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inception/mercury-2.5-preview

Mean 0.868 · 27/40 perfect tests · $0.10 total · each card: the question → the correct answer (gold) → this model's actual answer

data-crm
stage-hygiene-audit0.17

? The Question

Audit stage hygiene on the open pipeline below. Rules: a deal at DS2 or later must have a substantive why-buys (15+ characters, not a placeholder such as tbd, n/a, see notes). A why_buys value of the form LONG(n) means the real text was present but truncated for display; treat it as present and substantive. Every deal should have a DS1 entry timestamp. A deal whose stage entry timestamps are out of order regressed (an earlier-stage timestamp is later than a later-stage timestamp). Report: percent of DS2+ deals non-compliant on why-buys overall and by owner (use the owners table for names), count of deals with no DS1 timestamp, count that regressed, and three process fixes. State definitions before numbers.
cost $0.0018 · 9s · in 34,214 / out 2,968 tok
✗ noncomp_pct✗ no_ds1_0✗ regressed_0✗ per_owner_breakdown✗ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

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

Model's actual answer

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To clarify the requested metrics:
- **DS2+ non-compliant on why-buys:** Deals with stage DS2, DS3, DS4, or DS5 where `why_buys` is empty, blank, or a placeholder like tbd/n/a/see notes. `LONG(n)` is treated as compliant (substantive).
- **Count no DS1 timestamp:** Any deal where `t_ds1` is empty or blank.
- **Count regressed:** Any deal where `t_ds1` > `t_ds2` > `t_ds3` > `t_ds4` > `t_ds5` timestamps exist but are chronologically out of order (earlier stage entered after later stage).
- **Percent by owner:** Non-compliant DS2+ deals / Total DS2+ deals for that owner.

### Summary Metrics
- **DS2+ deals overall:** 83 deals
- **DS2+ why-buys non-compliant (overall):** 74 deals
- **Percent non-compliant (overall):** 89.2% (74/83)
- **Count with no DS1 timestamp:** 12 deals
- **Count regressed:** 17 deals

### Non-Compliant Why-Buys By Owner (DS2+ Deals)
- **Bryce Harmon:** 61% (26/43 deals DS2+; 26 non-compliant)
- **Alex Franklin:** 93% (41/44 deals DS2+; 41 non-compliant)
- **Dana Mercer:** 89% (17/19 deals DS2+; 17 non-compliant)
- **Cole Ingram:** 100% (5/5 deals DS2+; 5 non-compliant)
- **Farid Osman:** 50% (2/4 deals DS2+; 2 non-compliant)

### Top 3 Process Fixes
1. Enforce a mandatory `why_buys` text field on stage change to DS2, blocking transitions if empty or a placeholder (allow `LONG()` imports as compliant).
2. Add automated timestamp integrity checks on pipeline refresh to flag or halt moves where `t_ds1` ≥ `t_ds2` ≥ `t_ds3` ≥ `t_ds4` ≥ `t_ds5`.
3. Require all deals with `t_ds1` populated; auto-archive or re-open to DS0 any deal missing `t_ds1` when it reaches DS2+.
owner-resolution0.17

? The Question

The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.
cost $0.0021 · 9s · in 36,366 / out 3,249 tok
✗ resolved_count✗ unmatched_none✓ deactivated_flagged✗ jake_amount✗ justin_amount✗ no_invented

✓ Correct Answer (ground truth)

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

Model's actual answer

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### GTM Analysis: Deal-to-Owner Resolution

**1. Number of Deals Resolved:** 280 out of 280 (100%)

**2. Deals with Owner IDs having No Match in Owners Table:** None (0 deals)

**3. Owner IDs that Map to Archived/Deactivated Owners:**
- Gavin Porter (1520255671)
- Hugo Lindqvist (77260721)
*Note: Neither of these archived owners appears on any of the 280 open deals.*

**4. Total Pipeline Amount per Resolved Owner:**
- Bryce Harmon: $1,494,776.00
- Alex Franklin: $1,260,390.00
- Dana Mercer: $545,094.00
- Cole Ingram: $577,138.18
- Farid Osman: $17,703.00
- Elena Sinclair: $2,100.00

**Total Open Pipeline:** $3,897,201.18
gong-hubspot-join-integrity0.75

? The Question

Check the integrity of the call-to-deal mapping below. The gong table lists calls in the last 90 days keyed to HubSpot deal ids; deals_open lists the current open default-pipeline deals. Report: (a) count of gong rows whose deal id has NO match in deals_open (orphans; list a few sample aliases), (b) duplicate conversation keys (rows where calls_90d exceeds distinct_conversation_keys), (c) among the open deals that ARE at stage DS3 or later, the share that have at least one logged call. Give sample aliases for the orphan finding, all taken from the data. Note: gong_owner_id is a Gong user id, not a HubSpot owner id.
cost $0.0122 · 28s · in 201,867 / out 26,969 tok
✓ orphan_count✗ dup_keys_0✓ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

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

Model's actual answer

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### (a) Orphan Call Rows
- **Count:** 30 out of 67 Gong rows have deal IDs not found in `deals_open.csv`
- **Sample Orphan Aliases:**
  - Deal-8FA85D
  - Deal-8FC3F9
  - Deal-3B7945
  - Deal-42B265
  - Deal-9CCC42

### (b) Duplicate Conversation Keys
- **Count:** 0 rows where `calls_90d` exceeds `distinct_conversation_keys`
- **Finding:** All 67 Gong rows show matching call counts and distinct conversation keys.

### (c) Late-Stage Deals with Logged Calls
- **Total Open Deals at Stage DS3 or Later:** 85
- **Deals with ≥1 Logged Call:** 25
- **Share:** 25/85 = **29.41%**
snowflake-sql-trapped-catalogPASS 1.00

? The Question

Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.
cost $0.0010 · 9s · in 16,508 / out 2,558 tok
✓ avoids_stale_deals✓ avoids_unpopulated_live✓ uses_cohort_summary✓ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

graded checks: avoids_stale_deals · avoids_unpopulated_live · uses_cohort_summary · succeeded_filter · no_giver_deletion_filter · returns_sql

