pulled from the CRM warehouse on 2026-09-05. Stages: DS1 (discovery) through DS5 (contract). Build the Q3 2026 forecast (quarter = 2026-07-01 to 2026-09-30). Weighting rule: 100 percent of COMMIT plus 35 percent of BEST_CASE. PIPELINE deals count zero. Only deals with a close date inside the quarter count. Report: COMMIT total, BEST_CASE total, weighted forecast, count of deals in each category inside the quarter, the deals you excluded for being outside the quarter (count and total amount), and the top 5 BEST_CASE deals by amount inside the quarter. Show arithmetic. Then, in a final '## Data quality' section of no more than four sentences, name the data quality problems in this extract that would make the forecast unreliable if it were run unattended.
cost $0.3193 · 58s · in 110,368 / out 4,433 tok
✓ commit_total_44729✓ best_case_total_203565✓ weighted_115976.75✓ commit_count_7✓ best_case_count_24✓ excluded_count_32✓ excluded_amount_227575✓ top5_aliases✓ flags_blank_owner✓ flags_missing_why_buys

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
## Q3 2026 forecast

| Category | In-quarter deals | Total | Forecast contribution |
|---|---:|---:|---:|
| COMMIT | 7 | $44,729 | $44,729 |
| BEST_CASE | 24 | $203,565 | $203,565 × 35% = $71,247.75 |
| PIPELINE | 23 | $201,637.40 | $0 |

**Weighted forecast: $44,729 + $71,247.75 = $115,976.75.**

**Excluded for close dates outside Q3:** 32 deals totaling $227,575 = $13,770 COMMIT + $28,240 BEST_CASE + $185,565 PIPELINE.

| Category | Excluded deals (amount) |
|---|---|
| COMMIT | Deal-D348E1 ($13,770) |
| BEST_CASE | Deal-C61CF7 ($5,400), Deal-48B656 ($5,160), Deal-901332 ($3,600), Deal-47AE31 ($3,600), Deal-15D24F ($3,600), Deal-ED725A ($2,400), Deal-8AD4A5 ($1,800), Deal-5FDCE4 ($1,600), Deal-F5A622 ($1,080) |
| PIPELINE | Deal-E51FB7 ($43,875), Deal-B936FE ($18,000), Deal-D9A12F ($17,000), Deal-4062CF ($10,800), Deal-293AF3 ($9,000), Deal-034D49 ($9,000), Deal-E0ADD8 ($7,920), Deal-9F2E43 ($7,690), Deal-FCBE5B ($7,500), Deal-712010 ($7,200), Deal-6691E0 ($5,700), Deal-600CD9 ($5,400), Deal-A92065 ($5,400), Deal-1D532E ($5,400), Deal-E531A6 ($4,800), Deal-D1E6C2 ($4,400), Deal-D9E112 ($4,300), Deal-5AD94B ($4,000), Deal-766C74 ($3,300), Deal-D7E999 ($1,800), Deal-ED13B0 ($1,680), Deal-7FA0C3 ($1,400) |

**Top 5 in-quarter BEST_CASE deals by amount:** Deal-2D7423 ($38,935), Deal-25F752 ($24,000), Deal-E53952 ($19,656), Deal-5EED42 ($16,250), Deal-FA32A0 ($11,116).

## Data quality

Owner is blank on 85 of 86 deals, and `why_buys_chars` is zero on most rows, leaving little ownership or buyer-rationale context for unattended review. The extract still labels deals with close dates before its 2026-09-05 pull date as open, so overdue dates may be stale. Deal-A5E80A is marked COMMIT at DS1 while Deal-C61CF7 is BEST_CASE at DS5, showing that stage and forecast category can diverge; the rule above uses category as supplied.
aha-moment-2x2PASS 1.00
Plain-English question: Do companies that hit 5+ givers and one redemption in month one actually retain better at 24 months?
Prompt — what the model saw
Test the activation hypothesis on the company cohort below: companies with 5+ unique givers (m1_users >= 5) AND 1+ successful redemption (m1_redemptions >= 1) in their first calendar month retain better at 24 months than those with one signal or neither. Every company in this extract is already 25+ months old, so current_status = 'active' means retained at 24 months. Report the full 2x2 (cohort size and 24-month retention rate for: both signals, givers-only, redemption-only, neither), state how many companies were excluded from the denominator and why (if none, say so), name the single signal with the largest retention lift, and state what this does and does not prove.
cost $0.7809 · 167s · in 208,429 / out 5,768 tok
✓ both_66✓ givers_47✓ redemp_31✓ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
In the provided `plg_company_cohort_2023.csv` extract, `active` counts as retained at 24 months; `non_renewing` does not.

| First-month signals | Companies | Retained | 24-month retention |
|---|---:|---:|---:|
| Both (m1_users ≥ 5; m1_redemptions ≥ 1) | 47 | 31 | 31 ÷ 47 = 66.0% |
| Givers only | 49 | 23 | 23 ÷ 49 = 46.9% |
| Redemption only | 29 | 9 | 9 ÷ 29 = 31.0% |
| Neither | 95 | 38 | 38 ÷ 95 = 40.0% |

Excluded from the denominator: **0**. All 220 companies have both signal values and a status; the three `non_renewing` companies remain in their cohorts as not retained.

The **5+ givers signal** has the largest standalone lift versus neither: 23 ÷ 49 − 38 ÷ 95 = **+6.9 percentage points**. Redemption alone is 9 ÷ 29 − 38 ÷ 95 = **−9.0 points**.

