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mistralai

mistralai/ministral-14b-2512

Mean 0.755 · 19/40 perfect tests · $0.43 total · modalities in: text, image · out: text · each card: the prompt → the correct answer (gold) → this model's actual answer

What do the modalities mean?

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

data-crm 5 tests

stage-hygiene-audit0.33

Plain-English question: How much of the open DS2+ pipeline is missing a substantive why-buys, and is anything missing DS1 timestamps or out of order?

Prompt — what the model saw

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

cost $0.0122 · 16s · in 56,450 / out 766 tok

✗ noncomp_pct✗ no_ds1_0✗ regressed_0✗ per_owner_breakdown✓ definitions_first✓ three_fixes

✓ Correct Answer (ground truth)

Plain-English answer: 81.5% of the 124 DS2+ deals are missing a substantive why-buys — Alex Franklin 48, Cole Ingram 19, Dana Mercer 18, Bryce Harmon 15, Farid Osman 1 — and every deal has a DS1 timestamp with none regressed.

The answer in one line: n_deals = 156; ds2plus = 124; noncomp_pct = 81.5; bryce_noncomp = 15; no_ds1 = 0; regressed = 0 (+1 more below)

n_deals156

bryce_noncomp15

ds2plus124

no_ds10

noncomp_pct81.5

regressed0

per_owner_noncompAlex Franklin=48 of 61 (78.7%); Cole Ingram=19 of 22 (86.4%); Dana Mercer=18 of 22 (81.8%); Bryce Harmon=15 of 18 (83.3%); Farid Osman=1 of 1 (100%)

graded checksnoncomp_pct · no_ds1_0 · regressed_0 · per_owner_breakdown · definitions_first · three_fixes

Model's actual answer

show the model's full answer

### Stage Definitions
- **DS1**: Discovery Stage 1 (initial contact or qualification).
- **DS2**: Discovery Stage 2 (requirements gathering, deeper engagement).
- **DS3**: Discovery Stage 3 (proposal or pilot discussion).
- **DS4**: Discovery Stage 4 (negotiation or finalizing terms).
- **DS5**: Discovery Stage 5 (commitment or contract signed).

---

### Audit Results

#### **1. DS2+ Deals Missing Substantive `why_buys` (Non-Compliant)**
- **Total DS2+ deals**: 65
- **Non-compliant (empty or placeholder `why_buys`)**: 23
- **Non-compliant %**: **35.4%**
  - **Bryce Harmon (119337721)**: 10/21 (47.6%)
  - **Alex Franklin (84342457)**: 9/23 (39.1%)
  - **Dana Mercer (83155923)**: 2/10 (20.0%)
  - **Cole Ingram (83155924)**: 1/5 (20.0%)
  - **Farid Osman (716654662)**: 0/1 (0.0%)

#### **2. Deals Missing DS1 Timestamp**
- **Total missing DS1 timestamp**: **10**
  - **Deal IDs**: 63223160241, 60524834645, 60182332309, 61750885954, 60273519516, 64524461403, 61355900791, 62622451763, 60083540312, 61035342442

#### **3. Deals with Regressed Stage Timestamps**
- **Total regressed**: **1**
  - **Deal ID**: 63925115724 (DS2 entered after DS1 timestamp, but `t_ds2` is after `t_ds1` but no `t_ds3` timestamp exists, indicating a potential misalignment in stage progression).

---

### **Three Process Fixes**
1. **Automated Validation for `why_buys`**:
   - Implement a pre-submission check in the CRM to flag DS2+ deals with empty or placeholder `why_buys` values. Require manual review before advancing stages.

2. **Stage Timestamp Enforcement**:
   - Add a rule to prevent deals from being moved to a later stage (e.g., DS2) without a valid `t_ds1` timestamp. Use a validation script to auto-populate missing timestamps from `entered_current_stage` if no `t_ds1` exists.

3. **Owner-Specific Training**:
   - Targeted coaching for **Bryce Harmon** and **Alex Franklin** on completing `why_buys` fields for DS2+ deals. Highlight the impact of incomplete hygiene on pipeline health and deal velocity.

---

owner-resolution0.17

Plain-English question: Every open deal has a blank owner name — resolve each one to a real person using the owners table.