Model's actual answer

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```sql
-- The catalog provides PRODUCTION.PLG.COMPANY_COHORT_SUMMARY which is the only table 
-- storing per-company first-month metrics (M1 columns). There is no individual 
-- giving/recognition event table provided to derive unique_givers vs recognition_count separately.
-- M1_GIVES is used for the giving metric as per catalog definition.
SELECT 
    SIGNUP_DATE as first_customer_month,
    M1_GIVES as giving_count, -- Column available; unique givers vs total recognitions not distinct in catalog schema
    M1_REDEMPTIONS as successful_redemption_count -- Derived from STATE='succeeded' per catalog notes
FROM 
    PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
```
crm-completeness-enrichmentPASS 1.00

? The Question

Audit the CRM extract for completeness and propose fixes. Deals need owner, stage, amount, close date, why-buys; companies need industry, employee count, HQ country; contacts need email, title, persona. Report completeness percent per field. Identify duplicate company clusters (name variants, shared domain) with a survivor per cluster. List invalid emails and domain mismatches. Fill missing company fields only where the enrichment export has a matching row; where CRM and enrichment disagree list both and recommend a source. Never invent a value. End with the 10 fixes with the most pipeline amount at stake.
cost $0.0008 · 10s · in 2,135 / out 4,383 tok
✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

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

Model's actual answer

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## 1. Deal Audit Status
**Deal audit not possible.** No deals.csv file is provided. Deal fields (owner, stage, amount, close date, why-buys) cannot be audited without this data.

## 2. Company Completeness (34 records)
| Field | Empty | % Empty | Notes |
| :--- | :--- | :--- | :--- |
| **Industry** | 0 | 0% | All present, but needs normalization (e.g., "tech" vs "Technology") |
| **Employee Count** | 10 | 29% | C-EC3025, C-96039F, C-44EA29, C-D04904, C-60C75F, C-B23205, C-7BBDFA, C-50D386, C-93C8BF, C-EE9FFB |
| **HQ Country** | 9 | 26% | C-2D1F1B, C-D73B89, C-44EA29, C-D04904, C-2C60E5, C-7BBDFA, C-50D386, C-EE9FFB, C-93C8BF |

## 3. Contact Completeness (51 records)
| Field | Empty/Invalid | % | Details |
| :--- | :--- | :--- | :--- |
| **Email** | 5 | 10% | 4 missing domain (CT-0010, CT-0080, CT-0081, CT-0192); 1 mismatched domain (CT-0011) |
| **Title** | 14 | 27% | Includes C-C6FE92 (CT-0022), C-AA8DDA (3), C-B23205 (CT-0132), C-E51FB7 (CT-0141) |
| **Persona** | 16 | 31% | Includes C-2D1F1B (CT-0000), C-D73B89 (CT-0041), C-77A95A (CT-0110) |

## 4. Duplicate Company Clusters
| Domain | CRM Records (Alias) | Recommendation |
| :--- | :--- | :--- |
| **acme-corp.com** | C-0A092931 (500, US) vs C-0A092932 (510, USA) | Keep C-0A092931; normalize "USA" → "US" |
| **globex.io** | C-0A092933 (SaaS, 200) vs C-0A092934 (Technology, 200) | Keep C-0A092933; normalize "Technology" → "SaaS" |

## 5. Company Field Disagreements (CRM vs. Enrichment)
| Alias | Domain | Field | CRM Value | Enrichment Value | Recommendation |
| :--- | :--- | :--- | :--- | :--- | :--- |
| C-EC3025 | ec3025.com | employee_count | **Missing** | 400 | Use enrichment |
| C-96039F | 96039f.com | employee_count | **Missing** | 400 | Use enrichment |
| C-96039F | 96039f.com | hq_country | USA | United States | Normalize to US |
| C-44EA29 | 44ea29.com | employee_count | **Missing** | 400 | Use enrichment |
| C-44EA29 | 44ea29.com | hq_country | **Missing** | **Missing** | Unresolvable |
| C-44EA29 | 44ea29.com | industry | tech | Computer Software | Normalize to Technology |
| C-D04904 | d04904.com | employee_count | **Missing** | 400 | Use enrichment |
| C-D04904 | d04904.com | hq_country | **Missing** | **Missing** | Unresolvable |
| C-60C75F | 60c75f.com | employee_count | **Missing** | 400 | Use enrichment |
| C-60C75F | 60c75f.com | industry | tech | Computer Software | Normalize to Technology |
| C-60C75F | 60c75f.com | hq_country | United States | United States | Normalize to US |
| C-2C60E5 | 2c60e5.com | employee_count | 340 | 340 | Agree |
| C-2C60E5 | 2c60e5.com | hq_country | **Missing** | **Missing** | Unresolvable |
| C-7BBDFA | 7bbdfa.com | employee_count | **Missing** | 400 | Use enrichment |
| C-7BBDFA | 7bbdfa.com | industry | health care | health care | Normalize to Healthcare |
| C-50D386 | 50d386.com | employee_count | **Missing** | 400 | Use enrichment |
| C-50D386 | 50d386.com | industry | health care | health care | Normalize to Healthcare |

## 6. Invalid Emails & Domain Mismatches
| Contact Key | Company Alias | Email | Issue |
| :--- | :--- | :--- | :--- |
| CT-0010 | C-66D1FC | user0@ | Missing domain |
| CT-0011 | C-66D1FC | user1@other-domain.com | Domain mismatch (66d1fc.com) |
| CT-0080 | C-92D97D | user0@ | Missing domain |
| CT-0081 | C-92D97D | user1@ | Missing domain |
| CT-0192 | C-425E2A | user2@ | Missing domain |

## 7. Top 10 Fixes by Pipeline Amount at Stake
**Note:** No deal/amount data was provided in the input files. Consequently, I cannot identify specific pipeline dollar values. However, these are the highest-impact fixes required to clean data integrity:

1.  **Merge C-0A092931 / C-0A092932 (acme-corp.com)** – Potential 100% record duplication.
2.  **Merge C-0A092933 / C-0A092934 (globex.io)** – Potential 100% record duplication.
3.  **Fix CT-0011 email (C-66D1FC)** – Mismatched domain invalidates outreach.
4.  **Fix CT-0010 email (C-66D1FC)** – Invalid email format blocks communication.
5.  **Fix CT-0080 email (C-92D97D)** – Invalid email format blocks communication.
6.  **Fix CT-0081 email (C-92D97D)** – Invalid email format blocks communication.
7.  **Fill C-EC3025, C-96039F, C-D04904 employee counts** – Blocks ICP segmentation.
8.  **Normalize "health care" to "Healthcare" for C-7BBDFA, C-50D386** – Ensures category integrity.
9.  **Normalize "tech" to "Technology" for C-44EA29, C-60C75F** – Ensures category integrity.
10. **Fill missing HQ countries for C-7BBDFA, C-50D386** – Critical for regional reporting.
deal-intelligence
closed-lost-classification0.80

? The Question

Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.
cost $0.0011 · 8s · in 7,506 / out 5,269 tok
✗ total_90✓ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
<tool_code>
print("""
## Classification

### Deal-by-Deal (27 deals)

| Deal Alias | Tag | Reason Text | Primary Category | Side |
|---|---|---|---|---|
| Deal-DB0AAC | Lost- Timing (1 year or more) | rescheduled a lot of meetings... putting this on pause for now | Timing | Buyer |
| Deal-F7F635 | Competitor | group decided to go in another direction | Competitor | Buyer |
| Deal-AC944F | MIA | unresponsive | No Decision | Unknown |
| Deal-214060 | MIA | unresponsive | No Decision | Unknown |
| Deal-91A056 | Lost- Timing (1 year or more) | asked to reconnect early in 2027 | Timing | Buyer |
| Deal-29326C | Lost- Timing (1 year or more) | Timing | Timing | Buyer |
| Deal-5DB9B0 | Lost- Does not fit ICP (write in notes) | Spam | Product Gap | Buyer |
| Deal-831B7B | Lost- Timing (1 year or more) | I'm hoping we'll be able to look at this again in the new year | Timing | Buyer |
| Deal-F97C37 | Competitor | other vendor had more diversified offerings | Competitor | Buyer |
| Deal-13E9CF | Doing nothing/Not a priority/Cost | deprioritized by the org. Need to reach out next year | No Decision | Buyer |
| Deal-39E25C | Lost- Timing (1 year or more) | Timing, reconenct next year | Timing | Buyer |
| Deal-7ED004 | Lost- Budget/Price | Did not get budget approval | Pricing | Buyer |
| Deal-21B045 | MIA | MIA | No Decision | Unknown |
| Deal-B3ABED | Lost- Timing (1 year or more) | MIA- We'll revisit this again likely in Q2 next year to try and get budget for in 2028 | Timing | Buyer |
| Deal-422BA6 | Competitor | Executive team chose a competing vendor | Competitor | Buyer |
| Deal-ED9AE7 | Lost DM | Timing, budget, authroity | No Decision | Buyer |
| Deal-988493 | MIA | mia | No Decision | Unknown |
| Deal-381C8C | Competitor | not going to be moving forward with Bonusly | Competitor | Buyer |
| Deal-F308CA | MIA | No contact since intro in April | No Decision | Unknown |
| Deal-F1E8A6 | Competitor | said they are not going to be moving forward with Bonusly | Competitor | Buyer |
| Deal-B6AC09 | Lost- Timing (1 year or more) | revisiting in 2027 | Timing | Buyer |
| Deal-70F704 | Lost DM | They were only looking to automate anniversary awards and have been MIA | Product Gap | Buyer |
| Deal-E6E80A | Lost- Timing (1 year or more) | Got pushed into early 2027 | Timing | Buyer |
| Deal-B038F0 | Lost- Timing (1 year or more) | Got pushed back into early 2027 | Timing | Buyer |
| Deal-4664E1 | MIA | No contact after intro | No Decision | Unknown |
| Deal-175756 | Lost- Timing (1 year or more) | Due to other priorities they are putting this on hold until 2027 | No Decision | Buyer |
| Deal-E74A73 | Doing nothing/Not a priority/Cost | The team would first like to test the points calculation manually | No Decision | Buyer |
| Deal-DDAB52 | Competitor | Rippl - platform offers a lot more at the same cost | Competitor | Buyer |
| Deal-ACE061 | Competitor | I feel they went with HeyTaco | Competitor | Buyer |
| Deal-BB78F3 | Lost- Timing (1 year or more) | Bonusly is still something we're interested in... leadership would like us to roll out a few plant-specific action items | Product Gap | Buyer |
| Deal-D48E0B | MIA | MIA | No Decision | Unknown |
| Deal-15DA99 | Lost- Timing (1 year or more) | Timing, looking to bring it back up eaerly 2027 | Timing | Buyer |
| Deal-F4AF5D | Lost- Timing (1 year or more) | Timing looking at early next year | Timing | Buyer |
| Deal-79B7A1 | Lost- Timing (1 year or more) | Timing | Timing | Buyer |
| Deal-583ADB | MIA | MIA | No Decision | Unknown |
| Deal-8E27DA | Feature Request | They moved forward with just a swag provider and didn't want R&R | Product Gap | Buyer |
| Deal-2D2F8D | Competitor | Decided to move in a different direction | Competitor | Buyer |
| Deal-E0441F | MIA | Was stale when I inherited it | No Decision | Unknown |
| Deal-7CB44D | MIA | No meaningful contact since demo | No Decision | Unknown |
| Deal-0F96AA | Competitor | After a thorough evaluation, we won't be advancing Bonusly | Competitor | Buyer |
| Deal-1BCA50 | Competitor | It was mostly about the budget and details... stakeholder was already way down the path with another vendor | Competitor | Buyer |
| Deal-7CC678 | Competitor | Nothing specific provided | Competitor | Buyer |
| Deal-FAC17C | Lost DM | Contract has been out two months but they couldn't get final approval | No Decision | Buyer |
| Deal-242273 | Competitor | Both of our top two vendors were able to help us solution our need to digitize... onsite facilities | Competitor | Buyer |
| Deal-50E5D8 | Doing nothing/Not a priority/Cost | Leadership decided the company is going to pause on this for now | No Decision | Buyer |
| Deal-A2C349 | Competitor | we've decided to stick with Awardco for our recognition needs | Competitor | Buyer |
| Deal-9F176A | Lost- Timing (1 year or more) | We've put a pause on this work and I don't anticipate it picking back up until closer to the end of the year | Timing | Buyer |
| Deal-7B2236 | Doing nothing/Not a priority/Cost | It was a combination of budget and a shift in what they wanted out of the Kudos board | Pricing | Buyer |
| Deal-AFA56C | MIA | unresponsive | No Decision | Unknown |
| Deal-C7156E | Competitor | After careful consideration we have selected another vendor | Competitor | Buyer |
| Deal-C33D91 | Lost- Budget/Price | company going through significant budget cuts | Pricing | Buyer |
| Deal-9048EB | MIA | Confirmed with CS and Sales leadership moving to C/L is the best move. No meaningful contact since April and it was a bad fit | Product Gap | Unknown |
| Deal-5E64CE | Doing nothing/Not a priority/Cost | The fee for getting out of the Nectar agreement is a lot | Other (Contract) | Buyer |
| Deal-8A0992 | Competitor | Went with a Canadian provider that more closely aligns | Competitor | Buyer |
| Deal-D0C698 | Competitor | Her client is a past user of Kudos and wants to use that platform again | Competitor | Buyer |
| Deal-69CF3D | Lost- Timing (1 year or more) | On Hold | Timing | Buyer |
| Deal-ECBF89 | Lost- Timing (1 year or more) | On Hold for now | Timing | Buyer |
| Deal-3618CC | Lost DM | Wanted Surveys | Product Gap | Buyer |
| Deal-EECC02 | Competitor | Went another direction | Competitor | Buyer |
| Deal-5AD03E | Competitor | Wanted more defined budget access | Pricing | Buyer |
| Deal-D1A623 | Lost- Timing (1 year or more) | timing | Timing | Buyer |
| Deal-413C56 | Doing nothing/Not a priority/Cost | Back to school is priority and CEO not ready | No Decision | Buyer |
| Deal-47F1A1 | Competitor | Staying with WorkTango for another 12 months | Competitor | Buyer |
| Deal-BF2A98 | Competitor | Recently deployed HiThrive within the org | Competitor | Buyer |
| Deal-2A292B | Doing nothing/Not a priority/Cost | going to build something simple internally | Other (Build) | Buyer |
| Deal-D1AABF | MIA | No response | No Decision | Unknown |
| Deal-FEDBCB | Doing nothing/Not a priority/Cost | Wanted to reconnect closer to the end of the year but was not super engaged | No Decision | Buyer |
| Deal-1E7DA9 | Competitor | Thank you for all the time... We have selected another platform | Competitor | Buyer |
| Deal-2BBA21 | MIA | No contact since intro call | No Decision | Unknown |
| Deal-286F9C | Competitor | But we decided to go with another platform. Bonusly sounds great but it's not really a good fit for us | Product Gap | Buyer |
| Deal-7FBAC6 | Doing nothing/Not a priority/Cost | Leadership has made the decision to pause (again) for now | No Decision | Buyer |
| Deal-369281 | Competitor | went with what they have in paylocity | Competitor | Buyer |
| Deal-386F6E | MIA | No response | No Decision | Unknown |
| Deal-9FCD0D | Competitor | The team chose to go with a Canadian company | Competitor | Buyer |
| Deal-55867E | Lost- Timing (1 year or more) | Thank you for checking in... I don't think we'll be moving forward with Bonusly at this time | Timing | Buyer |
| Deal-DAFB82 | Lost- Budget/Price | Right now there are too many other company priorities that budget is needed for and they don't see this being budgeted in until 2028 | Pricing | Buyer |
| Deal-2FEDDB | Doing nothing/Not a priority/Cost | Unsure on timing that she is going to be able to get this moving | No Decision | Buyer |
| Deal-64B19A | Competitor | Likely stayed with Motivosity | Competitor | Buyer |
| Deal-3F86A0 | MIA | unresponsive | No Decision | Unknown |
| Deal-096750 | MIA | No meaningful contact after intro | No Decision | Unknown |
| Deal-F325A5 | Lost DM | Layoffs and Change in Leadership - this is no longer a priority right now | No Decision (Champion Left) | Buyer |
| Deal-ABD14C | Doing nothing/Not a priority/Cost | Not interested in signing up for our program | No Decision | Buyer |
| Deal-79E61A | MIA | Unresponsive | No Decision | Unknown |
| Deal-8A119B | Lost- Budget/Price | Didn't get approval | Pricing | Buyer |
| Deal-AE7C4E | MIA | Unresponsive | No Decision | Unknown |
| Deal-DAB4F1 | MIA | Unresponsive | No Decision | Unknown |
| Deal-B4B50F | MIA | Unresponsive | No Decision | Unknown |
| Deal-981AD4 | Feature Request | Doesn't fit UI and not UK focused | Product Gap | Buyer |
| Deal-DC77FE | Competitor | Bonusly was a very strong contender... I found a system that offered a bit more in terms of customization | Competitor | Buyer |
| Deal-5885B9 | MIA | MIA | No Decision | Unknown |
""")
</tool_code>
pipeline-tieringPASS 1.00

? The Question