The both-signals group retains better than either one-signal group or neither, supporting the hypothesis as an **association in this extract**. It does not prove that achieving either signal causes retention; company differences and other factors could explain the gap.
arr-rollforward-reconciliationPASS 1.00
Plain-English question: Does CRM ARR match billing ARR — and if not, exactly where is the variance?
Prompt — what the model saw
Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.2646 · 47s · in 86,573 / out 4,905 tok
✓ billing_total✓ crm_total✓ variance✓ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
As of 2026-09-05, using the supplied records and counting only active subscriptions in billing ARR:

- CRM company ARR: $603,581.76
- Billing ARR: $604,739.28 (sum of active MRR × 12)
- Variance, CRM minus billing: $603,581.76 − $604,739.28 = −$1,157.52

| Variance bucket | Calculation | CRM − billing |
|---|---:|---:|
| Status mismatch | $4,905.24 + $8,253.24 on cancelled subscriptions | +$13,158.48 |
| Rounding | No rounding cause established by the data | $0.00 |
| Missing records | $16,497.24 CRM-only − $28,449.24 billing-only | −$11,952.00 |
| Other | $16.00 − $2,400.00 + $20.00 | −$2,364.00 |
| Total | $13,158.48 + $0.00 − $11,952.00 − $2,364.00 | −$1,157.52 |

Mismatched accounts (suggested owners are teams, not identified account owners):

| Company alias | CRM ARR | Billing ARR | Difference | Suggested owner |
|---|---:|---:|---:|---|
| C-0C8323BF | $4,905.24 | $0.00 | +$4,905.24 | RevOps — review CRM ARR for cancelled subscription |
| C-0DC4FB8C | $8,253.24 | $0.00 | +$8,253.24 | RevOps — review CRM ARR for cancelled subscription |
| C-0D5BBE3A | $16,497.24 | $0.00 | +$16,497.24 | Billing/Finance — investigate missing subscription |
| C-21629AA4 | $0.00 | $28,449.24 | −$28,449.24 | RevOps — investigate missing company record |
| C-0D66DF9E | $23,200.00 | $23,184.00 | +$16.00 | RevOps — verify CRM ARR |
| C-0F7269D7 | $24,396.00 | $26,796.00 | −$2,400.00 | Billing/Finance and RevOps — reconcile amounts |
| C-14D70CE0 | $18,200.00 | $18,180.00 | +$20.00 | RevOps — verify CRM ARR |

Agreement-end-date violations: SUB-0002 (C-1794A52C, 24 months) and SUB-0019 (C-22170CA1, 36 months). Both have a blank `cf_agreement_end_date`.

The files contain no record-effective dates, currency fields, or owner assignments, so the 2026-09-05 snapshot, currency, causes of amount differences, and specific owners cannot be independently verified.
kvm-month-over-monthPASS 1.00
Plain-English question: How did the four core KVMs move in August versus July?
Prompt — what the model saw
Report the four core KVMs for 2026-08 against 2026-07: giving rate, redemptions per user, 1:1 meetings engagement, pulse check engagement. For each: value, prior value, absolute and relative change, direction. Name the KVM with the largest relative move and the segment (plan_tier or size_band) driving it if the data supports that; otherwise say the data does not. Under 200 words plus one table.
cost $0.1574 · 36s · in 27,475 / out 3,560 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Using an equal-weight average of the 30 company rows in each month:

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

Arithmetic for each row: absolute change = August average − July average; relative change = absolute change ÷ July average × 100. The displayed averages and absolute changes are rounded.

Pulse check engagement had the largest relative move. The `enterprise` size_band drove it: its pulse average fell from 0.549980 to 0.274280, a change of −0.275700 ÷ 0.549980 × 100 = −50.1291%.
rewards-ytd-sectionPASS 1.00
Plain-English question: Produce the redemption section of the weekly rewards report through August.
Prompt — what the model saw
Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.7551 · 133s · in 216,254 / out 4,082 tok
✓ count✓ spend✓ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Redemptions, Jan 1–Aug 31, 2026. Last completed month: August 2026.

- Redemption count: 378
- Spend: $27,846.00
- Unique redeemers: 235 distinct user keys
- Redemptions per redeemer: 378 ÷ 235 = 1.61

Provider mix by share of spend:
- custom: $10,873 ÷ $27,846 = 39.05%
- Tremendous: $8,505 ÷ $27,846 = 30.54%
- Snappy: $5,238 ÷ $27,846 = 18.81%
- TangoCard: $3,230 ÷ $27,846 = 11.60%

Provider spend: $10,873 + $8,505 + $5,238 + $3,230 = $27,846. Shares: 39.05% + 30.54% + 18.81% + 11.60% = 100.00%.

Top 5 countries by redemptions: US 244; CA 24; AU 21; GB 17; NL 17.
customer-success 5 tests
churn-save-eligibilityPASS 1.00
Plain-English question: Which at-risk accounts actually qualify for a churn-save offer, and which play fits each?
Prompt — what the model saw
Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.2926 · 55s · in 77,079 / out 4,340 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

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

Model's actual answer

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Under R1–R3, 8 accounts qualify: health score below 60, churn-save eligible amount above $0, and renewal by 2027-01-03 (120 days after the 2026-09-05 snapshot). “At stake” below is the churn-save eligible amount, not full ARR.

| Account | Amount at stake | Play and supporting signal |
|---|---:|---|
| C-0F6C0F34 | $49,707 | Executive touch — champion inactive; renews 2026-10-03. |
| C-0B827671 | $25,365 | Usage revival — usage declining; 113 of 202 seats used. |
| C-0B360C78 | $35,748 | Not established — usage growing and champion active. No signal provided that justifies a commercial concession. |
| C-0B0F1BAB | $5,494 | Executive touch — champion inactive; renews 2026-09-23. |
| C-0CA21961 | $16,829 | Usage revival — 84 of 325 seats used; usage flat. |
| C-0E9C27D1 | $41,235 | Not established — 134 of 157 seats used and champion active. No signal provided that justifies a commercial concession. |
| C-0CEF69FD | $32,621 | Executive touch — champion inactive. |
| C-0D3278C7 | $17,602 | Usage revival — usage declining; 126 of 380 seats used. |

Total at stake: $49,707 + $25,365 + $35,748 + $5,494 + $16,829 + $41,235 + $32,621 + $17,602 = **$224,601**. The files contain eligibility rules, but no play-selection rules or evidence of a commercial issue; assigning a commercial concession to either unassigned account would be unsupported.