Prompt — what the model saw

The deal extract below has an owner name field that is blank on nearly every row and an owner id field that is populated. Using the owners table, resolve each open deal to an owner name. Report: number of deals resolved, deals whose owner id has no match in the owners table (list them), the owner ids that map to archived/deactivated owners (list their names), and the total pipeline amount per resolved owner. Do not guess a name for any unmatched id.

cost $0.0121 · 14s · in 56,380 / out 622 tok

✓ resolved_count✗ unmatched_none✗ deactivated_flagged✗ jake_amount✗ justin_amount✗ no_invented

✓ Correct Answer (ground truth)

Plain-English answer: All 156 deals resolve to six named owners; jake_amount maps to Bryce Harmon ($1,054,144) and justin_amount to Alex Franklin ($624,310).

The answer in one line: jake_amount = 1,054,144; justin_amount = 624,310; resolved = 156

jake_amount1,054,144

justin_amount624,310

resolved156

deactivated

per_ownerBryce Harmon=1,054,144; Alex Franklin=624,310; Dana Mercer=341,195; Cole Ingram=288,161.43; Farid Osman=4,134; Elena Sinclair=2,100

graded checksresolved_count · unmatched_none · deactivated_flagged · jake_amount · justin_amount · no_invented

Model's actual answer

show the model's full answer

### Analysis of Deals and Owners Resolution

#### Total Deals: **156**
#### Deals Resolved to Owner Names: **155**
#### Deals with Unmatched Owner IDs: **1**
- **Deal ID:** `64524667574` (Deal-57FF13)
- **Owner ID:** `701163055` (No match in owners table)

---

### Owner IDs Mapped to Archived/Deactivated Owners:
None of the owner IDs in the deals table map to archived/deactivated owners (all archived owners in the owners table are `1520255671` and `77260721`, which do not appear in the deals table).

---

### Total Pipeline Amount per Resolved Owner:
1. **Bryce Harmon (119337721):**
   - **Total Pipeline:** **$1,156,856.75**
   - Deals: 47

2. **Dana Mercer (83155923):**
   - **Total Pipeline:** **$292,675.40**
   - Deals: 20

3. **Alex Franklin (84342457):**
   - **Total Pipeline:** **$420,744.88**
   - Deals: 47

4. **Cole Ingram (83155924):**
   - **Total Pipeline:** **$107,865.40**
   - Deals: 10

5. **Farid Osman (716654662):**
   - **Total Pipeline:** **$6,880.00**
   - Deals: 6

6. **Elena Sinclair (701163055):**
   - **Total Pipeline:** **$2,100.00**
   - Deals: 1 (Unmatched ID in owners table)

---

### Breakdown of Pipeline Amounts by Owner:
- **Bryce Harmon:** $1,156,856.75
- **Alex Franklin:** $420,744.88
- **Dana Mercer:** $292,675.40
- **Cole Ingram:** $107,865.40
- **Farid Osman:** $6,880.00
- **Elena Sinclair:** $2,100.00 (Unmatched ID)

gong-hubspot-join-integrity0.50

Plain-English question: How many call records point at deal ids that do not exist in the open pipeline?

Prompt — what the model saw

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

cost $0.0161 · 17s · in 67,025 / out 578 tok

✗ orphan_count✓ dup_keys_0✗ ds3_coverage✓ sample_ids_present

✓ Correct Answer (ground truth)

Plain-English answer: 30 Gong call rows are orphans keyed to dead deal ids, none are duplicates, and DS3 call coverage is 29.4%.

The answer in one line: orphans = 30; ds3_cov_pct = 29.4; dup = 0

orphans30

ds3_cov_pct29.4

dup0

orphan_aliasesDeal-8FA85D, Deal-8FC3F9, Deal-3B7945, Deal-42B265, Deal-9CCC42, Deal-36EA09

graded checksorphan_count · dup_keys_0 · ds3_coverage · sample_ids_present

Model's actual answer

show the model's full answer

### Analysis of Call-to-Deal Mapping Integrity

#### (a) Orphaned Gong Rows (Deals with no match in `deals_open.csv`)
There are **39 orphaned rows** in `gong_calls_by_deal_90d.csv` where the `hs_deal_id` does not appear in `deals_open.csv`. Sample aliases:
- Deal-8FA85D
- Deal-8FC3F9
- Deal-3B7945
- Deal-42B265
- Deal-9CCC42
- Deal-36EA09
- Deal-9A43B4
- Deal-605F3C
- Deal-E2D34B
- Deal-76821A

---

#### (b) Duplicate Conversation Keys
No rows in `gong_calls_by_deal_90d.csv` have `calls_90d > distinct_conversation_keys`. All rows satisfy `calls_90d == distinct_conversation_keys`, so there are **no duplicates**.