Score the open pipeline below on these signals: forecast category, stage, meetings_30d, contact count, and engagement recency. Assign exactly one tier per deal: LOCK, ACTION, BUILD, REVIVE, WATCH, or RISKY (RISKY = the forecast category disagrees with the engagement evidence). Never tier a deal LOCK if it has zero meetings_30d. Do NOT list every deal. Return ONLY a compact JSON object with exactly these keys: - "tier_counts": object mapping each tier to the number of deals in it (must sum to the total deal count), - "tier_examples": object mapping each tier to up to 3 example deal aliases from the data, - "risky_deals": array of the deal aliases you tiered RISKY, - "lock_violations": number of deals tiered LOCK that have zero meetings_30d (should be 0), - "pipeline_shape": one short paragraph on the overall pipeline shape. Note: inbound_emails_30d is 0 for every row due to a data defect, so treat meetings_30d as the inbound signal.
cost $0.0047 · 13s · in 101,975 / out 3,255 tok
✓ json_returned✓ tier_vocabulary✓ tier_counts_sum✓ risky_deals_listed✓ no_lock_violation_claimed

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
{"tier_counts":{"LOCK":3,"ACTION":13,"BUILD":104,"REVIVE":1,"WATCH":0,"RISKY":35},"tier_examples":{"LOCK":["Deal-D348E1","Deal-C26D20","Deal-403845"],"ACTION":["Deal-25F752","Deal-944310","Deal-3974EB"],"BUILD":["Deal-2D1F1B","Deal-66D1FC","Deal-950043"],"REVIVE":["Deal-6787C2"],"WATCH":[],"RISKY":["Deal-E53952","Deal-5408B0","Deal-9AAE5F"]},"risky_deals":[],"lock_violations":0,"pipeline_shape":"The pipeline is heavily weighted toward early-to-mid stages (DS1–DS3) with 156 total opportunities. While there are 3 locked deals showing strong late-stage engagement, a significant portion of 'committed' forecast category deals (35) shows zero meetings in the last 30 days, indicating forecasting hygiene issues and potential churn risk. The downstream volume is concentrated in DS4/DS5, but engagement consistency needs review."}
call-transcript-extractionPASS 1.00

? The Question

For each transcript, extract CRM write-back fields as JSON: why-buys (prospect statements only), pain points, stakeholders from the speaker list, budget signal (prospect-stated or null), timeline signal, competitor mentioned (only if the prospect raised it), next step (only if explicitly agreed), objections, confidence. Never fill a field from what the rep said or hoped.
cost $0.0012 · 6s · in 18,166 / out 3,140 tok
✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

rep: Alex Franklin
per_transcript: {"TX-001": {"why_buys": ["automating anniversary and birthday awards"], "pain_points": ["HR team of three cannot keep up with it manually", "track everything in a spreadsheet, and people slip through
rep_sourced_nulls: {"TX-003": ["budget_signal \u2014 only the rep mentioned pricing/budget"], "TX-004": ["next_step \u2014 rep proposed, prospect said 'no promises'"], "TX-006": ["budget_signal \u2014 rep decoy, prospec
allowed_new: TX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar
graded checks: json_emitted · budget_signal · null_discipline · stakeholders · competitor_prospect_only

Model's actual answer

show the model's full answer
```json
[
  {
    "deal_alias": "Deal-CFE7F4",
    "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 the cracks"],
    "stakeholders": ["VP People", "HR Admin"],
    "budget_signal": "$40k earmarked for engagement tools this fiscal year",
    "timeline_signal": "live before open enrollment in November",
    "competitor_mentioned": "Achievers",
    "next_step": "security review on September 12",
    "objections": ["need SSO and audit logs for IT to sign off"],
    "confidence": "high"
  },
  {
    "deal_alias": "Deal-70BB30",
    "why-buys": ["tie recognition to retention for our hourly workforce"],
    "pain_points": ["regretted turnover there is over 30%"],
    "stakeholders": ["Head of Total Rewards", "CFO"],
    "budget_signal": "$25k pilot budget for this quarter",
    "timeline_signal": "decision by end of September",
    "competitor_mentioned": null,
    "next_step": "send the pilot agreement and we'll route it to legal this week",
    "objections": ["Integration with Workday has to be rock solid"],
    "confidence": "high"
  },
  {
    "deal_alias": "Deal-530B50",
    "why-buys": ["make recognition visible across our 12 retail locations"],
    "pain_points": ["Store managers have zero budget autonomy for on-the-spot recognition today"],
    "stakeholders": ["People Ops Manager", "CEO"],
    "budget_signal": null,
    "timeline_signal": "no rush on our side until Q1",
    "competitor_mentioned": "Bucketlist",
    "next_step": "schedule a call with our CEO — I'll send two times",
    "objections": ["The CEO has to be sold first — she decides anything people-related"],
    "confidence": "medium-low"
  },
  {
    "deal_alias": "Deal-180D02",
    "why-buys": ["consolidate three separate recognition tools into one"],
    "pain_points": ["paying for three tools and none of them talk to our HRIS"],
    "stakeholders": ["VP People", "IT Security Lead"],
    "budget_signal": "under $15k annually",
    "timeline_signal": "procurement cycle runs six to eight weeks minimum",
    "competitor_mentioned": null,
    "next_step": null,
    "objections": ["security review took three months for our last vendor — that's my hesitation"],
    "confidence": "low-medium"
  },
  {
    "deal_alias": "Deal-F8767A",
    "why-buys": ["automate service milestones", "give us analytics on recognition equity across departments"],
    "pain_points": ["night-shift teams feel invisible — their engagement scores run 20 points lower", "exec team is skeptical after a failed rollout two years ago"],
    "stakeholders": ["HR Director", "People Ops Coordinator"],
    "budget_signal": "$12k approved under our engagement line",
    "timeline_signal": "running before our January all-hands",
    "competitor_mentioned": "Nectar",
    "next_step": "come present to our exec team on October 2",
    "objections": ["exec team is skeptical after a failed rollout two years ago"],
    "confidence": "medium"
  },
  {
    "deal_alias": "Deal-EE195F",
    "why-buys": ["cut the admin time on service awards"],
    "pain_points": ["I personally spend five hours a month ordering and shipping plaques"],
    "stakeholders": ["HR Manager", "COO"],
    "budget_signal": null,
    "timeline_signal": "Q1 start is realistic",
    "competitor_mentioned": null,
    "next_step": "send the one-page overview and I'll forward it to our COO this week",
    "objections": ["COO usually prefers building things in-house"],
    "confidence": "medium"
  }
]
```
next-to-closePASS 1.00

? The Question

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.
cost $0.0005 · 7s · in 2,596 / out 1,955 tok
✓ exactly_three✓ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

top3: rank=1; deal_alias=Deal-547B2B; amount=11200; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11 | rank=2; deal_alias=Deal-B7EBD1; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-10 | rank=3; deal_alias=Deal-403845; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11
exactly_three: True
blocked_excluded: deal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)
ranking_rule: Slack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc
graded checks: exactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

show the model's full answer
Based on CRM close dates and Slack status updates:

1.  **Deal-547B2B** ($11,200)
    *   **Why:** Slack confirms the prospect's VP People explicitly stated they are signing tomorrow; the owner described it as "signature-imminent."
    *   **Left:** Final execution.

2.  **Deal-403845** ($9,000)
    *   **Why:** Slack reports the order form moved to the prospect's finance team with no blockers reported.
    *   **Left:** Finance approval and signature.

3.  **Deal-A2B47C** ($6,360)
    *   **Why:** Slack notes this deal is "still warm, just normal legal-review pace."
    *   **Left:** Legal sign-off.

*Note:* Deal-2465CE ($5,400) was pulled from commit due to a procurement freeze and champion departure. Deal-B7EBD1 ($9,000, Sept 10 close) lacks Slack confirmation of movement compared to the three above.
product-gap-detectionPASS 1.00

? The Question

Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.
cost $0.0004 · 8s · in 1,914 / out 1,644 tok
✓ real_gaps✓ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
True Product Gaps (Real Gaps Only)

**1. Deal: Deal-EC3025**
Prospect Line: "We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."
Classification: Real Gap (Docs state SCIM user provisioning is NOT listed as a supported capability)
Deal Amount: Not provided in data

**2. Deal: Deal-D0D6B5**
Prospect Line: "Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."
Classification: Real Gap (Docs state ADP Workforce Now integrations are NOT listed as supported capabilities)
Deal Amount: Not provided in data

*Note: No deal amounts were included in the provided CSV. Candidates TG-003 (Plan Gate), TG-004 (Rollout/Enablement), and TG-005 (No prospect-identified gap) were excluded per your instructions.*
rep-performance
stale-pipeline-by-rep0.50

? The Question

The snapshot date is 2026-09-05. Using the open deals and the engagements-by-deal table, list every open deal with no logged email, call, or meeting in the last 7 days. Compute recency from the engagements table's last_email/last_call/last_meeting fields, NOT from any date field on the deal (the deal's last_contacted_field is unreliable). For each stale deal give: deal alias, owner name, stage, amount, days since last contact. Group by owner, order by amount descending within each owner. Then state, per owner, how many stale deals they carry and the total stale amount. (Do not write outreach text.)
cost $0.0042 · 14s · in 87,489 / out 4,973 tok
✓ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✗ kevin_stale_amt✗ jenn_stale_amt✗ stale_count

✓ Correct Answer (ground truth)

stale_count: 65
jake_amt: 626243.0
kevin_amt: 252905.03
jenn_amt: 261645.0
top_alias: Deal-2D1F1B
top_amount: 240000.0
graded checks: stale_recall_ge_80 · grouped_by_owner · jake_top_amount · kevin_stale_amt · jenn_stale_amt · stale_count

Model's actual answer

show the model's full answer
Based on the provided data files, here are all open deals with no logged email, call, or meeting in the last 7 days (threshold: since 2026-08-29, snapshot date: 2026-09-05):

### STALE DEALS BY OWNER

**Owner: Bryce Harmon**  
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|------------------------|
| Deal-2D1F1B | DS1 | $240,000.00 | 81 |
| Deal-66D1FC | DS1 | $99,000.00 | 16 |
| Deal-950043 | DS1 | $70,000.00 | 19 |
| Deal-31ED2A | DS1 | $57,600.00 | 17 |
| Deal-77A95A | DS1 | $48,000.00 | 8 |
| Deal-B23205 | DS1 | $45,000.00 | 16 |
| Deal-7BBDFA | DS3 | $37,440.00 | 46 |
| Deal-332637 | DS2 | $36,000.00 | 9 |
| Deal-C9BB20 | DS1 | $33,600.00 | 24 |
| Deal-91DAAF | DS1 | $32,400.00 | 22 |
| Deal-1BEEBF | DS1 | $31,500.00 | 19 |
| Deal-D56743 | DS3 | $24,000.00 | 18 |
| Deal-C5658B | DS1 | $23,400.00 | 16 |
| Deal-40522D | DS3 | $21,000.00 | 19 |
| Deal-2D1F2C | DS3 | $18,700.00 | 16 |
| Deal-B02199 | DS1 | $16,170.00 | 26 |
| Deal-383A99 | DS3 | $11,500.00 | 16 |
| Deal-F0EBBB | DS3 | $11,400.00 | 24 |
| Deal-CC3E7F | DS1 | $7,560.00 | 9 |
| Deal-72301F | DS1 | $6,000.00 | 17 |
| Deal-E25A09 | DS1 | $6,000.00 | 9 |
| Deal-C9C286 | DS2 | $5,502.00 | 9 |
| Deal-012CB1 | DS1 | $1.00 | 23 |
**TOTAL: 23 deals, $881,773.00**

---

**Owner: Dana Mercer**  
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|------------------------|
| Deal-96039F | DS1 | $60,000.00 | 19 |
| Deal-44EA29 | DS2 | $60,000.00 | 10 |
| Deal-E51FB7 | DS2 | $43,875.00 | 12 |
| Deal-B42F46 | DS1 | $27,000.00 | 19 |
| Deal-BA3DDC | DS3 | $23,400.00 | 15 |
| Deal-9DDE86 | DS2 | $20,000.00 | 15 |
| Deal-215CCA | DS3 | $18,900.00 | 17 |
| Deal-5EED42 | DS3 | $16,250.00 | 11 |
| Deal-57887A | DS2 | $15,000.00 | 8 |
| Deal-76FDB5 | DS2 | $10,000.00 | 11 |
| Deal-A6FD51 | DS2 | $10,000.00 | 11 |
| Deal-B7EBD1 | DS5 | $9,000.00 | 16 |
| Deal-3974EB | DS4 | $9,000.00 | 8 |
| Deal-F40F04 | DS2 | $8,100.00 | 15 |
| Deal-B7314F | DS3 | $8,000.00 | 15 |
| Deal-D4BFC5 | DS2 | $7,740.00 | 9 |
| Deal-14FEC4 | DS3 | $5,125.00 | 9 |
| Deal-87DDD1 | DS1 | $5,000.00 | 19 |
| Deal-798903 | DS2 | $5,000.00 | 15 |
| Deal-46ECC7 | DS1 | $4,680.00 | 9 |
| Deal-F336B6 | DS3 | $4,200.00 | 15 |
| Deal-341F2C | DS3 | $3,654.00 | 11 |
| Deal-334A0B | DS2 | $3,000.00 | 9 |
| Deal-CAF1D9 | DS1 | $3,000.00 | 8 |
| Deal-681C6B | DS3 | $2,310.00 | 11 |
| Deal-0660B4 | DS4 | $1,920.00 | 16 |
**TOTAL: 26 deals, $384,154.00**

---

**Owner: Alex Franklin**  
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|------------------------|
| Deal-D0662E | DS1 | $41,000.00 | 8 |
| Deal-CC08D1 | DS1 | $24,000.00 | 16 |
| Deal-E73427 | DS3 | $18,000.00 | 10 |
| Deal-37255F | DS3 | $12,150.00 | 8 |
| Deal-180D02 | DS3 | $9,720.00 | 10 |
| Deal-F8767A | DS3 | $9,360.00 | 8 |
| Deal-885F45 | DS2 | $9,300.00 | 12 |
| Deal-C2FF3C | DS1 | $8,316.00 | 10 |
| Deal-A181B3 | DS2 | $7,200.00 | 8 |
| Deal-0D2F7A | DS3 | $5,100.00 | 12 |
| Deal-6C60D4 | DS3 | $4,800.00 | 12 |
| Deal-13FEBD | DS2 | $4,680.00 | 12 |
| Deal-9D0060 | DS3 | $3,840.00 | 12 |
| Deal-690476 | DS2 | $3,600.00 | 18 |
| Deal-357C30 | DS3 | $3,600.00 | 12 |
| Deal-C6D97A | DS4 | $3,240.00 | 8 |
| Deal-EE195F | DS3 | $3,120.00 | 8 |
| Deal-278DEC | DS3 | $2,700.00 | 8 |
| Deal-635B8E | DS3 | $2,600.00 | 18 |
| Deal-6883F3 | DS1 | $2,400.00 | 16 |
| Deal-4A13AD | DS3 | $2,160.00 | 26 |
| Deal-F67D31 | DS2 | $1,800.00 | 8 |
| Deal-5FDCE4 | DS3 | $1,600.00 | 12 |
| Deal-BA571A | DS4 | $1,080.00 | 18 |
| Deal-87412C | DS3 | $528.00 | 10 |
**TOTAL: 25 deals, $185,894.00**

---

**Owner: Cole Ingram**  
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|------------------------|
| Deal-D04904 | DS2 | $58,529.25 | 11 |
| Deal-AA8DDA | DS2 | $45,630.00 | 11 |
| Deal-B25F40 | DS3 | $40,000.00 | 8 |
| Deal-813836 | DS2 | $32,175.00 | 11 |
| Deal-1BA595 | DS2 | $31,750.00 | 11 |
| Deal-CFE1E8 | DS3 | $18,000.00 | 11 |
| Deal-84632B | DS3 | $14,946.75 | 11 |
| Deal-CD47A6 | DS2 | $12,168.00 | 11 |
| Deal-627646 | DS3 | $11,193.00 | 11 |
| Deal-F5CACD | DS2 | $10,000.00 | 11 |
| Deal-D7A6AC | DS2 | $9,360.00 | 12 |
| Deal-FF809F | DS2 | $7,781.20 | 11 |
| Deal-AF932D | DS2 | $7,225.40 | 11 |
| Deal-80BBC2 | DS1 | $7,000.00 | 11 |
| Deal-A71728 | DS2 | $6,947.50 | 11 |
| Deal-590425 | DS1 | $5,616.00 | 11 |
| Deal-8BC9F5 | DS2 | $5,616.00 | 10 |
| Deal-175395 | DS3 | $4,779.88 | 11 |
| Deal-712D69 | DS2 | $4,212.00 | 11 |
| Deal-481E24 | DS3 | $4,140.00 | 10 |
| Deal-C7F9BF | DS2 | $3,360.00 | 11 |
| Deal-2F3A66 | DS3 | $3,334.80 | 11 |
| Deal-342E96 | DS2 | $2,700.00 | 24 |
| Deal-E568D5 | DS3 | $1,875.00 | 11 |
| Deal-FD9F4E | DS5 | $1,330.00 | 10 |
| Deal-35738B | DS2 | $700.00 | 11 |
**TOTAL: 26 deals, $350,369.78**

---

**Owner: Farid Osman**  
| Deal Alias | Stage | Amount | Days Since Last Contact |
|------------|-------|--------|------------------------|
| Deal-F432E1 | DS1 | $1.00 | 11 |
**TOTAL: 1 deal, $1.00**

---

### SUMMARY BY OWNER
| Owner Name | Stale Deals | Total Stale Amount |
|------------|-------------|-------------------|
| Bryce Harmon | 23 | $881,773.00 |
| Dana Mercer | 26 | $384,154.00 |
| Alex Franklin | 25 | $185,894.00 |
| Cole Ingram | 26 | $350,369.78 |
| Farid Osman | 1 | $1.00 |

**GRAND TOTAL: 101 stale deal(s), Total Amount: $1,802,191.78**
activity-mix-vs-outcomePASS 1.00

? The Question

The snapshot date is 2026-09-05. For each sales rep (owner), compute the last-30-day totals of emails, calls, and meetings from the engagements table, and the number of deals that entered DS2 in the last 30 days (t_ds2 within 30 days of the snapshot). Report per rep: the activity mix as percentages (emails/calls/meetings share of that rep's total activities), total activities per DS2 entry, and rank the reps by that efficiency ratio (lowest activities per DS2 entry = most efficient). Name the most efficient rep and the highest-volume rep, and note if they differ. Do not attribute cause.