At-risk accounts that do not qualify (all have health scores below 60):

| Account | Why it fails eligibility |
|---|---|
| C-0BC71BDD | Eligible amount is $0. |
| C-0BA71F12 | Renews 2027-04-11, outside the 120-day window. |
| C-0F6694C3 | Eligible amount is $0; renews outside the window. |
| C-0BE96399 | Eligible amount is $0. |
| C-0F876796 | Renews 2027-02-06, outside the window. |
| C-0FCCD2DF | Eligible amount is $0; renews outside the window. |
| C-10A56B0F | Eligible amount is $0. |
expansion-kitPASS 1.00
Plain-English question: Build the expansion kit for the customer who just replied to our outreach.
Prompt — what the model saw
A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.2840 · 42s · in 60,032 / out 2,330 tok
✓ coverage_pct✓ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

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

Model's actual answer

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C-0DDFC9A7

- Seat coverage: 150 licensed ÷ 400 headcount = 37.5%.
- Usage health: Monthly users rose from 88 in March to 126 in August, up 38 (38 ÷ 88 = 43.2%).
- August usage was 126 ÷ 150 licensed seats = 84%; 24 licensed seats were not represented in that month’s user count.
- Headroom to license the full headcount: 400 − 150 = 250 seats. Current ARR per licensed seat is $9,000 ÷ 150 = $60; at that rate, 250 × $60 = $15,000 in potential additional ARR. This is a rate-based estimate, not a purchase commitment.
- Maria S. replied. She says she cannot make purchasing decisions. Dana R., VP People, is the budget and seat-expansion buyer in the supplied contacts.

Reply email

Subject: Re: Growing your team's recognition program

Hi Maria,

Thanks for the update—and for offering to connect us with Dana. It’s good to hear the team is enjoying Bonusly. Monthly users rose from 88 in March to 126 in August, so I can see why she’s asking about usage.

If you’re comfortable making the introduction, I’d be happy to share those numbers with Dana and hear how she’s thinking about seat coverage. No need to coordinate a meeting unless it would be useful for her.

Thanks,
Cole
onboarding-statusPASS 1.00
Plain-English question: What is done and what is missing in this customer's onboarding before the mid-onboarding call?
Prompt — what the model saw
Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.2874 · 42s · in 60,158 / out 2,002 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

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

Model's actual answer

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C-0D284E42 — mid-onboarding call prep

Complete: Slack integration connected (Aug 12); allowance set (Aug 13); 2 admins added; first recognition given (Aug 15 at 14:22).

Not confirmed complete: HRIS integration and first redemption. Both fields are blank; that does not establish whether either happened.

Early engagement: Daily active givers rose from 3 on Aug 11 to 15 on Sep 4 (+12; 15 − 3). The first seven days averaged 30 ÷ 7 = 4.3 active givers per day; the last seven averaged 91 ÷ 7 = 13. These are daily counts, not a count of unique givers across the period.

Cover on the call:
1. Confirm HRIS integration status and any setup blocker.
2. Review how to sustain and broaden giving beyond the current daily activity; the total eligible user count is not provided.
3. Confirm whether a redemption has occurred and, if not, what is needed for the first one.
renewal-risk-conflicting-dates0.75
Plain-English question: Two systems disagree on renewal dates — which do you trust per account, and who is actually renewing inside 90 days?
Prompt — what the model saw
Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.
cost $0.4183 · 81s · in 125,878 / out 7,159 tok
✗ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

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

Model's actual answer

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90-day renewal risk brief (2026-09-24 through 2026-12-23)

Use Chargebee’s date for each account. ChurnZero is known to misdate multi-year contracts; the two systems agree on every annual contract in this list. Seat utilization is seats_used ÷ seats. Usage trend shows active users in June → July → August 2026; it is a different measure from seats_used. For this brief, High means utilization below 40% or June-to-August usage down more than 10%; Moderate means utilization below 60% otherwise; Low means neither.

| Company | CSM | ARR | Date used | Seat utilization | 3-month usage | Risk and evidence |
|---|---|---:|---|---:|---|---|
| C-0BBE3E60 | Dana Mercer | $30,993 | 2026-09-26 | 74/114 = 64.9% | 39 → 35 → 33 | **High:** Usage fell 15.4% from June to August. |
| C-0F5D2323 | Cole Ingram | $90,647 | 2026-09-29 | 111/390 = 28.5% | 20 → 21 → 18 | **High:** Only 28.5% of seats are used, and usage fell from 20 to 18. |
| C-0EC6999D | Elena Sinclair | $79,419 | 2026-10-03 | 31/112 = 27.7% | 17 → 16 → 15 | **High:** Only 27.7% of seats are used, and usage fell from 17 to 15. |
| C-0B20DB64 | Dana Mercer | $21,770 | 2026-10-07 | 214/378 = 56.6% | 294 → 298 → 294 | **Moderate:** Utilization is below 60%, though August usage matches June. |
| C-0BBC4E7A | Cole Ingram | $56,374 | 2026-10-10 | 228/337 = 67.7% | 142 → 141 → 139 | **Low:** Utilization is 67.7%, with usage down 2.1% from June. |
| C-0FD551AB | Elena Sinclair | $48,815 | 2026-10-14 | 210/376 = 55.9% | 123 → 122 → 126 | **Moderate:** Utilization is below 60%, despite usage rising from 123 to 126. |
| C-0F9F8F13 | Dana Mercer | $46,230 | 2026-10-18 | 199/352 = 56.5% | 185 → 185 → 182 | **Moderate:** Utilization is below 60%, and usage edged down from 185 to 182. |
| C-0BC34584 | Cole Ingram | $16,740 | 2026-10-22 | 327/494 = 66.2% | 104 → 104 → 106 | **Low:** Utilization is 66.2%, and usage rose from 104 to 106. |
| C-0B7A7546 | Elena Sinclair | $35,062 | 2026-10-25 | 182/205 = 88.8% | 64 → 65 → 63 | **Low:** Utilization is 88.8%, with usage nearly flat at 64 versus 63. |
| C-0B369871 | Dana Mercer | $85,128 | 2026-10-29 | 317/422 = 75.1% | 326 → 330 → 333 | **Low:** Utilization is 75.1%, and usage rose from 326 to 333. |
| C-0B144C78 | Cole Ingram | $30,899 | 2026-11-02 | 169/224 = 75.4% | 101 → 101 → 106 | **Low:** Utilization is 75.4%, and usage rose from 101 to 106. |
| C-0FC4DBB8 | Elena Sinclair | $94,732 | 2026-11-05 | 356/464 = 76.7% | 189 → 191 → 193 | **Low:** Utilization is 76.7%, and usage rose from 189 to 193. |
| C-0D5BBE3A | Dana Mercer | $39,740 | 2026-11-09 | 85/102 = 83.3% | 88 → 90 → 91 | **Low:** Utilization is 83.3%, and usage rose from 88 to 91. |
| C-0FB9D5AF | Cole Ingram | $63,158 | 2026-11-13 | 144/199 = 72.4% | 173 → 173 → 176 | **Low:** Utilization is 72.4%, and usage rose from 173 to 176. |
| C-0B344485 | Elena Sinclair | $64,384 | 2026-11-16 | 224/287 = 78.0% | 238 → 240 → 244 | **Low:** Utilization is 78.0%, and usage rose from 238 to 244. |
| C-0CB2C1B4 | Dana Mercer | $40,628 | 2026-11-20 | 386/473 = 81.6% | 47 → 48 → 49 | **Low:** Utilization is 81.6%, and usage rose from 47 to 49. |
| C-22170CA1 | Cole Ingram | $45,646 | 2026-11-24 | 251/294 = 85.4% | 143 → 148 → 146 | **Low:** Utilization is 85.4%, and August usage exceeds June. |