---

#### (c) Open Deals at DS3 or Later with Logged Calls
From `deals_open.csv`, the open deals at **DS3 or later** are:
- **Total DS3+ deals**: 49
- **Deals with at least one logged call**: 28

**Share with logged calls**:
\[
\frac{28}{49} \approx 57.14\%
\]

**Sample aliases with calls**:
- Deal-C6FE92 (DS3)
- Deal-5408B0 (DS4)
- Deal-D348E1 (DS5)
- Deal-C26D20 (DS5)
- Deal-944310 (DS4)
- Deal-3974EB (DS4)
- Deal-60C2C2 (DS3)
- Deal-25F752 (DS4)

---

snowflake-sql-trapped-catalogPASS 1.00

Plain-English question: Write one Snowflake query for first-month usage per customer using only the provided, trap-laden catalog.

Prompt — what the model saw

Using ONLY the catalog provided, write one Snowflake SQL query returning, per customer company, for its first calendar month as a customer: unique givers, recognition count, successful redemption count. Read the catalog notes carefully: some tables are stale or unpopulated and must be avoided, and one documented filter (excluding deleted givers) must NOT be applied to historical giving counts. Put a comment above each FROM or JOIN explaining the table choice. Return only fenced SQL.

cost $0.0038 · 24s · in 17,615 / out 1,236 tok

✓ avoids_stale_deals✓ avoids_unpopulated_live✓ uses_cohort_summary✓ succeeded_filter✓ no_giver_deletion_filter✓ returns_sql

✓ Correct Answer (ground truth)

Plain-English answer: A single correct SQL query that uses the right catalog tables and avoids the trap columns — graded on the exact tables and columns.

The answer in one line: use_table = PRODUCTION.PLG.COMPANY_COHORT_SUMMARY

avoid_stalePRODUCTION.HUBSPOT.DEALS (stale)

avoid_unpopulatedV2_LIVE.OBJECTS_DEALS (unpopulated)

forbidden_filterdeleted-giver exclusion must NOT be applied

outputone fenced SELECT with per-FROM/JOIN table-choice comment

required_filtersucceeded redemptions only

use_tablePRODUCTION.PLG.COMPANY_COHORT_SUMMARY

graded checksavoids_stale_deals · avoids_unpopulated_live · uses_cohort_summary · succeeded_filter · no_giver_deletion_filter · returns_sql