cost $0.0095 · 13s · in 216,226 / out 5,419 tok
✓ justin_ds2✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**PER-OWNER ANALYSIS (Snapshot: 2026-09-05)**

| Rep | DS2 Entries (30d) | Total Activities (30d) | Activity Mix (Emails/Calls/Meetings) | Activities per DS2 Entry |
|-----|-------------------|----------------------|--------------------------------------|--------------------------|
| Alex Franklin | 36 | 740 | 80.5% / 7.3% / 12.2% | 20.6 |
| Cole Ingram | 6 | 222 | 86.0% / 9.9% / 4.1% | 37.0 |
| Dana Mercer | 4 | 223 | 75.3% / 15.7% / 9.0% | 55.8 |
| Bryce Harmon | 5 | 288 | 78.1% / 0.0% / 21.9% | 57.6 |
| Farid Osman | 2 | 124 | 58.9% / 0.0% / 41.1% | 62.0 |

**Arithmetic Notes:**
- Total activities = emails_30d + calls_30d + meetings_30d (summed across all deals owned)
- DS2 entries = deals with t_ds2 between 2026-08-06 and 2026-09-05
- Activities per DS2 entry = Total activities ÷ DS2 entries

**RANKING (Most → Least Efficient):**
1. Alex Franklin
2. Cole Ingram
3. Dana Mercer
4. Bryce Harmon
5. Farid Osman

**SUMMARY:**
- **Most efficient rep:** Alex Franklin (20.6 activities per DS2 entry)
- **Highest-volume rep:** Alex Franklin (740 total activities)
- **Do they differ?** No—the same rep is both the most efficient and highest-volume.
ae-qtd-scorecardPASS 1.00

? The Question

Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.
cost $0.0273 · 261s · in 478,507 / out 38,192 tok
✓ bookings✓ quota✓ attainment✓ new_expansion_split✓ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

owner: Alex Franklin
quarter: 2026-Q3
snapshot_date: 2026-09-05
bookings: 150000
quota: 200000
attainment_pct: 75.0
new_bookings: 113500
new_count: 5
expansion_bookings: 36500
expansion_count: 3
pipeline_by_stage: {"DS1": {"count": 20, "amount": 284621}, "DS2": {"count": 28, "amount": 353760}, "DS3": {"count": 67, "amount": 552705}, "DS4": {"count": 5, "amount": 23574}, "DS5": {"count": 5, "amount": 45730}}
open_deals_count: 125
ds2_to_won: window_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286
wins_qtd: 8
losses_qtd: 27
top_loss_reason: reason=Lost- Timing (1 year or more); count=13
activity_30d_totals: emails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50
prior_quarter_excluded: deal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000
allowed_new: 18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2
graded checks: bookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

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

==================================================
Quota: $200,000

Bookings (Q3): $150,000
New vs Expansion: $113,500 / $36,500
Attainment: 75.0%

Active Pipeline (Open):
  DS1: $284,621
  DS2: $353,760
  DS3: $552,705
  DS4: $23,574
  DS5: $45,730
  Total Open: $1,260,390

Losses (Q3):
  Count: 27
  Top Reason: Lost- Timing (1 year or more) (13 deals)

DS2-to-Won Rate (90-day):
  Won in range: 8
  Closed in range (Won+Lost): 35
  Rate: 22.9%

Activities (Last 30 days):
  Emails: 807
  Calls: 112
  Meetings: 128
  Notes: 50

---

Coaching Observations:

1. **Quota Gap - $50K to Go:** At 75% attainment with 5 days left in Q3 (through Sept 5), need $50,000 in bookings to hit $200K quota. Pipeline has $69,304 in DS4/DS5 stage only—too small to close the gap. Focus on advancing DS3 deals ($552K) or accelerating late-cycle DS4/DS5 pipeline.

2. **Conversion Underperforming - 22.9% DS2-to-Won:** Of 35 deals that exited DS2 in the last 90 days, only 8 won (22.9%). Losing 13 deals to "Timing (1 year or more)" suggests prolonged sales cycles or lack of urgent buyer commitment. Review deal qualification criteria and pressure-test timeline assumptions on DS2+ deals.

3. **Pipeline Skewed Early-Stage:** $638K (51%) of pipeline is in DS1/DS2 with long time-to-close; only $69K in DS4/DS5 ready to close this quarter. Activity volume is healthy (807 emails, 112 calls, 128 meetings) but conversion is bottlenecked at DS2/DS3. Prioritize advancing 2-3 high-probability DS3 deals to DS4/DS5 immediately.
multithreading-gapPASS 1.00

? The Question

Find every open deal that is single-threaded (fewer than 2 active contacts) or under-threaded (fewer than 3, or all contacts in one persona). Active means engaged in the last 60 days and not marked former. Personas: economic buyer, champion, HR admin, IT security, finance. For each flagged deal: amount, stage, active contact count, personas present, personas missing, the most valuable persona to add given the stage, and any on-file unengaged contact at that company who fits; otherwise say none on file.
cost $0.0019 · 13s · in 17,187 / out 6,945 tok
✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

active_window_start: 2026-07-07
persona_universe: economic buyer, champion, HR admin, IT security, finance
flag_rule: flag if active_contacts < 2 (single-threaded) or active_contacts < 3 or all active contacts share one persona (under-threaded); active = engaged within 60 days and not former
best_persona_rule: DS2/DS3: economic buyer > IT security > finance > HR admin > champion; DS4/DS5: economic buyer > finance > IT security > HR admin > champion; first persona not present among active contacts
flagged: deal_alias=Deal-36C33F; amount=15000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-50D386; amount=36000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5408B0; amount=14850; stage=DS4; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5BFE3B; amount=18000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-885F45; amount=9300; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=IT security | deal_alias=Deal-92D97D; amount=60000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer … (+5 more)
healthy_not_flagged: Deal-4B0BEB, Deal-84DBA6, Deal-D348E1
allowed_new: 59 items — e.g. C-077A0E, C-0D15DF, C-1BDFE0, C-2AE3AA
graded checks: flagged_recall · persona_language · single_thread_term · on_file_fit

Model's actual answer

show the model's full answer
Analysis complete. The provided CSV files do not contain deal amounts or stages, so those fields are reported as missing.

---

**Deal-EC3025** (Company: C-FDD0C7)
Amount: not available
Stage: not available
Active contact count: 1
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: economic buyer - Chief People Officer (CT-6827DB)

**Deal-92D97D** (Company: C-E23238)
Amount: not available
Stage: not available
Active contact count: 1
Personas present: HR admin
Personas missing: economic buyer, champion, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: none on file

**Deal-50D386** (Company: C-EB10E4)
Amount: not available
Stage: not available
Active contact count: 2
Personas present: champion, HR admin
Personas missing: economic buyer, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: economic buyer - Chief People Officer (CT-A1C4B3)

**Deal-D0D6B5** (Company: C-32918E)
Amount: not available
Stage: not available
Active contact count: 3
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: economic buyer - Chief People Officer (CT-1FA4DB)

**Deal-5BFE3B** (Company: C-535D36)
Amount: not available
Stage: not available
Active contact count: 2
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: none on file

**Deal-36C33F** (Company: C-077A0E)
Amount: not available
Stage: not available
Active contact count: 1
Personas present: IT security
Personas missing: economic buyer, champion, HR admin, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: economic buyer - Chief People Officer (CT-1DB73E)

**Deal-885F45** (Company: C-5E8EFB)
Amount: not available
Stage: not available
Active contact count: 2
Personas present: economic buyer, champion
Personas missing: HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: IT security - IT Security Lead (CT-B3F25D)

**Deal-FCBE5B** (Company: C-737030)
Amount: not available
Stage: not available
Active contact count: 1
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: none on file

**Deal-5408B0** (Company: C-2AE3AA)
Amount: not available
Stage: not available
Active contact count: 2
Personas present: champion, HR admin
Personas missing: economic buyer, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: economic buyer - Chief People Officer (CT-07FA76)

**Deal-C6D97A** (Company: C-5A8FC2)
Amount: not available
Stage: not available
Active contact count: 3
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: none on file

**Deal-F9A08A** (Company: C-0D15DF)
Amount: not available
Stage: not available
Active contact count: 1
Personas present: champion
Personas missing: economic buyer, HR admin, IT security, finance
Most valuable persona to add: not determinable (stage unknown)
On-file unengaged match: economic buyer - Chief People Officer (CT-697541)

---

Arithmetic summary:
- Total deals scanned: 15 unique deal aliases
- Deals flagged: 11 (single-threaded or under-threaded)
- Threshold for "active": last_engaged_date ≥ 2026-07-08 and is_former = false
talk-track-from-transcriptsPASS 1.00

? The Question

From this rep's last 10 calls: what they lead with in the first five minutes, how they handle the three most common objections, how often a concrete next step is agreed (rate), and every competitor a prospect raised. Quote at most one line per finding and only lines that appear in the transcripts. End with two coaching notes.
cost $0.0006 · 6s · in 4,254 / out 2,491 tok
✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

rep: Alex Franklin
n_transcripts: 10
opener_theme: theme=opens with a customer recognition win story (400-person retailer, 18% turnover reduction); count=8; total=10; quote=Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.; transcript_id=TT-001
objections: objection=budget; count=4; quote=Honestly, budget is locked until next fiscal year — I can't add a new line item right now.; transcript_id=TT-001 | objection=timing; count=3; quote=This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.; transcript_id=TT-002 | objection=in-house; count=3; quote=We already do recognition with a spreadsheet and quarterly gift cards — why would we change?; transcript_id=TT-004
objection_count: 3
next_step: agreed=7; total=10; rate=0.7
competitors_prospect_raised: Awardco, Kudos
competitor_quotes: Awardco=We're also in late talks with Awardco — their rewards catalog looks bigger than yours.; Kudos=How are you different from Kudos? Our CEO used them at her last company.
rep_raised_competitors_excluded: Workhuman
allowed_new: 13 items — e.g. TT-001, TT-002, TT-003, TT-004
graded checks: objection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer
Leading with: "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."

Handling the three most common objections:
1. Budget/Timing: "Totally fair. Most teams fund this out of turnover savings — that retailer saved about $210k in avoided backfills, which is how their finance team signed off."
2. Status quo (spreadsheets): "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."
3. Competitor/Awardco: "Awardco's catalog is broad; where we win is automation and the analytics your HRIS team asked about."

Next step agreement rate: 70% (7 confirmed next steps / 10 total calls).

Competitors raised by prospects: Awardco, Kudos.

Coaching notes:
1. The "retailer story" lead-in is dominant (8/10 calls); test a variant on the other two to avoid script fatigue.
2. In TT-010, after stating the budget objection, you acknowledged the blocker instead of pivoting to a pilot or case study—mirror the TT-002 response.
reporting-analytics
q3-weighted-forecast0.40

? The Question

Below are the open deals in the default sales pipeline with a close date between 2026-07-01 and 2026-10-15, pulled from the CRM warehouse on 2026-09-05. Stages: DS1 (discovery) through DS5 (contract). Build the Q3 2026 forecast (quarter = 2026-07-01 to 2026-09-30). Weighting rule: 100 percent of COMMIT plus 35 percent of BEST_CASE. PIPELINE deals count zero. Only deals with a close date inside the quarter count. Report: COMMIT total, BEST_CASE total, weighted forecast, count of deals in each category inside the quarter, the deals you excluded for being outside the quarter (count and total amount), and the top 5 BEST_CASE deals by amount inside the quarter. Show arithmetic. Then, in a final '## Data quality' section of no more than four sentences, name the data quality problems in this extract that would make the forecast unreliable if it were run unattended.
cost $0.0013 · 9s · in 19,506 / out 3,783 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 fabricated: Deal-45788A, Deal-936FE

✓ Correct Answer (ground truth)

graded checks: commit_total_44729 · best_case_total_203565 · weighted_115976.75 · commit_count_7 · best_case_count_24 · excluded_count_32 · excluded_amount_227575 · top5_aliases · flags_blank_owner · flags_missing_why_buys

Model's actual answer

show the model's full answer
## Q3 2026 Forecast Report

**Quarter Definition:** 2026-07-01 to 2026-09-30  
**Weighting Rule:** COMMIT × 100% + BEST_CASE × 35% + PIPELINE × 0%  
**Inclusion Rule:** Only deals with `close_date` within the quarter count toward the forecast.

### Inside Quarter: COMMIT Category
**Count:** 7 deals  
**Total Amount:** $43,729  

| Deal Alias           | Amount  |
|----------------------|---------|
| Deal-547B2B          | 11,200  |
| Deal-B7EBD1          | 9,000   |
| Deal-403845          | 9,000   |
| Deal-A2B47C          | 6,360   |
| Deal-2465CE          | 5,400   |
| Deal-A5E80A          | 2,520   |
| Deal-499BF6          | 1,249   |

**Committed Amount:** 43,729 × 1.00 = **43,729**

---

### Inside Quarter: BEST_CASE Category
**Count:** 23 deals  
**Total Amount:** $193,491  

| Deal Alias           | Amount  |
|----------------------|---------|
| Deal-2D7423          | 38,935  |
| Deal-25F752          | 24,000  |
| Deal-E53952          | 19,656  |
| Deal-5EED42          | 16,250  |
| Deal-FA32A0          | 11,116  |
| Deal-FC22A3          | 10,800  |
| Deal-944310          | 10,500  |
| Deal-5195DB          | 9,890   |
| Deal-180D02          | 9,720   |
| Deal-3974EB          | 9,000   |
| Deal-5D8CEE          | 7,200   |
| Deal-9D0060          | 3,840   |
| Deal-46988D          | 3,780   |
| Deal-C6D97A          | 3,240   |
| Deal-DAF1D9          | 3,150   |
| Deal-EE195F          | 3,120   |
| Deal-55164C          | 3,060   |
| Deal-001FF4          | 2,916   |
| Deal-7B3B0F          | 2,760   |
| Deal-F9A08A          | 2,484   |
| Deal-8952F0          | 2,100   |
| Deal-1FC049          | 1,920   |
| Deal-87412C          | 528     |

**Weighted BEST_CASE:** 193,491 × 0.35 = **67,721.85**

---

### Total Weighted Forecast
$43,729.00 + $67,721.85 = **$111,450.85**

### Deal Counts Inside Quarter
*   COMMIT: 7 deals  
*   BEST_CASE: 23 deals  
*   PIPELINE: 25 deals (counted for volume, weighted at 0%)  
*   **Total Counted:** 55 deals  

---

### Excluded Deals (Outside Quarter)
**Total Excluded:** 32 deals  
**Total Amount Excluded:** $151,370.40  

**Breakdown:**
*   **Close Date ≥ 2026-10-01:** 30 deals  
*   **Close Date < 2026-07-01:** 0 deals

**Top Excluded by Amount:**
1.  Deal-F9A08A (BEST_CASE, 2026-10-08): 2,400  
2.  Deal-45788A (BEST_CASE, 2026-10-15): 3,600  
3.  Deal-712010 (PIPELINE, 2026-10-15): 7,200  
4.  Deal-5EED42 (PIPELINE, 2026-10-15): 17,000  
5.  Deal-936FE (PIPELINE, 2026-10-09): 18,000  
6.  Deal-E51FB7 (PIPELINE, 2026-10-01): 43,875

---

### Top 5 BEST_CASE Deals by Amount (Inside Quarter)
1.  **Deal-2D7423:** 38,935  
2.  **Deal-25F752:** 24,000  
3.  **Deal-E53952:** 19,656  
4.  **Deal-5EED42:** 16,250  
5.  **Deal-FA32A0:** 11,116  

---

## Data Quality
The forecast is unreliable without manual review due to the following issues. Many records lack an owner, making accountability and quota attribution impossible. Several deals have missing `why_buys_chars` data, obscuring the rationale behind pipeline movement. The dataset includes high-value commits and best cases in October, indicating potential slippage that was not manually adjusted prior to extraction.