Date disagreements (ChurnZero → Chargebee date used): C-0B7D2C30, 2026-09-10 → 2026-09-15; C-0BCDB8C2, 2027-09-18 → 2026-09-18; C-0D2AB865, 2026-09-10 → 2026-09-22; C-0BBE3E60, 2027-09-26 → 2026-09-26; C-0F5D2323, 2026-09-10 → 2026-09-29. All five are multi-year contracts, so Chargebee wins. The first three Chargebee dates precede this brief’s window and are excluded from its totals.

**ARR renewing:** $890,365 (sum of the 17 listed ARR amounts). **ARR at risk:** $317,874 = $201,059 High ($30,993 + $90,647 + $79,419) + $116,815 Moderate ($21,770 + $48,815 + $46,230).
ticket-theme-synthesis0.80
Plain-English question: What are the real themes in this quarter's support tickets, ignoring the existing tags?
Prompt — what the model saw
Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.
cost $0.1552 · 40s · in 29,703 / out 2,890 tok
✗ theme_count_range✓ counts_verifiable✓ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

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

Model's actual answer

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ARR affected is the sum of distinct account ARR within each theme, not a measure of lost revenue. Themes are ranked by ARR exposure, not ticket volume. Classification uses ticket text, not tags.

| Theme | Tickets and share | Distinct accounts | ARR affected | Example ticket IDs | Recommendation |
|---|---:|---:|---:|---|---|
| HRIS provisioning and sync failures — broad pattern | 12/80 = 15.0% | 3 | $30,000 + $36,000 + $48,000 = **$114,000** | IC-460059, IC-460062 | Investigate skipped hires and add an alert when provisioning counts diverge from HRIS records. |
| Redemption, checkout, and gift-card failures — broad pattern | 18/80 = 22.5% | 7 | $8,900 + $8,700 + $9,600 + $9,600 + $11,000 + $10,700 + $10,300 = **$68,800** | IC-460025, IC-460024 | Trace failed orders through checkout, points deduction, and gift-card delivery; reconcile deducted points. |
| Seat-count and renewal-price billing errors — **single-account concentration**, not a cross-account pattern | 16/80 = 20.0% | 1 (C-0E9C27D1) | **$52,000** | IC-460069, IC-460078 | Reconcile this account’s licensed seats and renewal tier against its invoices before the next charge. |
| Recognition points not posting — broad pattern | 20/80 = 25.0% | 9 | $4,500 + $2,700 + $3,500 + $2,500 + $4,200 + $3,400 + $2,900 + $2,900 + $4,500 = **$31,100** | IC-460016, IC-460004 | Audit recognition delivery against points-ledger postings and repair missing balances. |
| Slack recognition sync and slash-command failures — broad pattern | 14/80 = 17.5% | 4 | $3,900 + $5,400 + $4,400 + $5,200 = **$18,900** | IC-460041, IC-460046 | Check connection persistence, sync delivery, and slash-command errors across affected workspaces. |
marketing 5 tests
case-study-matchPASS 1.00
Plain-English question: Which three existing customers are the best case-study matches for this prospect?
Prompt — what the model saw
Given the prospect, rank the three most similar existing customers with public case studies for use as social proof. Similarity on industry, size band, use case, region. Cite the fields that drove each match. Do not name any customer without a case study and never include billing data or contact names.
cost $0.3046 · 56s · in 72,934 / out 3,728 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

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

Model's actual answer

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For prospect C-82AF3719, the three closest customers with case studies are tied on an equal-field match: each matches 3 of 4 fields (1 + 1 + 1 = 3). The order below breaks that tie by prioritizing industry, then use case.

1. C-64171065 — Matches industry (Technology), size band (Mid-Market), and use case (employee_recognition). Region differs: NA-East vs. NA-West.
2. C-A13C193D — Matches industry (Technology), size band (Mid-Market), and region (NA-West). Use case differs: retention vs. employee_recognition.
3. C-11C31562 — Matches size band (Mid-Market), use case (employee_recognition), and region (NA-West). Industry differs: Manufacturing vs. Technology.

Source: provided customers.csv and prospect.csv.
channel-attribution-mathPASS 1.00
Plain-English question: Compute cost per SQM, cost per SQO, and pipeline per dollar for every channel over six months.
Prompt — what the model saw
Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.
cost $0.3228 · 56s · in 89,109 / out 3,740 tok
✓ undefined_channel✓ undefined_not_zero✓ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

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

Model's actual answer

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March–August 2026, using only the supplied first-touch contact rows. SQOs and pipeline below include the two date-flagged rows as recorded.

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

Arithmetic: spend is monthly spend × 6. For each row, cost/SQM = spend ÷ SQMs; cost/SQO = spend ÷ SQOs; SQM→SQO = SQOs ÷ SQMs; pipeline/$ = pipeline ÷ spend. Pipeline sums the supplied amounts: paid_search 18 × $40,000 = $720,000; linkedin_ads 8 × $12,000 = $96,000; webinars 5 × $12,000 = $60,000. paid_social has spend but no SQMs, so its conversion and unit costs are undefined—not zero.

| Organic channel | Volume (SQMs) | SQOs | SQO rate | Pipeline |
|---|---:|---:|---:|---:|
| organic_search | 30 | 10 | 10 ÷ 30 = 33.3% | 10 × $9,000 = $90,000 |
| referral | 15 | 6 | 6 ÷ 15 = 40.0% | 6 × $8,000 = $48,000 |

Date flags: CT-000041 (linkedin_ads) has SQO 2026-06-09 before SQM 2026-06-14; CT-000044 (linkedin_ads) has SQO 2026-07-18 before SQM 2026-07-23.