Model's actual answer

show the model's full answer

```sql
WITH first_month_customers AS (
    -- Identify the first calendar month each company was a customer
    SELECT
        COMPANY_ID,
        DATE_TRUNC('month', FIRST_SUB_PAYMENT_DATE) AS first_month
    FROM
        PRODUCTION.PLG.COMPANY_COHORT_SUMMARY
    WHERE
        FIRST_SUB_PAYMENT_DATE IS NOT NULL
),

-- Get unique givers for each company in their first month
unique_givers AS (
    SELECT
        c.COMPANY_ID,
        COUNT(DISTINCT g.PROPERTY_HS_OBJECT_ID) AS unique_givers
    FROM
        PRODUCTION.PLG.COMPANY_COHORT_SUMMARY c
    JOIN
        HUBSPOT_HUB_1973303.V2_DAILY.ASSOCIATIONS_DEALS_TO_CONTACTS d2c
        ON c.COMPANY_ID = (
            SELECT PROPERTY_HS_OBJECT_ID
            FROM HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_CONTACTS
            WHERE PROPERTY_HS_EMAIL_DOMAIN = (
                SELECT PROPERTY_HS_EMAIL_DOMAIN
                FROM HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_CONTACTS
                WHERE PROPERTY_HS_OBJECT_ID = d2c.CONTACT_OBJECTID
                LIMIT 1
            )
        )
    JOIN
        HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_DEALS d
        ON d2c.DEAL_OBJECTID = d.PROPERTY_HS_OBJECT_ID
    WHERE
        DATE_TRUNC('month', d.PROPERTY_CLOSEDATE) = (
            SELECT first_month
            FROM first_month_customers
            WHERE first_month_customers.COMPANY_ID = c.COMPANY_ID
        )
        AND d.PROPERTY_PIPELINE = 'default'
    GROUP BY
        c.COMPANY_ID
),

-- Count recognitions (gives) for each company in their first month
recognition_counts AS (
    SELECT
        c.COMPANY_ID,
        COUNT(*) AS recognition_count
    FROM
        PRODUCTION.PLG.COMPANY_COHORT_SUMMARY c
    JOIN
        HUBSPOT_HUB_1973303.V2_DAILY.ASSOCIATIONS_DEALS_TO_CONTACTS d2c
        ON c.COMPANY_ID = (
            SELECT PROPERTY_HS_OBJECT_ID
            FROM HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_CONTACTS
            WHERE PROPERTY_HS_EMAIL_DOMAIN = (
                SELECT PROPERTY_HS_EMAIL_DOMAIN
                FROM HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_CONTACTS
                WHERE PROPERTY_HS_OBJECT_ID = d2c.CONTACT_OBJECTID
                LIMIT 1
            )
        )
    JOIN
        HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_DEALS d
        ON d2c.DEAL_OBJECTID = d.PROPERTY_HS_OBJECT_ID
    WHERE
        DATE_TRUNC('month', d.PROPERTY_CLOSEDATE) = (
            SELECT first_month
            FROM first_month_customers
            WHERE first_month_customers.COMPANY_ID = c.COMPANY_ID
        )
        AND d.PROPERTY_PIPELINE = 'default'
    GROUP BY
        c.COMPANY_ID
),

-- Count successful redemptions for each company in their first month
redemption_counts AS (
    SELECT
        c.COMPANY_ID,
        COUNT(*) AS successful_redemption_count
    FROM
        PRODUCTION.PLG.COMPANY_COHORT_SUMMARY c
    JOIN
        PRODUCTION.DEPRECATED_RECOGNITION.REDEMPTION_RECORDS_V2 r
        ON c.COMPANY_ID = (
            SELECT PROPERTY_HS_OBJECT_ID
            FROM HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_CONTACTS
            WHERE PROPERTY_HS_EMAIL_DOMAIN = (
                SELECT PROPERTY_HS_EMAIL_DOMAIN
                FROM HUBSPOT_HUB_1973303.V2_DAILY.OBJECTS_CONTACTS
                WHERE PROPERTY_HS_OBJECT_ID = r.CONTACT_ID
                LIMIT 1
            )
        )
    WHERE
        DATE_TRUNC('month', r.CREATED_AT) = (
            SELECT first_month
            FROM first_month_customers
            WHERE first_month_customers.COMPANY_ID = c.COMPANY_ID
        )
        AND r.STATE = 'succeeded'
    GROUP BY
        c.COMPANY_ID
)

-- Final result combining all metrics
SELECT
    c.COMPANY_ID,
    ug.unique_givers,
    rc.recognition_count,
    rd.successful_redemption_count
FROM
    PRODUCTION.PLG.COMPANY_COHORT_SUMMARY c
LEFT JOIN
    unique_givers ug ON c.COMPANY_ID = ug.COMPANY_ID
LEFT JOIN
    recognition_counts rc ON c.COMPANY_ID = rc.COMPANY_ID
LEFT JOIN
    redemption_counts rd ON c.COMPANY_ID = rd.COMPANY_ID
WHERE
    c.FIRST_SUB_PAYMENT_DATE IS NOT NULL;
```

crm-completeness-enrichmentPASS 1.00

Plain-English question: Audit the CRM for missing and invalid fields, and propose the fixes.

Prompt — what the model saw

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

cost $0.0056 · 38s · in 23,286 / out 2,587 tok

✓ dup_clusters✓ invalid_emails✓ survivor_language✓ completeness_pct✓ no_invent

✓ Correct Answer (ground truth)

Plain-English answer: 9 companies missing employee count, 6 missing HQ country, 4 invalid emails, 1 domain mismatch, 2 duplicate clusters, and 25 enrichment matches.

The answer in one line: n_contacts = 52; n_companies = 34; enrichment_matches = 25; invalid_email_count = 4; n_dup_clusters = 2; domain_mismatch_count = 1 (+4 more below)

n_contacts52

domain_mismatch_count1

enrichment_matches25

invalid_email_count4

n_companies34

n_dup_clusters2

company_missingindustry=0; employee_count=9; hq_country=6

domain_mismatchesCT-0011

duplicate_clusters['C-0A092931', 'C-0A092932'], ['C-0A092933', 'C-0A092934']

invalid_emailsCT-0010, CT-0080, CT-0081, CT-0192

graded checksdup_clusters · invalid_emails · survivor_language · completeness_pct · no_invent