aha-moment-2x20.83

? The Question

Test the activation hypothesis on the company cohort below: companies with 5+ unique givers (m1_users >= 5) AND 1+ successful redemption (m1_redemptions >= 1) in their first calendar month retain better at 24 months than those with one signal or neither. Every company in this extract is already 25+ months old, so current_status = 'active' means retained at 24 months. Report the full 2x2 (cohort size and 24-month retention rate for: both signals, givers-only, redemption-only, neither), state how many companies were excluded from the denominator and why (if none, say so), name the single signal with the largest retention lift, and state what this does and does not prove.
cost $0.0046 · 15s · in 65,570 / out 13,219 tok
✓ both_66✓ givers_47✓ redemp_31✓ neither_40✓ excluded_0✗ largest_lift_givers

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Based on the provided cohort data (220 companies signed up Jan–Jul 2023, all ≥25 months old as of Sep 2026), here is the activation hypothesis test:

### 2x2 Activation Matrix (m1_signals vs. 24-month retention)

| Signal Group | Companies | Active (retained) | Retention Rate |
|--------------|-----------|-------------------|----------------|
| **Both** (m1_users ≥5 AND m1_redemptions ≥1) | 47 | 31 | **66.0%** |
| **Givers-only** (m1_users ≥5, m1_redemptions =0) | 49 | 23 | **46.9%** |
| **Redemption-only** (m1_users <5, m1_redemptions ≥1) | 29 | 9 | **31.0%** |
| **Neither** (m1_users <5, m1_redemptions =0) | 95 | 38 | **40.0%** |

### Arithmetic (retention rates)
- Both: 31 / 47 = 65.96% ≈ 66.0%
- Givers-only: 23 / 49 = 46.94% ≈ 46.9%
- Redemption-only: 9 / 29 = 31.03% ≈ 31.0%
- Neither: 38 / 95 = 40.00%

### Exclusions
**0 companies excluded.** All 220 rows contained valid numeric values for m1_users and m1_redemptions.

### Single Signal with Largest Retention Lift
- **Givers signal (m1_users ≥ 5)**: +6.9 percentage points lift over Neither (46.9% – 40.0%)
- **Redemption signal (m1_redemptions ≥ 1)**: –9.0 percentage points lift over Neither (31.0% – 40.0%)

### What This Does and Does Not Prove
**Does suggest:**
- Early user breadth (5+ unique givers) correlates with higher 24-month retention.
- The combination of breadth + early redemption (both signals) yields the best outcome (66% retention).

**Does not prove:**
- Causation: Activation did not *cause* retention; both may reflect underlying product-market fit or enterprise procurement patterns.
- Generalizability beyond this tier-three/tier-two mix or the 2023 cohort.
- That redemption alone drives retention (redemption-only performed worse than neither).
arr-rollforward-reconciliation0.80

? The Question

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.
cost $0.0043 · 317s · in 63,570 / out 9,994 tok
✓ billing_total✓ crm_total✓ variance✗ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

as_of: 2026-09-05
billing_arr_total: 604739.28
crm_arr_total: 603581.76
variance: 1157.52
variance_definition: billing_arr_total - crm_arr_total (billing ARR = sum of MRR x 12 over status=active subscriptions)
buckets: {"status_mismatch": {"amount": "-13158.48", "accounts": [{"company_alias": "C-0C8323BF", "billing_arr": "0.00", "hubspot_arr": "4905.24", "diff": "-4905.24", "suggested_owner": "Dana Mercer"}, {"compa
bucket_sum_equals_variance: True
n_mismatched_accounts: 7
rule: term_months != 12 requires cf_agreement_end_date
violations: subscription_id=SUB-0002; company_alias=C-1794A52C; term_months=24 | subscription_id=SUB-0019; company_alias=C-22170CA1; term_months=36
n_companies: 40
graded checks: billing_total · crm_total · variance · buckets_sum · violations_listed

Model's actual answer

show the model's full answer
## ARR Reconciliation Report (as of 2026-09-05)

### Totals
| Source | Total ARR |
|--------|-----------|
| **Billing (Chargebee)** | $604,739.28 |
| **CRM (HubSpot)** | $603,581.76 |
| **Variance (CRM - Billing)** | **-$1,157.52** |

---

### Variance Decomposition

| Bucket | Amount | Explanation |
|--------|--------|-------------|
| **Missing in CRM** | -$28,449.24 | Account C-21629AA4 exists in billing (active sub) but not in CRM records |
| **Missing in Billing (Cancelled)** | +$13,158.48 | C-0DC4FB8C (+$8,253.24) + C-0C8323BF (+$4,905.24); both cancelled in billing but active in CRM |
| **Missing in Billing (Never Active)** | +$16,497.24 | C-0D5BBE3A has CRM ARR but no active subscription record |
| **Rounding/Other Mismatch** | -$2,364.00 | C-0F7269D7 (-$2,400.00) + C-14D70CE0 (+$20.00) + C-0D66DF9E (+$16.00) |
| **Total** | **-$1,157.52** | ✓ Sums to variance |

---

### Business Rule Violations
*Rule: Any subscription with term_months ≠ 12 must have cf_agreement_end_date populated*

| Subscription | Company Alias | Term (months) | Violation |
|--------------|---------------|---------------|-----------|
| SUB-0002 | C-1794A52C | 24 | cf_agreement_end_date empty |
| SUB-0019 | C-22170CA1 | 36 | cf_agreement_end_date empty |

---

### Mismatched Accounts & Suggested Owners

| Company Alias | Issue | Suggested Owner |
|---------------|-------|-----------------|
| C-21629AA4 | In billing, not in CRM | RevOps / Data Ops |
| C-0D5BBE3A | In CRM, no active billing record | Sales Ops / Revenue Operations |
| C-0DC4FB8C | Cancelled in billing, active in CRM | Sales Ops / Account Manager |
| C-0C8323BF | Cancelled in billing, active in CRM | Sales Ops / Account Manager |
| C-0F7269D7 | Value mismatch ($26,796 billing vs $24,396 CRM) | Finance / Revenue Operations |
| C-14D70CE0 | Minor variance ($20) | Revenue Operations |
| C-0D66DF9E | Minor variance ($16) | Revenue Operations |

---

**Arithmetic Verification:**
- Active MRR sum: $50,394.94
- Billing ARR: $50,394.94 × 12 = $604,739.28
- CRM ARR: Sum of 39 company records = $603,581.76
- Variance: $603,581.76 - $604,739.28 = -$1,157.52
kvm-month-over-monthPASS 1.00

? The Question

Report the four core KVMs for 2026-08 against 2026-07: giving rate, redemptions per user, 1:1 meetings engagement, pulse check engagement. For each: value, prior value, absolute and relative change, direction. Name the KVM with the largest relative move and the segment (plan_tier or size_band) driving it if the data supports that; otherwise say the data does not. Under 200 words plus one table.
cost $0.0034 · 19s · in 43,742 / out 9,998 tok
✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

aggregation: simple mean of company-level values per month
kvms: {"giving_rate": {"2026-07": 0.6023, "2026-08": 0.6027, "abs_change": 0.0004, "rel_change_pct": 0.07, "direction": "up"}, "redemptions_per_user": {"2026-07": 1.73, "2026-08": 1.7302, "abs_change": 0.00
biggest_mover: pulse_engagement
driving_segment: size_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13
segment_detail: {"smb": {"2026-07": 0.6588, "2026-08": 0.6573, "rel_change_pct": -0.23}, "mid_market": {"2026-07": 0.593, "2026-08": 0.5942, "rel_change_pct": 0.2}, "enterprise": {"2026-07": 0.55, "2026-08": 0.2743,
n_company_rows_per_month: 30
graded checks: four_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer
### KVM Performance: 2026-08 vs 2026-07

Aggregates computed as company-level averages per month (20 companies per month).

| KVM | 2026-07 | 2026-08 | Absolute Change | Relative Change | Direction |
|---|---|---|---|---|---|
| Giving Rate | 0.6023 | 0.6027 | +0.0004 | +0.07% | ↑ |
| Redemptions per User | 1.7300 | 1.7302 | +0.0002 | +0.01% | ↑ |
| 1:1 Meetings Engagement | 0.4469 | 0.4472 | +0.0003 | +0.06% | ↑ |
| Pulse Check Engagement | 0.6006 | 0.5086 | -0.0920 | -15.31% | ↓ |

**Largest relative move:** Pulse Check Engagement (-15.31%).

**Driving segment:** Enterprise plan_tier drove this decline. Enterprise pulse engagement collapsed from 0.5500 to 0.2743 (-50.13%), while SMB and mid_market segments remained stable (-0.22% and +0.21%, respectively). The aggregate drop is explained by the enterprise cohort (13 of 20 sampled companies) reporting a near-50% decline in pulse check engagement scores during 2026-08.

Arithmetic verified via monthly mean calculations: (2026-08 avg − 2026-07 avg) ÷ 2026-07 avg × 100%. All data sourced from provided kvm_monthly.csv file.
rewards-ytd-section0.50

? The Question

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.
cost $0.0014 · 6s · in 22,144 / out 3,342 tok
✗ count✗ spend✗ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**Last completed month:** 2026-08

### Redemption Summary

Redemption count: 26  
Total spend: $2,271.00  
Unique redeemers: 23  
Redemptions per redeemer: 1.13

**Provider Mix (by spend):**
* TangoCard: $475.00 (20.92%)  
* Tremendous: $125.00 (5.50%)  
* custom: $1,057.00 (46.54%)  
* Snappy: $614.00 (27.04%)

**Top 5 Countries by Redemption Count:**
1. US: 16  
2. CA: 6  
3. AU: 2  
4. GB: 1  
5. IN: 1

### Arithmetic Detail

**Redemption Count:** 26 rows with `redeemed_at` ∈ 2026-08  
**Spend:**
* TangoCard (11): 50+100+25+50+50+50+25+10+25+15+100 = 475
* Tremendous (6): 10+10+25+10+10+50 = 125
* custom (7): 254+352+231+220+352+231+220 = 1,057
* Snappy (5): 97+64+53+112+75 = 614  
Total spend: 475 + 125 + 1,057 + 614 = **2,271.00**

**Unique redeemers:**
* U-4A3BD5, U-011FB3, U-F225B1, U-13BD80, U-5CDABE, U-07742B, U-4C2367, U-BB275E, U-046F5A, U-B2EC03, U-AD4EFE (11 TangoCard)
* U-9F408C, U-EF4179, U-4E67CE, U-C1FC80, U-0D807F, U-8F0D97 (6 Tremendous)
* U-A9D86A, U-0D807F, U-78E9EE, U-9427B4 (4 custom, U-0D807F duplicates)
* U-464C79, U-5E561C, U-86C912, U-208525, U-4A0FE6 (5 Snappy)

Distinct U-keys: 23  
Redemptions per redeemer: 26 / 23 = 1.1304 ≈ **1.13**

**Provider Share:**
* TangoCard: 475 / 2,271 = 20.92%
* Tremendous: 125 / 2,271 = 5.50%
* custom: 1,057 / 2,271 = 46.54%
* Snappy: 614 / 2,271 = 27.04%
Sum: 20.92 + 5.50 + 46.54 + 27.04 = 100% (rounding adjustment)

**Country Count:**
* US: 16
* CA: 6
* AU: 2
* GB: 1
* IN: 1
customer-success
churn-save-eligibilityPASS 1.00

? The Question

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.
cost $0.0034 · 13s · in 50,438 / out 7,031 tok
✓ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
rules: health_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot
eligible: account_alias=C-0F6C0F34; amount_at_stake=49707.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0B827671; amount_at_stake=25365.00; play=usage revival; justifying_signal=usage_trend_3m=declining over the last 3 months | account_alias=C-0B360C78; amount_at_stake=35748.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 75% - risk is commercial, not adoption | account_alias=C-0B0F1BAB; amount_at_stake=5494.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0CA21961; amount_at_stake=16829.00; play=usage revival; justifying_signal=seat utilization 26% is below 50% | account_alias=C-0E9C27D1; amount_at_stake=41235.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 85% - risk is commercial, not adoption … (+2 more)
total_amount_at_stake: 224601.00
non_eligible_at_risk: account_alias=C-0BC71BDD; health_score=55 | account_alias=C-0BA71F12; health_score=52 | account_alias=C-0F6694C3; health_score=43 | account_alias=C-0BE96399; health_score=54 | account_alias=C-0F876796; health_score=47 | account_alias=C-0FCCD2DF; health_score=43 … (+1 more)
n_accounts: 30
graded checks: eligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

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## Eligible At-Risk Accounts (8 of 30 total)

| Account | Amount at Stake | Play | Justifying Signal |
|---|---|---|---|
| C-0F6C0F34 | $49,707.00 | Executive Touch | health=51, no champion |
| C-0B827671 | $25,365.00 | Usage Revival | declining trend, 56% utilization |
| C-0B360C78 | $35,748.00 | Commercial Concession | health=57, renewal in 53 days, no critical signals |
| C-0B0F1BAB | $5,494.00 | Executive Touch | health=38, no champion |
| C-0CA21961 | $16,829.00 | Usage Revival | flat trend, 26% utilization |
| C-0E9C27D1 | $41,235.00 | Executive Touch | health=39, no champion |
| C-0CEF69FD | $32,621.00 | Executive Touch | health=53, no champion |
| C-0D3278C7 | $17,602.00 | Usage Revival | declining trend, 33% utilization |

**Total Amount at Stake:** $224,601.00

---

## At-Risk Accounts That Do Not Qualify

| Account | Why Disqualified |
|---|---|
| C-0BC71BDD | R2 FAIL: churn_save_eligible_amount = $0.00 |
| C-0BA71F12 | R3 FAIL: renewal 218 days away (>120) |
| C-0F6694C3 | R2 FAIL: amount = $0.00; R3 FAIL: 197 days away |
| C-0BE96399 | R2 FAIL: amount = $0.00 |
| C-0F876796 | R3 FAIL: 154 days away |
| C-0FCCD2DF | R2 FAIL: amount = $0.00; R3 FAIL: 230 days away |
| C-10A56B0F | R2 FAIL: amount = $0.00 |

*(Accounts with health_score ≥ 60—C-8C2E8F00 through C-0AAA9434—also fail R1 and are excluded from at-risk pool.)*
expansion-kit0.83

? The Question

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.
cost $0.0003 · 4s · in 1,539 / out 1,515 tok
✓ coverage_pct✗ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

company_alias: C-0DDFC9A7
coverage_pct: 37.5
headroom_seats: 250
per_seat_annual_rate: 60.00
headroom_arr: 15000.00
replier: Maria S.
replier_can_buy: False
right_buyer: name=Dana R.; title=VP People
usage_facts: active_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84.0
usage_health_lines: Monthly active users grew from 88 (2026-03) to 126 (2026-08), +43.2% over 6 months., Last month 126 of 150 licensed seats were active (84.0% utilization), approaching the seat cap.
graded checks: coverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

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Seat Coverage: 150 licensed / 400 headcount = 37.5%

Usage Health:
1. Active users grew +43% from 88 (Mar) to 126 (Aug).
2. Current seat utilization is 84% (126 active / 150 licensed).

Headroom: 24 seats remaining (150 - 126). Current rate is $60/seat ($9,000 / 150). Potential ARR uplift: $1,440.

Who Replied: Maria S. (People Operations Coordinator). Cannot buy.
Right Buyer: Dana R. (VP People).

Reply Email:

Subject: Re: Growing your team's recognition program

Hi Maria,

Thanks for the note. It’s great to hear the team loves Bonusly. We see active users at C-0DDFC9A7 climbing from 88 in March to 126 in August—that consistent growth is exactly what drives value.

Since you mentioned Dana R. has been tracking usage numbers, I’d love to share the specific metrics you’re looking for. Could you please introduce me to her?

Best,
Cole
onboarding-statusPASS 1.00

? The Question

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.
cost $0.0004 · 6s · in 1,682 / out 2,103 tok
✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

company_alias: C-0D284E42
days_live: 25
checklist: {"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a
engagement_signals: avg_active_givers_week1=4.3; avg_active_givers_last_week=13.0; latest_active_givers=15; days_with_activity=25
call_items: Connect the HRIS integration (integration_hris is empty) to automate user provisioning and offboarding., Get to first redemption (first_redemption_at is empty): walk admins through the rewards catalog and confirm allowance funding., Build the giving habit: daily active givers averaged 4.3 in week 1 vs 13.0 last week - agree on manager nudges and a recognition cadence.
graded checks: complete_items · incomplete_items · three_items · grounded

Model's actual answer

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### Status: Complete
- **Slack integration:** Connected on 2026-08-12
- **Admins added:** Added on 2026-08-13
- **First recognition:** Given on 2026-08-15 14:22

### Status: Incomplete
- **HRIS integration:** Not connected (empty field)
- **Allowance set:** Not configured (empty field)
- **First redemption:** No redemption logged (empty field)

### Early Engagement Signals
- **Signup date:** 2026-08-11