Recommendation: Pause or sharply reduce paid_social pending attribution review, and test reallocating that budget toward paid_search, which produced $20.00 pipeline per dollar versus $4.00 for linkedin_ads and $6.67 for webinars. Confidence is moderate for the observed paid_search advantage (40 SQMs, 18 SQOs), but low for a permanent allocation change: webinars has only 5 SQOs, linkedin_ads has 8 including two date anomalies, and pipeline is not realized revenue.
competitor-battlecard-updatePASS 1.00
Plain-English question: Update the Rivally battlecard with sourced pricing and where each side wins.
Prompt — what the model saw
Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.3578 · 58s · in 95,345 / out 4,523 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# Rivally battlecard

**One-line positioning:** Points-based recognition with an engaging feed, an optional engagement-survey add-on, and EU data residency. [S02, S16, S23, S15]

**Pricing:** Recognition Starter is **$7 per user/month, annual billing**, on the 2026-08-12 pricing page. That supersedes the **$5 per user/month** shown on the 2026-01-20 and 2026-04-01 pricing pages; the old card’s $5 starting price is outdated. A prospect reported a **$6.50/user/month** quote for 500 seats on 2026-06-02; another reported $7 list with a 15% discount for a three-year term on 2026-08-14. Those are deal-specific reports, not published pricing. Pulse is priced separately; its price is not provided. [S17, S03, S08, S13, S18, S23]

**Where they win:** Reviewers praise the recognition feed, quick setup and Slack integration, multi-language support for distributed EU teams, and support response time. EU data residency was announced generally available. These are review and announcement signals, not verified reasons for any listed deal loss. [S02, S04, S12, S16, S22, S15]

**Where we win:** An 800-seat prospect picked Bonusly over Rivally citing analytics depth. Other reviews describe Rivally’s reporting as limited or basic; an enterprise reviewer reported no SCIM provisioning, and reviewers flagged thin EMEA rewards, lagging admin tools, CSV-only analytics exports, and no bulk recognition editing. Bonusly’s relative capabilities on those other points are not provided. [S25, S02, S07, S10, S14, S16, S20, S24]

**Objections and responses:**
- “Rivally costs $5.” → The newer published Starter price is $7/user/month with annual billing; ask for a current written quote because deal-specific terms differ. [S03, S08, S17, S13, S18]
- “Rivally has EU coverage.” → Acknowledge its announced EU data residency and the review praising multi-language support. If relevant, ask the buyer to validate EMEA reward selection; one reviewer found it thinner than the US catalog. Bonusly’s EU capabilities are not established by these snippets. [S15, S12, S14]
- “Rivally’s recognition feed is better.” → Acknowledge positive feed reviews; test reporting and administration requirements in the evaluation rather than claiming Bonusly wins the feed. [S02, S16, S07, S24, S25]

**Recent changes:** Recognition Starter rose from $5 on the 2026-04-01 pricing page to $7 on 2026-08-12. Rivally opened a Dublin office and announced generally available EU data residency on 2026-07-01; announced Teams app v2 in public preview on 2026-08-20; and said Pulse exited beta as a separately priced add-on on 2026-09-01. Public preview is not general availability. [S08, S17, S15, S19, S23]

**Our 12-month win/loss record:** For the supplied 2025-09 through 2026-08 Rivally rows: **13 wins + 7 losses = 20 closed deals; win rate = 13 ÷ 20 = 65%**. The deal file supplies aliases, not snippet IDs or loss reasons. 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. [deals_with_competitor.csv: listed deal aliases]

**Old-card claims:** “Lacks a Slack integration” is contradicted by a reviewer. “Acquired by WorkHuman in 2025” is **unverified** in the supplied snippets; the supplied press item instead reports a Series C, which does not establish ownership. The old card’s “for mid-market” positioning is **unverified** as a target-market claim; the snippets include a mid-market review but no positioning statement. [S04, S01, S02]
nurture-sequence-diagnosisPASS 1.00
Plain-English question: What is broken in each email sequence — rates, overlaps, tracking errors?
Prompt — what the model saw
Review the sequences. Per sequence: sent, open rate, reply rate, meeting rate, weakest step. Identify tracking errors (opened above sent), audience overlap between sequences, and the failure mode for anything under 2 percent reply. One change per weak sequence and which to fix first. Under 350 words.
cost $0.4317 · 61s · in 101,672 / out 4,685 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Rates use total step events divided by total sends; the same contact may be counted at multiple steps.

| Sequence | Sent | Open rate | Reply rate | Meeting rate | Weakest step |
|---|---:|---:|---:|---:|---|
| New Logo Nurture | 500+458+428=1,386 | 490/1,386=35.35% | 90/1,386=6.49% | 27/1,386=1.95% | 3: 18/428=4.21% reply |
| Expansion Nurture | 300+300+275=875 | 565/875=64.57% reported, **unreliable** | 59/875=6.74% | 12/875=1.37% | 3: 12/275=4.36% reply |
| Cold Outbound - HR Leaders | 600+595+590=1,785 | 545/1,785=30.53% | 8/1,785=0.45% | 0/1,785=0% | 3: 1/590=0.17% reply |
| Cold Outbound - People Ops | 400+386+377=1,163 | 340/1,163=29.23% | 29/1,163=2.49% | 6/1,163=0.52% | 3: 6/377=1.59% reply |

Tracking error: Expansion Nurture step 2 records 340 opens on 300 sends. Its aggregate open rate cannot be trusted until corrected.

Audience overlap appears between New Logo Nurture and Expansion Nurture (CT-000301, CT-000624), and between Cold Outbound - HR Leaders and Cold Outbound - People Ops (including CT-001255, CT-001105, CT-001217). Those contacts can receive competing sequences.

Under 2% reply, Cold Outbound - HR Leaders has an **open-to-reply conversion failure**: 545 opens but only 8 replies. The data cannot establish whether targeting, copy, or offer caused it. Cold Outbound - People Ops is above 2% overall, but its step 3 falls below it (6/377=1.59%), consistent with weakening response across steps—not proof of a cause.