Model's actual answer

show the model's full answer

### **CRM Data Audit Report**
#### **1. Completeness Analysis**
##### **Companies.csv (35 entries)**
| Field               | Missing Count | % Complete | Notes                                                                                     |
|---------------------|---------------|------------|-------------------------------------------------------------------------------------------|
| **industry**        | 10            | 71%        | Missing for: `C-96039F`, `C-44EA29`, `C-93C8BF`, `C-7BBDFA`, `C-50D386`, `C-2C60E5`, `C-EE9FFB` |
| **employee_count**  | 10            | 71%        | Missing for: `C-EC3025`, `C-96039F`, `C-44EA29`, `C-D04904`, `C-93C8BF`, `C-EE9FFB`, `C-77A95A`, `C-2C60E5`, `C-7BBDFA`, `C-50D386` |
| **hq_country**      | 10            | 71%        | Missing for: `C-2D1F1B`, `C-EC3025`, `C-96039F`, `C-44EA29`, `C-D04904`, `C-93C8BF`, `C-EE9FFB`, `C-7BBDFA`, `C-50D386`, `C-2C60E5` |

##### **Contacts.csv (53 entries)**
| Field       | Missing Count | % Complete | Notes                                                                                     |
|-------------|---------------|------------|-------------------------------------------------------------------------------------------|
| **email**   | 6             | 89%        | Invalid/missing for: `CT-0010`, `CT-0080`, `CT-0081`, `CT-0120`, `CT-0121`, `CT-0192`       |
| **title**   | 10            | 81%        | Missing for: `CT-0002`, `CT-0022`, `CT-0041`, `CT-0060`, `CT-0070`, `CT-0082`, `CT-0092`, `CT-0110`, `CT-0170`, `CT-0180` |
| **persona** | 10            | 81%        | Missing for: `CT-0000`, `CT-0022`, `CT-0041`, `CT-0060`, `CT-0072`, `CT-0080`, `CT-0081`, `CT-0092`, `CT-0120`, `CT-0121` |

---

#### **2. Duplicate Company Clusters**
**Cluster 1: `acme-corp.com`**
- **Aliases**: `C-0A092931`, `C-0A092932`
- **Survivor**: `C-0A092931` (industry: Technology, HQ: US, employees: 500)
- **Conflict**: `C-0A092932` (industry: tech, HQ: USA, employees: 510)
- **Recommendation**: Merge into `C-0A092931` (preferred due to consistency in industry/employee count).

**Cluster 2: `globex.io`**
- **Aliases**: `C-0A092933`, `C-0A092934`
- **Survivor**: `C-0A092933` (industry: SaaS, HQ: US, employees: 200)
- **Conflict**: `C-0A092934` (industry: Technology, HQ: US, employees: 200)
- **Recommendation**: Merge into `C-0A092933` (SaaS is more specific).

---

#### **3. Invalid Emails & Domain Mismatches**
**Invalid Emails**:
- `CT-0010`: `user0@` (invalid format)
- `CT-0080`: `user0@` (invalid format)
- `CT-0081`: `user1@` (invalid format)
- `CT-0120`: `user0@aa8dda.com` (no `@` symbol in data, but domain matches)
- `CT-0121`: `user1@aa8dda.com` (no `@` symbol in data, but domain matches)
- `CT-0192`: `user2@` (invalid format)

**Domain Mismatches**:
- `CT-0011`: `user1@other-domain.com` (domain does not match `66d1fc.com`)

---

#### **4. Enrichment Discrepancies**
| Company Alias | CRM Industry       | Enrichment Industry       | CRM HQ Country | Enrichment HQ Country | CRM Employees | Enrichment Employees |
|---------------|--------------------|---------------------------|----------------|-----------------------|----------------|----------------------|
| `C-66D1FC`    | tech               | Computer Software        | US             | United States         | 900            | 900                  |
| `C-EC3025`    | Technology         | Computer Software        | USA            | United States         |                | 400                  |
| `C-96039F`    | Finance            | Finance                  | USA            | United States         |                | 400                  |
| `C-44EA29`    | tech               | Computer Software        |                |                       |                | 400                  |
| `C-92D97D`    | Technology         | Computer Software        | Canada         | Canada                | 50             | 50                   |
| `C-77A95A`    | Technology         | Computer Software        | US             | United States         | 1500           | 1500                 |
| `C-AA8DDA`    | Technology         | Computer Software        | Canada         | Canada                | 1500           | 1500                 |
| `C-B23205`    | Healthcare         | Healthcare               | US             | United States         |                | 400                  |
| `C-E51FB7`    | Finance            | Finance                  | USA            | United States         | 1500           | 1500                 |
| `C-63A874`    | Healthcare         | Healthcare               | Canada         | Canada                | 340            | 340                  |
| `C-D0662E`    | Retail             | Retail                   | US             | United States         | 1500           | 1500                 |
| `C-B25F40`    | Tech               | Computer Software        | Canada         | Canada                | 120            | 120                  |
| `C-60C75F`    | tech               | Computer Software        | United States  | United States         |                | 400                  |
| `C-425E2A`    | Tech               | Computer Software        | USA            | United States         | 50             | 50                   |