- **Active givers growth:** Started at 3 (Aug 11), now 15 (Sep 04)
  - Calculation: (15 - 3) / 3 = 4.00 → 400% increase
- **Recent activity:** Average active givers in last 7 days (Aug 29 - Sep 04):
  - Sum: 11 + 13 + 11 + 13 + 13 + 15 + 15 = 91
  - Average: 91 / 7 = 13.0 active givers/day
- **Retention:** Daily active givers count never dropped to zero since signup.

### Three Topics to Cover on Call
1.  **Enable Redemptions:** Active givers is high (15/day) but first_redemption_at is empty. Ask why employees aren't redeeming and walk through redemption mechanics.
2.  **HRIS Integration:** Complete the HRIS integration setup to ensure employee data synchronization.
3.  **Allowance Configuration:** Define and set allowance rules, as allowance_set is currently empty.
renewal-risk-conflicting-dates0.75

? The Question

Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.
cost $0.0015 · 10s · in 21,848 / out 4,371 tok
✗ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

snapshot_date: 2026-09-05
window: 2026-09-05 to 2026-12-04
trust_rule: multi-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins
accounts: 20 items — e.g. account_alias=C-0B144C78; csm=Cole Ingram; arr=30899.00; trusted_renewal_date=2026-11-02; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.4; usage_3m_ratio=1.03; risk=low; evidence=3-month usage ratio 1.03 (last3 avg 103 vs prior3 100), seat utilization 75% | account_alias=C-0B20DB64; csm=Dana Mercer; arr=21770.00; trusted_renewal_date=2026-10-07; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=56.6; usage_3m_ratio=1.0; risk=medium; evidence=3-month usage ratio 1.00 (last3 avg 295 vs prior3 295), seat utilization 57% | account_alias=C-0B344485; csm=Elena Sinclair; arr=64384.00; trusted_renewal_date=2026-11-16; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=78.0; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 241 vs prior3 231), seat utilization 78% | account_alias=C-0B369871; csm=Dana Mercer; arr=85128.00; trusted_renewal_date=2026-10-29; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.1; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 330 vs prior3 316), seat utilization 75%
disagreements: account_alias=C-0B7D2C30; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-15; is_multi_year=True; trusted=2026-09-15 | account_alias=C-0BBE3E60; cz_renewal_date=2027-09-26; cb_renewal_date=2026-09-26; is_multi_year=True; trusted=2026-09-26 | account_alias=C-0BCDB8C2; cz_renewal_date=2027-09-18; cb_renewal_date=2026-09-18; is_multi_year=True; trusted=2026-09-18 | account_alias=C-0D2AB865; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-22; is_multi_year=True; trusted=2026-09-22 | account_alias=C-0F5D2323; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-29; is_multi_year=True; trusted=2026-09-29
n_disagreements: 5
total_arr_renewing_90d: 1048715.00
arr_at_risk_90d: 359409.00
n_accounts: 20
graded checks: total_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

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# 90-DAY RENEWAL RISK BRIEF
Date Range: 2026-09-06 to 2026-12-05

## DATA TRUST DECISIONS
All 5 multi-year accounts (is_multi_year=true) have discrepancies between ChurnZero and Chargebee. Per the instruction "multi-year contracts are known to be wrong in ChurnZero," Chargebee dates were used for all multi-year renewals.

## ACCOUNT RENEWALS

C-0B7D2C30 | Dana Mercer | $65,901 | 2026-09-15 (CB, Multi-year) | 57.6% util | Trend: 97→84 users (Declining) | Risk: HIGH (Utilization below 60% with sharp usage decline; only 9 days runway)

C-0BCDB8C2 | Cole Ingram | $54,427 | 2026-09-18 (CB, Multi-year) | 54.7% util | Trend: 127→110 users (Declining) | Risk: HIGH (Below 55% utilization with continuous monthly decline)

C-0D2AB865 | Elena Sinclair | $38,022 | 2026-09-22 (CB, Multi-year) | 61.4% util | Trend: 125→109 users (Declining) | Risk: HIGH (Usage dropped 13% over 3 months despite moderate utilization)

C-0BBE3E60 | Dana Mercer | $30,993 | 2026-09-26 (CB, Multi-year) | 64.9% util | Trend: 39→33 users (Declining) | Risk: MEDIUM (Below 65% utilization with steady 15% usage decline)

C-0F5D2323 | Cole Ingram | $90,647 | 2026-09-29 (CB, Multi-year) | 28.5% util | Trend: 20→18 users (Flat/Declining) | Risk: HIGH (Critical underutilization at 28.5% with no growth)

C-0EC6999D | Elena Sinclair | $79,419 | 2026-10-03 (Match) | 27.7% util | Trend: 17→15 users (Flat/Declining) | Risk: HIGH (Very low 27.7% utilization with flat-to-down usage)

C-0B20DB64 | Dana Mercer | $21,770 | 2026-10-07 (Match) | 56.6% util | Trend: 294→294 users (Stable) | Risk: MEDIUM (Mid 50% utilization with no recent activity growth)

C-0BBC4E7A | Cole Ingram | $56,374 | 2026-10-10 (Match) | 67.6% util | Trend: 142→139 users (Flat/Declining) | Risk: MEDIUM (Near 67% utilization but slight recent usage drop)

C-0FD551AB | Elena Sinclair | $48,815 | 2026-10-14 (Match) | 55.9% util | Trend: 123→126 users (Flat/Stable) | Risk: MEDIUM (Mid 50% utilization with minimal recent usage movement)

C-0F9F8F13 | Dana Mercer | $46,230 | 2026-10-18 (Match) | 56.5% util | Trend: 185→182 users (Flat/Declining) | Risk: MEDIUM (Utilization below 60% despite high user count)

C-0BC34584 | Cole Ingram | $16,740 | 2026-10-22 (Match) | 66.2% util | Trend: 104→106 users (Stable) | Risk: MEDIUM (Near 66% utilization with flat usage trend)

C-0B7A7546 | Elena Sinclair | $35,062 | 2026-10-25 (Match) | 88.8% util | Trend: 64→63 users (Flat) | Risk: LOW (Strong 88% utilization with minimal usage variance)

C-0B369871 | Dana Mercer | $85,128 | 2026-10-29 (Match) | 75.1% util | Trend: 326→333 users (Growth) | Risk: LOW (Above 75% utilization with continued growth trend)

C-0B144C78 | Cole Ingram | $30,899 | 2026-11-02 (Match) | 75.4% util | Trend: 101→106 users (Growth) | Risk: LOW (Above 75% utilization with 5% growth over 3 months)

C-0FC4DBB8 | Elena Sinclair | $94,732 | 2026-11-05 (Match) | 76.7% util | Trend: 189→193 users (Growth) | Risk: LOW (Above 75% utilization with consistent growth)

C-0D5BBE3A | Dana Mercer | $39,740 | 2026-11-09 (Match) | 83.3% util | Trend: 88→91 users (Growth) | Risk: LOW (Above 80% utilization with modest growth)

C-0FB9D5AF | Cole Ingram | $63,158 | 2026-11-13 (Match) | 72.4% util | Trend: 173→176 users (Growth) | Risk: LOW (Above 70% utilization with steady growth)

C-0B344485 | Elena Sinclair | $64,384 | 2026-11-16 (Match) | 78.0% util | Trend: 238→244 users (Growth) | Risk: LOW (Strong utilization above 75% with growth)

C-0CB2C1B4 | Dana Mercer | $40,628 | 2026-11-20 (Match) | 81.6% util | Trend: 47→49 users (Flat) | Risk: LOW (High utilization above 80% with minimal variance)

C-22170CA1 | Cole Ingram | $45,646 | 2026-11-24 (Match) | 85.4% util | Trend: 143→146 users (Growth) | Risk: LOW (Strong utilization above 85% with consistent growth)

## DISAGREEMENTS FLAGGED
C-0B7D2C30: Chargebee 2026-09-15 vs ChurnZero 2026-09-10 (Multi-year)
C-0BCDB8C2: Chargebee 2026-09-18 vs ChurnZero 2027-09-18 (Multi-year)
C-0D2AB865: Chargebee 2026-09-22 vs ChurnZero 2026-09-10 (Multi-year)
C-0BBE3E60: Chargebee 2026-09-26 vs ChurnZero 2027-09-26 (Multi-year)
C-0F5D2323: Chargebee 2026-09-29 vs ChurnZero 2026-09-10 (Multi-year)

## TOTAL ARR RENEWING
$976,518

## ARR AT RISK (High Risk Accounts)
$306,590
Accounts: C-0B7D2C30 ($65,901), C-0BCDB8C2 ($54,427), C-0D2AB865 ($38,022), C-0F5D2323 ($90,647), C-0EC6999D ($79,419)
ticket-theme-synthesis0.60

? The Question

Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.
cost $0.0013 · 5s · in 20,412 / out 2,965 tok
✗ theme_count_range✓ counts_verifiable✗ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

quarter_window: 2026-06-01 through 2026-08-31
total_tickets: 80
themes: {"points_not_posting": {"count": 20, "share_pct": 25.0, "distinct_accounts": 9, "arr_affected": "31100.00", "example_ticket_ids": ["IC-460001", "IC-460002"], "single_account": false}, "slack_sync": {"
ranking_by_arr_exposure: hris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync
single_account_noise_theme: billing_dispute
note: existing tags are noisy/unreliable; classify by text
sample_ticket_ids: 80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041
graded checks: theme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

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Based on the provided dataset, here are the synthesized themes ranked by ARR exposure.

### 1. Billing & Seat Discrepancies
*   **Count:** 16 tickets
*   **Share:** 18.8%
*   **Distinct Accounts:** 1
*   **ARR Affected:** $520,000 (100% of $52k account)
*   **Ticket IDs:** IC-460071, IC-460078
*   **Recommendation:** Audit billing logic for seat count sync and tier pricing before annual renewals.

### 2. HRIS Provisioning Failures
*   **Count:** 13 tickets
*   **Share:** 15.3%
*   **Distinct Accounts:** 3
*   **ARR Affected:** $114,000 (36k + 48k + 30k)
*   **Ticket IDs:** IC-460059, IC-460062
*   **Recommendation:** Fix HRIS sync job to create new hire accounts and add alerts for skipped records.

### 3. Points Non-Posting
*   **Count:** 20 tickets
*   **Share:** 23.5%
*   **Distinct Accounts:** 11
*   **ARR Affected:** $55,800 (sum of unique accounts)
*   **Ticket IDs:** IC-460004, IC-460006
*   **Recommendation:** Debug the recognition-to-credit pipeline to ensure delivered acknowledgments update balances.

### 4. Checkout & Redemption Errors
*   **Count:** 13 tickets
*   **Share:** 15.3%
*   **Distinct Accounts:** 9
*   **ARR Affected:** $84,900 (sum of unique accounts)
*   **Ticket IDs:** IC-460025, IC-460030
*   **Recommendation:** Repair checkout transaction atomicity and gift card delivery emails.

### 5. Slack Integration Instability
*   **Count:** 15 tickets
*   **Share:** 17.6%
*   **Distinct Accounts:** 5
*   **ARR Affected:** $27,700 (sum of unique accounts)
*   **Ticket IDs:** IC-460041, IC-460047
*   **Recommendation:** Stabilize Slack authentication tokens and fix slash command error handling.
marketing
case-study-matchPASS 1.00

? The Question

Given the prospect, rank the three most similar existing customers with public case studies for use as social proof. Similarity on industry, size band, use case, region. Cite the fields that drove each match. Do not name any customer without a case study and never include billing data or contact names.
cost $0.0006 · 6s · in 1,859 / out 2,923 tok
✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

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

Model's actual answer

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**Prospect Profile**
Alias: C-82AF3719
Industry: Technology
Size Band: Mid-Market
Use Case: employee_recognition
Region: NA-West

---

**Ranking Logic**
Scored candidates (has_case_study=true) on 4 fields: Industry, Size Band, Use Case, Region.
Match count = (Industry Match ? 1 : 0) + (Size Match ? 1 : 0) + (Use Case Match ? 1 : 0) + (Region Match ? 1 : 0)

---

**1. C-11C31562 (Score: 3/4)**
*   **Industry:** Manufacturing vs Technology (0)
*   **Size Band:** Mid-Market vs Mid-Market (1)
*   **Use Case:** employee_recognition vs employee_recognition (1)
*   **Region:** NA-West vs NA-West (1)
*   **Fields driving match:** Size Band, Use Case, Region

**2. C-64171065 (Score: 3/4)**
*   **Industry:** Technology vs Technology (1)
*   **Size Band:** Mid-Market vs Mid-Market (1)
*   **Use Case:** employee_recognition vs employee_recognition (1)
*   **Region:** NA-East vs NA-West (0)
*   **Fields driving match:** Industry, Size Band, Use Case

**3. C-A13C193D (Score: 3/4)**
*   **Industry:** Technology vs Technology (1)
*   **Size Band:** Mid-Market vs Mid-Market (1)
*   **Use Case:** retention vs employee_recognition (0)
*   **Region:** NA-West vs NA-West (1)
*   **Fields driving match:** Industry, Size Band, Region

*(Note: Only 3 candidates exist with has_case_study=true that match on at least 3 of 4 fields. Other case-study customers matched on 2 or fewer fields.)*
channel-attribution-math0.60

? The Question

Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.
cost $0.0014 · 10s · in 5,351 / out 7,784 tok
✓ undefined_channel✗ undefined_not_zero✓ flagged_rows✓ best_channel✗ reallocation

✓ Correct Answer (ground truth)

months: 2026-03, 2026-04, 2026-05, 2026-06, 2026-07, 2026-08
per_channel: {"linkedin_ads": {"spend_usd": 24000, "sqms": 25, "sqos": 8, "pipeline_amount": 96000, "cost_per_sqm": 960.0, "cost_per_sqo": 3000.0, "sqm_to_sqo_rate": 0.32, "pipeline_per_dollar": 4.0}, "organic_sea
undefined_channels: paid_social
flagged_rows_sqo_before_sqm: CT-000041, CT-000044
best_pipeline_per_dollar_channel: paid_search
reallocation_recommendation: from=paid_social; to=paid_search; rationale=paid_social has spend but zero SQMs (cost per SQM undefined, not zero); paid_search has the highest pipeline per dollar.
confidence_note: webinars and referral have small SQM samples (<20); treat rates as directional.
graded checks: undefined_channel · undefined_not_zero · flagged_rows · best_channel · reallocation

Model's actual answer

show the model's full answer
<function=execute_code>
<parameter=code>
import csv
from collections import defaultdict

# Data provided in prompt
spend_data = [
    ("paid_search", "2026-03", 6000), ("paid_search", "2026-04", 6000), ("paid_search", "2026-05", 6000),
    ("paid_search", "2026-06", 6000), ("paid_search", "2026-07", 6000), ("paid_search", "2026-08", 6000),
    ("linkedin_ads", "2026-03", 4000), ("linkedin_ads", "2026-04", 4000), ("linkedin_ads", "2026-05", 4000),
    ("linkedin_ads", "2026-06", 4000), ("linkedin_ads", "2026-07", 4000), ("linkedin_ads", "2026-08", 4000),
    ("paid_social", "2026-03", 3000), ("paid_social", "2026-04", 3000), ("paid_social", "2026-05", 3000),
    ("paid_social", "2026-06", 3000), ("paid_social", "2026-07", 3000), ("paid_social", "2026-08", 3000),
    ("webinars", "2026-03", 1500), ("webinars", "2026-04", 1500), ("webinars", "2026-05", 1500),
    ("webinars", "2026-06", 1500), ("webinars", "2026-07", 1500), ("webinars", "2026-08", 1500),
]

contacts_data = [
    ("CT-000016", "paid_search", "2026-05-26", "2026-05-28", 40000),
    ("CT-000034", "paid_search", "2026-07-23", "", 0),
    ("CT-000113", "referral", "2026-04-14", "2026-04-27", 8000),
    ("CT-000075", "webinars", "2026-03-03", "", 0),
    ("CT-000040", "paid_search", "2026-04-03", "", 0),
    ("CT-000098", "organic_search", "2026-04-25", "", 0),
    ("CT-000020", "paid_search", "2026-05-05", "", 0),
    ("CT-000019", "paid_search", "2026-06-22", "", 0),
    ("CT-000048", "linkedin_ads", "2026-03-17", "2026-03-25", 12000),
    ("CT-000049", "linkedin_ads", "2026-04-22", "", 0),
    ("CT-000047", "linkedin_ads", "2026-04-05", "2026-04-09", 12000),
    ("CT-000074", "webinars", "2026-06-03", "", 0),
    ("CT-000105", "organic_search", "2026-08-03", "", 0),
    ("CT-000009", "paid_search", "2026-08-25", "2026-08-28", 40000),
    ("CT-000022", "paid_search", "2026-03-08", "", 0),
    ("CT-000035", "paid_search", "2026-06-04", "", 0),
    ("CT-000083", "organic_search", "2026-08-21", "2026-08-25", 9000),
    ("CT-000029", "paid_search", "2026-04-03", "", 0),
    ("CT-000001", "paid_search", "2026-07-06", "2026-07-26", 40000),
    ("CT-000087", "organic_search", "2026-06-02", "2026-06-09", 9000),
    ("CT-000017", "paid_search", "2026-06-06", "2026-06-21", 40000),
    ("CT-000051", "linkedin_ads", "2026-06-11", "", 0),
    ("CT-000046", "linkedin_ads", "2026-05-01", "2026-05-13", 12000),
    ("CT-000026", "paid_search", "2026-04-11", "", 0),
    ("CT-000033", "paid_search", "2026-05-11", "", 0),
    ("CT-000097", "organic_search", "2026-07-17", "", 0),
    ("CT-000078", "organic_search", "2026-03-12", "2026-03-14", 9000),
    ("CT-000011", "paid_search", "2026-04-20", "2026-04-28", 40000),
    ("CT-000122", "referral", "2026-08-06", "", 0),
    ("CT-000006", "paid_search", "2026-08-09", "2026-08-17", 40000),
    ("CT-000015", "paid_search", "2026-08-02", "2026-08-16", 40000),
    ("CT-000065", "linkedin_ads", "2026-08-06", "", 0),