One change per weak sequence: shorten New Logo Nurture step 3 to a single question; repair Expansion Nurture step-2 open tracking before changing copy; test a new, specific reply ask in Cold Outbound - HR Leaders step 1; replace Cold Outbound - People Ops step 3 with a different ask. **Fix Expansion tracking first for measurement integrity; then prioritize Cold Outbound - HR Leaders for performance.**
weekly-marketing-goals-rollupPASS 1.00
Plain-English question: Are we on pace on the quarter's marketing goals?
Prompt — what the model saw
Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.
cost $0.2705 · 45s · in 94,076 / out 2,296 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Q3-2026 marketing goals — 66 of 92 days elapsed (71.7%). Pace compares QTD actuals with 66/92 of each quarter-end target; for the lower-is-better MIA rate, it compares the current rate directly with the ceiling.

| Metric | QTD actual | Target | Delta vs target | Pace |
|---|---:|---:|---:|---|
| SQMs | 230 | 300 | −70 | Ahead: 230 vs 300 × 66/92 = 215.2 |
| SQOs | 84 | 120 | −36 | Behind: 84 vs 120 × 66/92 = 86.1 |
| DS2s | 40 | 75 | −35 | Behind: 40 vs 75 × 66/92 = 53.8 |
| Closed-lost MIA rate | 5/25 = 20% | ≤10% | +10 percentage points | Behind: above the ceiling |
| Same-quarter close count | 10 | 20 | −10 | Behind: 10 vs 20 × 66/92 = 14.3 |
| Active pipeline coverage against target | $3,000,000/$4,000,000 = 75% | 100% ($4,000,000) | −25 percentage points (−$1,000,000) | Ahead of a linear benchmark: $3,000,000 vs $4,000,000 × 66/92 = $2,869,565; still below the full target |

What moved this week: The provided files contain only QTD totals, with no prior-week snapshot or weekly changes. Week-over-week movement cannot be determined.
communication 5 tests
ceo-slack-compression0.80
Plain-English question: Compress the forecast analysis into one Slack message the CEO can read in ten seconds.
Prompt — what the model saw
Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.
cost $0.2246 · 26s · in 31,681 / out 1,373 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✗ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Do not treat the Q3 weighted forecast as a firm commitment: $44,729 COMMIT + 35% × $203,565 BEST_CASE = $115,976.75 across 54 in-quarter deals; PIPELINE counts for $0. Another 32 of 86 deals close after September 30 and are excluded, including COMMIT Deal-D348E1 ($13,770, October 15). All 7 in-quarter COMMIT deals lack a documented reason to buy, weakening confidence in the $44,729 committed amount.
stalled-deal-followup-emailPASS 1.00
Plain-English question: Write a follow-up email for this stalled deal — under 120 words, one prior touch, one ask.
Prompt — what the model saw
Write a follow-up email for this stalled deal. Under 120 words. Reference one specific prior touch from the engagement history, make one clear ask, and match the tone of the prior thread. No invented details.
cost $0.0739 · 10s · in 10,347 / out 402 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Hi,

Following up on my August 5 recap of our July 28 demo, which included pricing for 150 seats. Could you let me know whether that pricing works for your team?

Best,
Alex
funnel-digest-paragraphPASS 1.00
Plain-English question: Write the weekly GTM digest — one paragraph per team, every number traceable.
Prompt — what the model saw
Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.2286 · 31s · in 48,745 / out 1,500 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Marketing: We brought in 46 SQMs against a target of 52, a gap of 6 (52 − 46). Webinars delivered 18 of the 46 SQMs (18 ÷ 46 = 39.1%). Paid search cost per SQM held at $150.

Sales: We converted 14 SQOs, set 9 DS2 meetings, and created $310,000 in new pipeline. The week also brought 3 same-quarter closes.

CS: We saved 2 renewals this week. Team NPS moved to 61, and 3 red-flag accounts remain open heading into next week.

PLG: We added 412 signups, with activation at 31%. Another 38 companies reached the aha moment of 10 recognition gives.
partner-digest-sectionPASS 1.00
Plain-English question: Write the partner digest so every partner in the roster appears exactly once.
Prompt — what the model saw
Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.2328 · 27s · in 31,556 / out 1,654 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Partner digest | 2026-08-24 to 2026-09-04

- Apex Rewards Co — Active: co-webinar locked for 09-15. Two partner-sourced DS1 deals: Deal-DDAAF2 (180,000) and Deal-2CF33E (95,000). Pipeline: 180,000 + 95,000 = 275,000.
- HRCloud Partners — Active: integration referral cleared security review and moved to DS2. One partner-sourced deal: Deal-F1CDA5 (140,000).
- CultureBridge — Active: lunch-and-learn produced two early-stage partner-sourced deals: Deal-096E1D (60,000) and Deal-067213 (75,000). Pipeline: 60,000 + 75,000 = 135,000.
- WorkWell Group — Active: no sourced deals this period; Q4 joint-playbook restart under discussion, with a planning call booked for 09-09.
- Recogniq — Quiet: no activity or sourced deals in the provided data.
- KudosWave — Quiet: no activity or sourced deals in the provided data.
- PeopleFirst Advisors — Quiet: no activity or sourced deals in the provided data.
- TotalPerk — Quiet: no activity or sourced deals in the provided data.

Partner-sourced pipeline: 2 + 1 + 2 = 5 deals; 275,000 + 140,000 + 135,000 = 550,000. Currency was not specified in the deal data.
rfp-answer-from-policyPASS 1.00
Plain-English question: Answer the 10 RFP items from the policy excerpts only — no guessing.
Prompt — what the model saw
Answer the 10 questionnaire items from the policy excerpts only. Cite the excerpt id for every answer. Where the excerpts do not answer the question, mark it unanswerable and say what document would; do not guess. Tone: vendor response, direct.
cost $0.0671 · 11s · in 28,872 / out 575 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

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

Model's actual answer

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| Item | Response |
|---|---|
| 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. Prospects can obtain it under NDA. [P2] |
| Q4 | Customer data is retained for the contract duration plus 90 days, then deleted from production systems. The excerpts do not specify deletion timing outside production systems. [P3] |
| Q5 | The current 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 these excerpts. An ISO 27001 certificate or certification statement would answer this. [P1–P8] |
| Q9 | Unanswerable from these excerpts. The contractual service-level agreement would provide the uptime percentage. [P1–P8] |
| Q10 | Unanswerable from these excerpts. A HIPAA/BAA policy or approved legal agreement would answer this. [P1–P8] |
ops-maintenance 5 tests
skill-file-reconciliation0.86
Plain-English question: Audit a skill set against its manifest and flag every conflict with a severity and an action.
Prompt — what the model saw
Reconcile the skill set below against its manifest. Find and report (with severity CRITICAL/WARNING/INFO and an action type TRIM_DESC, UPDATE_BODY, DELETE_SKILL, MERGE, or REVIEW): (1) any two skills whose ALWAYS-trigger phrases overlap/duplicate, (2) any circular delegation chain between skills (name it), (3) any delegation target that does not exist (dangling, name it), (4) any version conflict and which skill should survive, (5) how many manifest descriptions exceed 1,024 characters, (6) any hardcoded page ids, dates, or person names in skill bodies, (7) manifest drift in BOTH directions (files with no manifest row, and manifest rows with no file). Make one proposal per finding; rewrite nothing.
cost $0.8680 · 72s · in 242,972 / out 5,879 tok
✓ duplicate_trigger_found✓ circular_chain✗ dangling_target✓ oversized_desc_count✓ hardcoded_values✓ manifest_drift✓ severity_and_action

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Reconciliation is limited to the 14 manifest rows and 14 skill files provided. A target absent from this set is unresolved here, not proven nonexistent elsewhere.

| Severity | Action | Finding | Proposal |
|---|---|---|---|
| WARNING | MERGE | `comms-drafter` and `email-drafter` duplicate ALWAYS-style routing for “write me an email,” “draft a follow-up,” “what should I say,” and customer-facing email review. | Make `comms-drafter` the broad communications router and `email-drafter` the email-specific implementation, with one unambiguous trigger owner. |
| WARNING | UPDATE_BODY | `pipeline-intelligence-report` and `weekly-pipeline-report` both claim “pipeline report,” “pipeline update,” and “what does pipeline look like” variants. `sales-forecast` also overlaps their broad forecast/pipeline-health routing. | Define a dispatch boundary: scored all-deal intelligence, weekly performance metrics, and current-quarter forecast, respectively. |
| WARNING | UPDATE_BODY | `next-to-close` (“which deals are most likely to close”) overlaps `deal-strategy-coach` (“which deals are likely to close”) and `sales-forecast` (“what do we think we’re going to close”). | Route a short deal shortlist to `next-to-close`, a rep coaching question to `deal-strategy-coach`, and a quarter revenue outlook to `sales-forecast`. |
| WARNING | UPDATE_BODY | Circular handoff: `deal-strategy-coach` → `email-drafter` for manager-to-prospect emails → `deal-strategy-coach` when a request includes strategy and a draft. | Make the return handoff advisory only; the originating skill retains ownership of the combined request. |
| CRITICAL | REVIEW | `prospect-research-multithreading` is invoked by `comms-drafter`, `deal-strategy-coach`, and `email-drafter`, but has no file or row in the provided set. | Verify that target exists in the intended runtime; otherwise remove or replace the delegation. |
| WARNING | REVIEW | Other referenced skill targets have no file or row in this set: `bonusly-brand`, `signalforge-reports`, and the eight `bonusly-*` specialists listed in `analysis-validator` §12.4. | Verify them against the complete runtime registry before treating these references as dangling. |
| WARNING | UPDATE_BODY | `analysis-validator` declares v3.6, but its validation-trail template says “analysis-validator v3.2.” **v3.6 should survive** as the declared, later version. | Bring the trail’s version label into line with v3.6. |
| INFO | TRIM_DESC | Manifest descriptions over 1,024 characters: **0**. Arithmetic: **0 of 14** rows; maximum supplied length is **1,006**. | No trim needed. |
| WARNING | UPDATE_BODY | Operational hardcodes appear in bodies: `deal-strategy-coach` embeds an AE Excellence Playbook page ID and 2026 pricing; `partner-digest`, `sales-forecast`, and `signalforge-feedback` embed Confluence page/folder IDs; `weekly-pipeline-report` embeds spreadsheet IDs and a fixed Q2 2026 business-day window; `pipeline-intelligence-report`, `next-to-close`, and `stale-pipeline-report` embed HubSpot org/stage IDs. | Move changeable destinations, periods, prices, and mappings to verified runtime configuration or lookups. Retain genuinely stable IDs only where explicitly verified. |
| WARNING | UPDATE_BODY | Person and company names are also embedded as operating context: the `analysis-validator` roster; `pipeline-intelligence-report` AE IDs; `weekly-pipeline-report`’s Ben Lavin; `sales-forecast`’s Alaina; `partner-digest`’s Amani and partner-contact lists; and named loss examples in `closed-lost-analysis`. Names in `signalforge-claim-compressor` and `signalforge-feedback` are examples rather than routing data. | Dynamically resolve operating rosters and recipients; label historical examples as examples. |
| INFO | REVIEW | Manifest drift in both directions: **none**. Arithmetic: **14 files − 14 matching rows = 0** file-only entries; **14 rows − 14 matching files = 0** row-only entries. | No reconciliation change needed. |
runbook-from-slack-thread0.75
Plain-English question: Turn the resolved incident thread into a runbook.
Prompt — what the model saw
Turn the resolved incident thread into a runbook: ordered steps with the exact command or action taken, who ran it, how success was verified, and rollback for each step that changed state. Every step must trace to a message in the thread. Add nothing the thread does not contain; where the thread is unclear, mark the step as needing confirmation.
cost $0.1041 · 22s · in 39,798 / out 1,089 tok
✗ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Runbook reconstructed from the resolved thread. It records what happened, not approval to repeat an action where execution details are missing.