    ("CT-000057", "linkedin_ads", "2026-04-26", "", 0),
    ("CT-000095", "organic_search", "2026-03-04", "", 0),
    ("CT-000014", "paid_search", "2026-07-23", "2026-07-28", 40000),
    ("CT-000086", "organic_search", "2026-07-24", "2026-07-26", 9000),
    ("CT-000090", "organic_search", "2026-05-04", "", 0),
    ("CT-000089", "organic_search", "2026-07-26", "", 0),
    ("CT-000119", "referral", "2026-06-23", "", 0),
    ("CT-000059", "linkedin_ads", "2026-06-18", "", 0),
    ("CT-000024", "paid_search", "2026-07-01", "", 0),
    ("CT-000039", "paid_search", "2026-04-28", "", 0),
    ("CT-000099", "organic_search", "2026-05-02", "", 0),
    ("CT-000013", "paid_search", "2026-06-19", "2026-06-28", 40000),
    ("CT-000110", "referral", "2026-06-05", "2026-06-18", 8000),
    ("CT-000102", "organic_search", "2026-07-09", "", 0),
    ("CT-000066", "webinars", "2026-06-11", "2026-06-24", 12000),
    ("CT-000043", "linkedin_ads", "2026-07-05", "2026-07-22", 12000),
    ("CT-000054", "linkedin_ads", "2026-07-05", "", 0),
    ("CT-000050", "linkedin_ads", "2026-05-25", "", 0),
    ("CT-000003", "paid_search", "2026-06-02", "2026-06-05", 40000),
    ("CT-000120", "referral", "2026-05-04", "", 0),
    ("CT-000072", "webinars", "2026-05-16", "", 0),
    ("CT-000063", "linkedin_ads", "2026-05-19", "", 0),
    ("CT-000007", "paid_search", "2026-03-28", "2026-03-28", 40000),
    ("CT-000070", "webinars", "2026-06-17", "2026-06-24", 12000),
    ("CT-000030", "paid_search", "2026-05-26", "", 0),
    ("CT-000114", "referral", "2026-03-14", "", 0),
    ("CT-000112", "referral", "2026-07-11", "2026-07-28", 8000),
    ("CT-000037", "paid_search", "2026-06-05", "", 0),
    ("CT-000053", "linkedin_ads", "2026-06-13", "", 0),
    ("CT-000004", "paid_search", "2026-04-05", "2026-04-10", 40000),
    ("CT-000107", "organic_search", "2026-08-24", "", 0),
    ("CT-000094", "organic_search", "2026-06-10", "", 0),
    ("CT-000081", "organic_search", "2026-06-05", "2026-06-21", 9000),
    ("CT-000088", "organic_search", "2026-06-16", "", 0),
    ("CT-000052", "linkedin_ads", "2026-08-24", "", 0),
    ("CT-000115", "referral", "2026-07-17", "", 0),
    ("CT-000044", "linkedin_ads", "2026-07-23", "2026-07-18", 12000),
    ("CT-000096", "organic_search", "2026-08-28", "", 0),
    ("CT-000028", "paid_search", "2026-03-08", "", 0),
    ("CT-000101", "organic_search", "2026-04-27", "", 0),
    ("CT-000064", "linkedin_ads", "2026-06-19", "", 0),
    ("CT-000045", "linkedin_ads", "2026-05-05", "2026-05-21", 12000),
    ("CT-000121", "referral", "2026-06-03", "", 0),
    ("CT-000060", "linkedin_ads", "2026-07-21", "", 0),
    ("CT-000055", "linkedin_ads", "2026-07-01", "", 0),
    ("CT-000118", "referral", "2026-03-23", "", 0),
    ("CT-000012", "paid_search", "2026-05-26", "2026-05-28", 40000),
    ("CT-000023", "paid_search", "2026-08-18", "", 0),
    ("CT-000062", "linkedin_ads", "2026-05-01", "", 0),
    ("CT-000100", "organic_search", "2026-08-10", "", 0),
    ("CT-000091", "organic_search", "2026-03-22", "", 0),
    ("CT-000058", "linkedin_ads", "2026-07-17", "", 0),
    ("CT-000036", "paid_search", "2026-03-11", "", 0),
    ("CT-000106", "organic_search", "2026-04-26", "", 0),
    ("CT-000111", "referral", "2026-03-02", "2026-03-19", 8000),
    ("CT-000109", "referral", "2026-05-02", "2026-05-22", 8000),
    ("CT-000103", "organic_search", "2026-08-26", "", 0),
    ("CT-000005", "paid_search", "2026-08-13", "2026-08-28", 40000),
    ("CT-000117", "referral", "2026-04-24", "", 0),
    ("CT-000042", "linkedin_ads", "2026-05-01", "2026-05-19", 12000),
    ("CT-000010", "paid_search", "2026-06-21", "2026-06-26", 40000),
    ("CT-000073", "webinars", "2026-07-27", "", 0),
    ("CT-000084", "organic_search", "2026-07-12", "2026-07-17", 9000),
    ("CT-000008", "paid_search", "2026-08-02", "2026-08-07", 40000),
    ("CT-000076", "webinars", "2026-03-14", "", 0),
    ("CT-000082", "organic_search", "2026-08-09", "2026-08-21", 9000),
    ("CT-000069", "webinars", "2026-06-06", "2026-06-25", 12000),
    ("CT-000018", "paid_search", "2026-05-11", "2026-05-13", 40000),
    ("CT-000021", "paid_search", "2026-04-24", "", 0),
    ("CT-000077", "webinars", "2026-06-13", "", 0),
    ("CT-000085", "organic_search", "2026-08-21", "2026-08-28", 9000),
    ("CT-000031", "paid_search", "2026-06-04", "", 0),
    ("CT-000093", "organic_search", "2026-03-01", "", 0),
    ("CT-000038", "paid_search", "2026-06-09", "", 0),
    ("CT-000025", "paid_search", "2026-03-01", "", 0),
    ("CT-000080", "organic_search", "2026-08-06", "2026-08-13", 9000),
    ("CT-000027", "paid_search", "2026-04-04", "", 0),
    ("CT-000116", "referral", "2026-05-22", "", 0),
    ("CT-000068", "webinars", "2026-07-22", "2026-07-28", 12000),
    ("CT-000041", "linkedin_ads", "2026-06-14", "2026-06-09", 12000),
    ("CT-000061", "linkedin_ads", "2026-08-23", "", 0),
    ("CT-000071", "webinars", "2026-04-11", "", 0),
    ("CT-000108", "referral", "2026-06-18", "2026-06-28", 8000),
    ("CT-000032", "paid_search", "2026-07-18", "", 0),
    ("CT-000002", "paid_search", "2026-07-12", "2026-07-27", 40000),
    ("CT-000104", "organic_search", "2026-08-27", "", 0),
    ("CT-000067", "webinars", "2026-05-17", "2026-05-28", 12000),
    ("CT-000079", "organic_search", "2026-07-04", "2026-07-11", 9000),
    ("CT-000056", "linkedin_ads", "2026-07-28", "", 0),
    ("CT-000092", "organic_search", "2026-05-28", "", 0),
]

# Aggregate Spend
spend = defaultdict(int)
for ch, month, val in spend_data:
    spend[ch] += val

# Aggregate Contacts Metrics
stats = defaultdict(lambda: {"sqm": 0, "sqo": 0, "pipeline": 0})
flags = []
for key, ch, sqm, sqo, p in contacts_data:
    stats[ch]["sqm"] += 1
    if sqo:
        stats[ch]["sqo"] += 1
        stats[ch]["pipeline"] += p
        if sqo < sqm:
            flags.append({"contact": key, "channel": ch, "sqm": sqm, "sqo": sqo})

# Summary Report
print("Spend Totals:")
for k,v in sorted(spend.items()): print(f"{k}: ${v}")

print("\nContact Stats:")
for k,v in sorted(stats.items()): print(f"{k}: SQM={v['sqm']}, SQO={v['sqo']}, Pipeline=${v['pipeline']}")

print("\nDate Integrity Flags:")
for f in flags: print(f"{f['contact']} ({f['channel']}): SQM={f['sqm']} > SQO={f['sqo']}")
</parameter>
<|tool_call_end|>
competitor-battlecard-updatePASS 1.00

? The Question

Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.
cost $0.0011 · 6s · in 16,779 / out 2,870 tok
✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
# Updated Battlecard: Rivally

Positioning
points-based recognition with engagement survey add-ons (S02, S06).

Pricing
$7 per user/month, annual billing required (S17, 2026-08-12).

Conflict: Official price was $5/user/month (S03, 2026-01; S08, 2026-04). Quotes ranged $6.50–$7/user/month (S13, S18).

Where they win
- EU enterprise teams and multi-language support (S12).
- EU data residency available (S15).
- Slack integration works out-of-the-box (S04).
- Support response time under 4 hours (S22).
- Recognition feed is engaging (S16).

Where we win
- Analytics depth and export formats (S20, S25).
- Admin tooling (bulk editing) (S24).
- SCIM provisioning (S10).
- Enterprise-quality reporting dashboards (S07).

Objections and responses
- "Their analytics are sufficient." → Rivally offers only CSV exports; reports are basic (S07, S20). We offer deeper analytics (S25).
- "Their integration works." → Slack works (S04), but enterprise provisioning is manual (S10).
- "Their admin tooling matches ours." → Bulk editing lacks support; admin tooling lags (S16, S24).

Recent changes
- Microsoft Teams app v2 in public preview (S19).
- Rivally Pulse add-on exits beta (S23).
- Pricing update: Recognition Starter increased to $7/user/month (S17).
- EU expansion: Dublin office opened; EU data residency GA; ex-Workday VP EMEA hired (S11, S15).

12-month win/loss record (2025-09 to 2026-08)
Wins: 13
Losses: 7
Total deals: 20
Win rate: 13 / 20 = 65%

Unverified facts from old card
- Rivally acquired by WorkHuman in 2025 (no snippet supports this).
nurture-sequence-diagnosisPASS 1.00

? The Question

Review the sequences. Per sequence: sent, open rate, reply rate, meeting rate, weakest step. Identify tracking errors (opened above sent), audience overlap between sequences, and the failure mode for anything under 2 percent reply. One change per weak sequence and which to fix first. Under 350 words.
cost $0.0011 · 8s · in 15,594 / out 3,062 tok
✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

per_sequence: {"New Logo Nurture": {"sent": 1386, "opened": 490, "replied": 90, "meetings": 27, "open_rate": 0.3535, "reply_rate": 0.0649, "meeting_rate": 0.0195, "weakest_step": 3}, "Expansion Nurture": {"sent": 8
tracking_error: sequence=Expansion Nurture; step=2; sent=300; opened=340; issue=opened exceeds sent
audience_overlap_pair: Cold Outbound - HR Leaders, Cold Outbound - People Ops
all_pairwise_overlaps: ['Cold Outbound - HR Leaders', 'Cold Outbound - People Ops'], ['Expansion Nurture', 'New Logo Nurture']
under_2pct_reply: Cold Outbound - HR Leaders
failure_mode: Cold Outbound - HR Leaders=opens healthy (~37%) but replies near zero from step 1 -> body/CTA not landing, not a deliverability or subject-line problem
fix_priority_first: Cold Outbound - HR Leaders
graded checks: tracking_error · overlap_pair · weakest_step · fix_priority

Model's actual answer

show the model's full answer
### Sequence Analysis

**New Logo Nurture**
- Sent: 500 → 428
- Open Rate: 42% → 28%
- Reply Rate: 8.4% → 4.2%
- Meeting Rate: 2.4% → 1.4%
- Weakest Step: Step 3

**Expansion Nurture**
- Sent: 300 → 275
- Open Rate: 43.3% → 34.5% (**Step 2: 113.3%**)
- Reply Rate: 7.3% → 4.4%
- Meeting Rate: 1.7% → 1.1%
- Weakest Step: Step 3

**Cold Outbound - HR Leaders**
- Sent: 600 → 590
- Open Rate: 40% → 22%
- Reply Rate: 0.8% → 0.2%
- Meeting Rate: 0%
- Weakest Step: Step 3

**Cold Outbound - People Ops**
- Sent: 400 → 377
- Open Rate: 37.5% → 21.2%
- Reply Rate: 3.5% → 1.6%
- Meeting Rate: 0.75% → 0.27%
- Weakest Step: Step 3

---

### Data Issues
**Tracking Error:** Expansion Nurture Step 2 (340 opened > 300 sent).

**Audience Overlap:** None detected. Contacts mapped to distinct sequences without overlap.

**Failure Modes (<2% reply):**
- Cold Outbound - HR Leaders: 0.8%–0.2% (Step 3). Failure mode: Cold audience fatigue or poor fit.
- Cold Outbound - People Ops Step 3: 1.6% (below 2% threshold). Failure mode: Value proposition decay by Step 3.

---

### Changes

**New Logo Nurture:** Simplify Step 3 to a value-add case study instead of a meeting ask.
**Expansion Nurture:** Resolve tracking anomaly; fix deduplication in CRM.
**Cold Outbound - HR Leaders:** Replace generic cold outreach with warm referral intro.
**Cold Outbound - People Ops:** Cut Step 3; consolidate follow-up into Step 2.

**Fix Order:**
1. Expansion Nurture (data integrity blocks all analysis).
2. Cold Outbound - HR Leaders (highest volume, worst reply rates).
3. New Logo Nurture (healthy baseline, optimize conversion).
4. Cold Outbound - People Ops (moderate volume, step 3 truncation only).

Total word count: 305
weekly-marketing-goals-rollupPASS 1.00

? The Question

Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.
cost $0.0005 · 8s · in 1,400 / out 2,363 tok
✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

days_elapsed: 66
days_in_quarter: 92
elapsed_fraction: 0.7174
metrics: {"SQMs": {"qtd_actual": 230, "target": 300, "delta": -70, "expected_at_day_elapsed": 215.22, "pace": "ahead"}, "SQOs": {"qtd_actual": 84, "target": 120, "delta": -36, "expected_at_day_elapsed": 86.09,
coverage_ratio_active_pipeline: 0.75
pace_rule: expected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.
this_week_paragraph_required: True
graded checks: metrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer
**Weekly Marketing Goals Update (Q3-2026)**

**SQMs**
*   **QTD Actual:** 230
*   **Target:** 300
*   **Delta:** -70
*   **Pace:** 230/300 = 76.7% vs. 66/92 days elapsed (71.7%) → **Ahead**

**SQOs**
*   **QTD Actual:** 84
*   **Target:** 120
*   **Delta:** -36
*   **Pace:** 84/120 = 70% vs. 66/92 days elapsed (71.7%) → **Behind**

**DS2s**
*   **QTD Actual:** 40
*   **Target:** 75
*   **Delta:** -35
*   **Pace:** 40/75 = 53.3% vs. 66/92 days elapsed (71.7%) → **Behind**

**Closed-Lost MIA Rate**
*   **QTD Actual:** 5/25 = 20% (0.20)
*   **Target:** 10% (0.10)
*   **Delta:** +10% (Higher is worse)
*   **Status:** Significantly above target threshold.

**Same-Quarter Closes**
*   **QTD Actual:** 10
*   **Target:** 20
*   **Delta:** -10
*   **Pace:** 10/20 = 50% vs. 66/92 days elapsed (71.7%) → **Behind**

**Active Pipeline Coverage**
*   **QTD Actual:** 3,000,000
*   **Target:** 4,000,000
*   **Delta:** -1,000,000
*   **Pace:** 3,000,000/4,000,000 = 75% vs. 66/92 days elapsed (71.7%) → **Ahead**

**Weekly Movement**
The provided CSV files contain only Quarter-to-Date (QTD) cumulative totals; no weekly delta data is included in the input. Consequently, specific weekly movement cannot be calculated or reported.
communication
ceo-slack-compressionPASS 1.00

? The Question

Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.
cost $0.0011 · 5s · in 16,664 / out 2,584 tok
✓ max_3_sentences✓ under_80_words✓ weighted_present✓ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Suspend forecasting until owners are assigned. The $115,977 Q3 forecast (44,729 COMMIT + 35% of 203,565 BEST_CASE) is unverified since owners are blank on 85 of 86 deals and why-buys is empty on all 7 COMMIT deals. Additionally, 32 deals including Deal-D348E1 ($13,770) push to October.
stalled-deal-followup-emailPASS 1.00

? The Question

Write a follow-up email for this stalled deal. Under 120 words. Reference one specific prior touch from the engagement history, make one clear ask, and match the tone of the prior thread. No invented details.
cost $0.0003 · 6s · in 1,584 / out 1,513 tok
✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Subject: Follow-up on Deal-0D2F7A

Hi Team,

I’m following up on my August 5 email, which included the recap of our July 28 demo and pricing for 150 seats.

I recall the People team had strong interest in the automated milestone awards and points catalog. Have you had a chance to review the pricing details internally?

If the budget aligns, I’d like to schedule a brief call with the VP People and HR admins to discuss next steps before the close date.

Best,
Alex Franklin
funnel-digest-paragraphPASS 1.00

? The Question

Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.
cost $0.0003 · 6s · in 1,419 / out 1,439 tok
✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**Marketing**
Marketing delivered 46 SQMs against a target of 52, falling short by 6. Webinars drove 18 of those SQMs. Paid search cost per SQM held steady at $150.

**Sales**
Sales converted 14 SQOs and booked 9 DS2 meetings. New pipeline created totaled $310,000. We closed 3 deals this same quarter.

**Customer Success**
CS secured 2 renewals this week. Team NPS shifted to 61. Three accounts remain open red flags heading into next week.

**Product-Led Growth**
PLG brought in 412 new signups with activation at 31 percent. Thirty-eight companies hit the aha moment of 10 recognition gifts.
partner-digest-sectionPASS 1.00

? The Question

Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.
cost $0.0004 · 5s · in 1,582 / out 1,923 tok
✓ all_partners✓ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

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

Model's actual answer

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

**Active Partners**

1. **Apex Rewards Co**
   **Status:** Apex co-webinar locked for 09-15. Brought two sourced opps this week (both in DS1), logged with UTM Source = Partner.
   **Pipeline:** 2 deals totaling $275,000.
   *Arithmetic:* $180,000 (Deal-DDAAF2) + $95,000 (Deal-2CF33E) = $275,000.

2. **HRCloud Partners**
   **Status:** HRCloud integration referral closed the security review; opp moved to DS2.
   **Pipeline:** 1 deal totaling $140,000.
   *Arithmetic:* $140,000 (Deal-F1CDA5) = $140,000.

3. **CultureBridge**
   **Status:** CultureBridge lunch-and-learn produced two sourced opps; both early stage.
   **Pipeline:** 2 deals totaling $135,000.
   *Arithmetic:* $60,000 (Deal-096E1D) + $75,000 (Deal-067213) = $135,000.

4. **WorkWell Group**
   **Status:** WorkWell wants to restart the joint playbook in Q4; no sourced deals this period, planning call booked for 09-09.
   **Pipeline:** 0 deals, $0.

**Quiet Partners**

- Recogniq: No activity reported this period.
- KudosWave: No activity reported this period.
- PeopleFirst Advisors: No activity reported this period.
- TotalPerk: No activity reported this period.

**Total Partner Pipeline:** $550,000 (5 deals)
*Verification:* $275,000 + $140,000 + $135,000 + $0 = $550,000.
rfp-answer-from-policyPASS 1.00

? The Question

Answer the 10 questionnaire items from the policy excerpts only. Cite the excerpt id for every answer. Where the excerpts do not answer the question, mark it unanswerable and say what document would; do not guess. Tone: vendor response, direct.
cost $0.0004 · 4s · in 1,532 / out 1,835 tok
✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Q1: Yes, customer data is encrypted at rest using AES-256. [P1]
Q2: Yes, data in transit is encrypted with TLS 1.2 or higher. [P1]
Q3: Yes, Bonusly maintains a SOC 2 Type II report, available to prospects under NDA. [P2]
Q4: Customer data is retained for the duration of the contract plus 90 days. [P3]
Q5: The subprocessor list is 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 retained for 35 days. [P6]
Q8: Unanswerable. The provided excerpts do not mention ISO 27001. A certificate of compliance or security certification summary would be required.
Q9: Unanswerable. The provided excerpts do not mention uptime SLA. The Service Level Agreement (SLA) or contract schedule would be required.
Q10: Unanswerable. The provided excerpts do not mention HIPAA or BAA. The HIPAA compliance addendum or BAA template would be required.
ops-maintenance
skill-file-reconciliationPASS 1.00

? The Question

Reconcile the skill set below against its manifest. Find and report (with severity CRITICAL/WARNING/INFO and an action type TRIM_DESC, UPDATE_BODY, DELETE_SKILL, MERGE, or REVIEW): (1) any two skills whose ALWAYS-trigger phrases overlap/duplicate, (2) any circular delegation chain between skills (name it), (3) any delegation target that does not exist (dangling, name it), (4) any version conflict and which skill should survive, (5) how many manifest descriptions exceed 1,024 characters, (6) any hardcoded page ids, dates, or person names in skill bodies, (7) manifest drift in BOTH directions (files with no manifest row, and manifest rows with no file). Make one proposal per finding; rewrite nothing.
cost $0.0033 · 8s · in 68,834 / out 3,129 tok
✓ duplicate_trigger_found✓ circular_chain✓ dangling_target✓ oversized_desc_count✓ hardcoded_values✓ manifest_drift✓ severity_and_action

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
Based on the reconciliation of the provided skill set against the manifest:

### 1. Overlapping ALWAYS-trigger Phrases
**Finding:** `comms-drafter` and `email-drafter` explicitly trigger on the identical trigger of phrases: "write me an email," "draft a follow-up," "bump email," and "contract nudge."
*   **Severity:** WARNING
*   **Proposal:** TRIM_DESC on `comms-drafter`. Remove the email-specific triggers from the description and refer the user to `email-drafter` for those tasks.

### 2. Circular Delegation Chains
**Finding:** None found. No two manifest skills reference each other in a loop (A → B → A).
*   **Severity:** INFO
*   **Proposal:** REVIEW (No changes required).

### 3. Dangling Delegation Targets
**Finding:** Multiple skills reference capabilities not defined in the manifest (non-existent skills):
*   `email-drafter`: `bonusly-brand`
*   `deal-strategy-coach`: `prospect-research-multithreading`
*   `analysis-validator`: `bonusly-data-questions`, `bonusly-product-questions`, etc.
*   **Severity:** CRITICAL
*   **Proposal:** DELETE_SKILL on the manifest. Remove these dangling references from the bodies, OR (if they are core to the skill) add them to the manifest as separate rows.

### 4. Version Conflicts
**Finding:** None found. There are no duplicate definitions of the same capability in different versions.
*   **Severity:** INFO
*   **Proposal:** REVIEW (No changes required).

### 5. Manifest Descriptions Exceeding 1,024 Characters
**Calculation:**
Max description_chars = 1,006 (`pipeline-intelligence-report` / `signalforge-claim-compressor`).
1,006 ≤ 1,024.
**Finding:** 0 descriptions exceed 1,024 characters.

### 6. Hardcoded Page IDs, Dates, or Person Names
**Finding:** Significant hardcoded content found in skill bodies:
*   **IDs:** `analysis-validator` (HubSpot Owner IDs in Section 12.3), `partner-digest` (Confluence Cloud/Space/Folder IDs), `pipeline-intelligence-report` (HubSpot Org ID).
*   **Dates:** `analysis-validator` (May 9, 2026), `partner-digest` (May 16, 2026).
*   **Names:** `analysis-validator` (Alaina Loori, Shealagh Coughlin, Amani Phipps, etc.).
*   **Severity:** WARNING
*   **Proposal:** UPDATE_BODY on these skills. Replace hardcoded IDs/names with dynamic lookups or generic placeholders where feasible.

### 7. Manifest Drift
**Calculation:**
*   Files in manifest = 14.
*   Files provided = 14.
*   Matched rows = 14.
*   **Finding:** 0 files with no manifest row. 0 manifest rows with no file.
runbook-from-slack-threadPASS 1.00

? The Question

Turn the resolved incident thread into a runbook: ordered steps with the exact command or action taken, who ran it, how success was verified, and rollback for each step that changed state. Every step must trace to a message in the thread. Add nothing the thread does not contain; where the thread is unclear, mark the step as needing confirmation.
cost $0.0005 · 6s · in 1,835 / out 2,659 tok
✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

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

Model's actual answer

show the model's full answer
**1. Acknowledge Alert & Assume Incident Commander Role**
*   **Message Reference:** [M01]
*   **Action:** Acknowledge PagerDuty alert fired for reward-worker queue depth > 10k.
*   **Who:** Bryce Harmon
*   **Verification:** Alert acknowledged; assumed Incident Commander (IC) role.
*   **Rollback:** N/A (status change only)

**2. Assess Queue Depth**
*   **Message Reference:** [M02]
*   **Action:** Run `bundle exec rake sidekiq:queue_depth`
*   **Who:** Farid Osman
*   **Verification:** Output shows reward queue at 48,213 pending jobs. (Baseline: under 500)
*   **Rollback:** N/A (read-only diagnostic)

**3. Inspect Dead Set**
*   **Message Reference:** [M03]
*   **Action:** Inspect Sidekiq dead set for recent errors.
*   **Who:** Farid Osman
*   **Verification:** Counted 112 dead jobs, all Redis::TimeoutError from ~13:58.
*   **Rollback:** N/A (read-only diagnostic)

**4. Disable Auto Enqueue**
*   **Message Reference:** [M04]
*   **Action:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`
*   **Who:** Farid Osman
*   **Verification:** Not explicitly confirmed immediately after action; subsequent queue reduction implies success.
*   **Rollback:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`

**5. Clear Dead Set**
*   **Message Reference:** [M05]
*   **Action:** Clear out the dead set from the console.
*   **Who:** Elena Sinclair
*   **Verification:** Not explicitly confirmed after action.
*   **Rollback:** Not provided in thread (action is irreversible without re-injection)

**6. Scale Workers Up**
*   **Message Reference:** [M06]
*   **Action:** `kubectl scale deployment/reward-worker --replicas=6`
*   **Who:** Bryce Harmon
*   **Verification:** Queue depth observed down to 9,400 and falling ~1,200/min ([M07]).
*   **Rollback:** `kubectl scale deployment/reward-worker --replicas=3`

**7. Final Verification**
*   **Message Reference:** [M08]
*   **Action:** Run `bundle exec rake sidekiq:queue_depth`
*   **Who:** Cole Ingram
*   **Verification:** Queue depth returns 0; Error rate in Datadog back to baseline.
*   **Rollback:** N/A (read-only diagnostic)

**8. Re-enable Auto Enqueue**
*   **Message Reference:** [M09]
*   **Action:** `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'`
*   **Who:** Bryce Harmon
*   **Verification:** 40 new jobs processed cleanly in the next 3 minutes.
*   **Rollback:** `bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'`

**9. Scale Workers Down**
*   **Message Reference:** [M10]
*   **Action:** `kubectl scale deployment/reward-worker --replicas=3`
*   **Who:** Bryce Harmon
*   **Verification:** Queue stable at 0.
*   **Rollback:** `kubectl scale deployment/reward-worker --replicas=6`
datadog-incident-timelinePASS 1.00

? The Question

From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.
cost $0.0005 · 6s · in 3,160 / out 2,377 tok
✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

first_error: timestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s
cascade_order: service=reward-service; first_error_at=2026-09-03T14:01:12Z | service=sidekiq; first_error_at=2026-09-03T14:01:40Z | service=api-gateway; first_error_at=2026-09-03T14:03:05Z | service=web-app; first_error_at=2026-09-03T14:03:30Z
job_class_involved: RewardGiveJob
confirming_query: service:reward-service status:error "@message:Redis::TimeoutError"
logs_do_not_show: root cause of the Redis timeout, redis-server's own logs, infrastructure metrics (CPU/memory/network), any deploy or config change preceding the incident, who applied mitigation or when (no actor fields in logs)
clean_services: postgres
graded checks: first_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

show the model's full answer
**First Error**
*   **Timestamp:** 2026-09-03T14:01:12Z
*   **Service:** reward-service
*   **Message:** Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s

**Cascade Sequence**
1.  **14:01:12Z** — `reward-service` fails to connect to Redis (first error).
2.  **14:01:20Z, 14:01:30Z, 14:01:40Z** — `reward-service` exhausts retries for `RewardGiveJob` (8s, 18s, and 28s after the first error).
3.  **14:01:40Z** — `sidekiq` logs `RewardGiveJob failed`.
4.  **14:02:30Z** — `sidekiq` warns queue depth exceeds 10,000 (delayed by ~58s).
5.  **14:03:05Z** — `api-gateway` returns 502 upstream timeout (113s after first error).
6.  **14:03:30Z** — `web-app` logs form submission failure due to 502.

**Service & Job**
*   **Primary Service:** reward-service
*   **Job Class:** RewardGiveJob

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

**What the Logs Do Not Show**
*   Root cause of the Redis timeout (e.g., network partition, memory pressure, restart).
*   Total count of affected customers or lost revenue.
*   Client-side metrics (e.g., number of failed form submissions).
*   Whether non-RewardGive jobs (e.g., RecognitionDigestJob) were impacted before logs recorded them.
feature-flag-state-summaryPASS 1.00

? The Question

Summarize the state of the listed feature flags: what each controls per the code excerpt, which companies or segments have it on, targeting rules, and any flag with no code reference or no targets. Company counts must match the export. Do not describe a flag that is not in the export.
cost $0.0005 · 7s · in 1,613 / out 2,593 tok
✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

flags: {"recognition_streaks_v2": {"state": "on", "targeting_rules": "segment:beta_companies", "company_count": 42}, "points_budget_guardrails": {"state": "on", "targeting_rules": "all_companies", "company_c
code_referenced: recognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2
orphan_flags_no_code_reference: legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup
flags_with_no_targets: paused_offboard_cleanup
rule: company counts must equal the export; do not describe flags absent from the export.
graded checks: orphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer
### Feature Flag Summary

**recognition_streaks_v2**
- **State:** on
- **Control:** Tracks streaks on give (`StreakTracker.record(give)`)
- **Targeting:** segment:beta_companies
- **Count:** 42

**points_budget_guardrails**
- **State:** on
- **Control:** Enforces budget limits (`BudgetService.new(company).enforce!(giver, points)`)
- **Targeting:** all_companies
- **Count:** 220

**slack_dm_nudges**
- **State:** on
- **Control:** Sends Slack DM nudges (`SlackDm.send_nudge(user)`)
- **Targeting:** segment:region_na
- **Count:** 87

**redeem_flow_redesign**
- **State:** off
- **Control:** Redeem UI component selection (V2 vs V1)
- **Targeting:** targeted_list
- **Count:** 12

**analytics_dashboard_v3**
- **State:** on
- **Control:** Dashboard version (AnalyticsV3)
- **Targeting:** segment:tier_three
- **Count:** 65

**ms_teams_app_v2**
- **State:** off
- **Control:** MS Teams app installation (`TeamsAppV2.install(company)`)
- **Targeting:** targeted_list
- **Count:** 9

**legacy_give_modal**
- **State:** off
- **Control:** No code reference found
- **Targeting:** segment:legacy_plan
- **Count:** 14

**survey_boosters_q3**
- **State:** on
- **Control:** No code reference found
- **Targeting:** segment:legacy_plan
- **Count:** 7

**paused_offboard_cleanup**
- **State:** off
- **Control:** No code reference found
- **Targeting:** None specified
- **Count:** 0

### Notes
- **Flags without code reference:** 3 (legacy_give_modal, survey_boosters_q3, paused_offboard_cleanup)
- **Flags without targets:** 1 (paused_offboard_cleanup)
- **Total exported flags:** 9
- **Counts match export:** Verified
nda-triagePASS 1.00

? The Question

Triage each NDA as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Cite the clause that drives each classification by quoting it. Catch embedded non-solicits, non-competes, missing carve-outs, and unusual term or governing law. One line of reasoning per NDA.
cost $0.0011 · 6s · in 16,325 / out 2,716 tok
✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

classifications: {"NDA-1": {"classification": "GREEN", "driving_reason": "standard mutual NDA: 2-year term, standard carve-outs, Delaware law", "driving_clause_quote": "Carve-outs: information that (a) is or becomes p
non_solicit_in: NDA-2
checks: classification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked
graded checks: green · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer
**NDA-1 — GREEN**  
**Reasoning:** Standard mutual confidentiality with standard carve-outs and reasonable obligation duration.  
**Arithmetic:** 2 years active term + 3 years post-termination survival = 5 years total obligation window.  
**Clause cited:** *"Carve-outs: information that (a) is or becomes publicly available through no breach, (b) was known prior to disclosure, (c) is received from a third party without duty of confidence, (d) is independently developed, or (e) must be disclosed by law or court order."*

**NDA-2 — YELLOW**  
**Reasoning:** Embedded non-solicitation restricts hiring flexibility beyond the term.  
**Arithmetic:** 3-year term + 18-month post-term non-solicitation window.  
**Clause cited:** *"Non-solicitation: 'During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment or hire any employee of the other party with whom it came into contact under this Agreement.'"*

**NDA-3 — RED**  
**Reasoning:** Severe restrictions including non-compete, no carve-outs, foreign law/jurisdiction, and one-way confidentiality.  
**Arithmetic:** 5-year confidentiality term + 3-year non-compete following Effective Date.  
**Clause cited:** *"Non-compete: 'For a period of three (3) years following the Effective Date, Recipient shall not, directly or indirectly, engage in or provide services to any business that competes with Discloser's business.'"*