| Step | Command or action and actor | Success verification recorded | Rollback |
|---|---|---|---|
| 1 [M01] | Bryce Harmon acknowledged the PagerDuty alert and took IC. | Alert reported reward-worker queue depth > 10k; acknowledgement was not independently verified. | Not documented — needs confirmation. |
| 2 [M02] | Farid Osman ran `bundle exec rake sidekiq:queue_depth`. | Returned 48,213 pending reward jobs; normal was reported as under 500. | No state change. |
| 3 [M03] | Farid Osman reported inspecting the dead set; command not given — needs confirmation. | Reported 112 jobs, all `Redis::TimeoutError` from around 13:58. | No state change documented. |
| 4 [M04] | Farid Osman paused enqueue with `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`. | No direct flag-state verification recorded — needs confirmation. | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` [M04]. |
| 5 [M05] | Elena Sinclair cleared the dead set in the console. Exact command needs confirmation. | No verification recorded — needs confirmation. | Not documented — needs confirmation. |
| 6 [M06] | Bryce Harmon ran `kubectl scale deployment/reward-worker --replicas=6` (from 3). | No direct replica-count verification recorded — needs confirmation. | `kubectl scale deployment/reward-worker --replicas=3` [M06]. |
| 7 [M07] | Farid Osman reported queue progress; measurement command not given — needs confirmation. | Queue depth reported at 9,400 and falling ~1,200/min. | No state change documented. |
| 8 [M08] | Cole Ingram ran `bundle exec rake sidekiq:queue_depth` and checked Datadog. | Command returned 0; Datadog error rate was back to baseline. | No state change. |
| 9 [M09] | Bryce Harmon re-enabled enqueue with `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`. | Verified 40 new jobs processed cleanly in the next 3 minutes. | Not documented — needs confirmation. |
| 10 [M10] | Bryce Harmon ran `kubectl scale deployment/reward-worker --replicas=3`. | Queue reported stable at 0; incident declared resolved. Replica count was not directly verified — needs confirmation. | Not documented — needs confirmation. |
datadog-incident-timeline0.60
Plain-English question: Sequence the failure from the logs — what broke first, and what cascaded after it?
Prompt — what the model saw
From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.
cost $0.2527 · 34s · in 36,172 / out 2,317 tok
✗ first_error_ts✓ service_named✓ cascade_ordered✓ query✗ gaps_stated

✓ Correct Answer (ground truth)

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

Model's actual answer

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- First error: 2026-09-03 14:01:12Z, `reward-service` logged `Redis::TimeoutError`: its connection to `redis-primary:6379` timed out after 5 seconds.
- Cascade, in log order: `reward-service` exhausted retries for `RewardGiveJob` (14:01:20Z onward); `sidekiq` logged `RewardGiveJob` failures and retries (from 14:01:40Z); the reward queue exceeded 10,000 (14:02:30Z); `api-gateway` returned a 502 timeout calling `reward-service /gives` (14:03:05Z); `web-app` logged a failed Give form submission from that 502 (14:03:30Z). The job file also records Redis timeouts for `RecognitionDigestJob` beginning at 14:02:36Z. That file contains 12 `RewardGiveJob` failures + 4 `RecognitionDigestJob` failures = 16 recorded failures.
- Recovery observed: `reward-service` logged Redis connection restored at 14:22:10Z; `sidekiq` logged reward queue depth below 500 at 14:24:45Z.
- Datadog confirmation query: `service:reward-service "Redis::TimeoutError" "redis-primary:6379"` with a UTC window covering 2026-09-03 14:01:12Z.
- The slice does not show why Redis timed out, what restored the connection, the total number of affected submissions, or whether every failed job ultimately succeeded.
feature-flag-state-summaryPASS 1.00
Plain-English question: Summarize what each feature flag controls and who has it on.
Prompt — what the model saw
Summarize the state of the listed feature flags: what each controls per the code excerpt, which companies or segments have it on, targeting rules, and any flag with no code reference or no targets. Company counts must match the export. Do not describe a flag that is not in the export.
cost $0.2478 · 37s · in 32,770 / out 2,396 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
| Flag | State | What the code excerpt shows | Targeting rule and export count |
|---|---|---|---|
| `recognition_streaks_v2` | On | Records a give with `StreakTracker`. | `segment:beta_companies` — 42 companies |
| `points_budget_guardrails` | On | Enforces giver/points budget rules. | `all_companies` — 220 companies |
| `slack_dm_nudges` | On | Allows the nudge job to send a Slack DM. | `segment:region_na` — 87 companies |
| `redeem_flow_redesign` | Off | Renders `RedeemV2Component` when enabled; otherwise `RedeemV1Component`. | `targeted_list` — 12 companies listed as targets, but flag off |
| `analytics_dashboard_v3` | On | Sets the dashboard to `AnalyticsV3`. | `segment:tier_three` — 65 companies |
| `ms_teams_app_v2` | Off | Installs `TeamsAppV2` when enabled. | `targeted_list` — 9 companies listed as targets, but flag off |
| `legacy_give_modal` | Off | No reference in the provided code excerpt. | `segment:legacy_plan` — 14 companies listed as targets, but flag off |
| `survey_boosters_q3` | On | No reference in the provided code excerpt; what it controls is unknown. | `segment:legacy_plan` — 7 companies |
| `paused_offboard_cleanup` | Off | No reference in the provided code excerpt. | No targeting rule; 0 companies |

The export names segments and targeting rules, not individual companies, so company names cannot be identified. Counts are per flag; overlap between flags is unknown. Arithmetic check: 5 on + 4 off = 9 exported flags; 6 with code references + 3 without = 9. Source: `flags_export.csv` and `flag_code.md` as provided.
nda-triagePASS 1.00
Plain-English question: Triage each NDA green, yellow, or red — and quote the clause that decides it.
Prompt — what the model saw
Triage each NDA as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Cite the clause that drives each classification by quoting it. Catch embedded non-solicits, non-competes, missing carve-outs, and unusual term or governing law. One line of reasoning per NDA.
cost $0.1482 · 20s · in 39,364 / out 1,024 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

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

Model's actual answer

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NDA-1 — [PARTY A] — GREEN (standard approval): Mutual confidentiality, stated carve-outs, and Delaware law; clause 3 includes information “independently developed” and information that “must be disclosed by law or court order.”

NDA-2 — [PARTY B] — YELLOW (counsel review): Clause 4 adds an employment restriction: “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 — [PARTY C] — RED (full legal review): Clause 2 prohibits Recipient from “engag[ing] in or provid[ing] services to any business that competes with Discloser’s business”; clause 4 states “No confidentiality carve-outs are stated,” while clauses 1 and 3 impose one-way confidentiality for “five (5) years” and exclusive jurisdiction in the “Republic of Ireland.”