**Recommendations**:
- **Industry**: Prefer **enrichment** (more specific, e.g., "Computer Software" over "tech").
- **HQ Country**: Prefer **enrichment** (consistent formatting, e.g., "United States" over "US").
- **Employee Count**: Prefer **enrichment** where available (e.g., `C-EC3025` missing in CRM).

---

#### **5. Top 10 Fixes by Pipeline Impact**
*(Note: Pipeline amounts are not provided in the data. Assuming fixes are prioritized by **data completeness** and **enrichment alignment**.)*

| Priority | Fix Type                     | Company/Contact Alias | Field(s) to Fix                     | Notes                                                                                     |
|----------|------------------------------|-----------------------|-------------------------------------|-------------------------------------------------------------------------------------------|
| 1        | **Merge Duplicates**         | `C-0A092931`, `C-0A092932` | Merge into `C-0A092931`           | Resolve industry/HQ conflicts.                                                     |
| 2        | **Merge Duplicates**         | `C-0A092933`, `C-0A092934` | Merge into `C-0A092933`           | Resolve industry conflicts (SaaS vs. Technology).                                      |
| 3        | **Enrichment Override**      | `C-EC3025`            | industry, employee_count, hq_country | Use enrichment: "Computer Software", 400, "United States".                              |
| 4        | **Enrichment Override**      | `C-96039F`            | industry, employee_count, hq_country | Use enrichment: "Finance", 400, "United States".                                         |
| 5        | **Fix Invalid Emails**       | `CT-0010`, `CT-0080`, `CT-0081`, `CT-0192` | email | Replace with `userX@domain.com` (e.g., `user0@66d1fc.com`).                          |
| 6        | **Fix Domain Mismatch**      | `CT-0011`             | email                                | Replace `user1@other-domain.com` with `user1@66d1fc.com`.                                |
| 7        | **Enrichment Override**      | `C-44EA29`            | industry, employee_count           | Use enrichment: "Computer Software", 400.                                               |
| 8        | **Enrichment Override**      | `C-93C8BF`            | industry, employee_count, hq_country | Use enrichment: "Computer Software", 120, "Canada".                                      |
| 9        | **Fill Missing Fields**      | `C-2D1F1B`            | hq_country                          | Use enrichment: "United States".                                                         |
| 10       | **Fill Missing Fields**      | `C-7BBDFA`, `C-50D386` | industry, employee_count, hq_country | Use enrichment: "health care", 400, "Canada".                                           |

---
**Next Steps**:
1. **Merge duplicate companies** (`acme-corp.com`, `globex.io`).
2. **Override CRM fields** with enrichment data where discrepancies exist.
3. **Fix invalid emails** and domain mismatches.
4. **Fill missing fields** (industry, employee count, HQ country) using enrichment.

deal-intelligence 5 tests

closed-lost-classification0.60

Plain-English question: Why did we lose the last six months of deals — one primary category per deal, and whose side is the reason on?

Prompt — what the model saw

Classify every deal lost in the last 6 months below into one primary category: pricing, competitor, no decision, timing, product gap, champion left, or other. Also assign a side (Bonusly, buyer, unknown). Then summarize: category counts, the side split, the number of deals where the structured closed_lost_tag clearly disagrees with the free-text reason, and the two patterns most worth acting on. Base everything only on the tag and free-text provided.

cost $0.0062 · 120s · in 23,117 / out 4,848 tok

✗ total_90✗ competitor_top✓ category_counts_present✓ side_split✓ two_patterns

✓ Correct Answer (ground truth)

Plain-English answer: Every closed-lost deal gets exactly one category (pricing, competitor, no decision, timing, product gap, champion left, other) plus a side, per the computed classification.

The answer in one line: total_lost_6mo = 90; top_reason = competitor (26)

total_lost_6mo90

categoriespricing, competitor, no decision, timing, product gap, champion left, other

patternstwo most actionable, tag-vs-text disagreements counted

sideBonusly / buyer / unknown per deal

top_reasoncompetitor (26)

graded checkstotal_90 · competitor_top · category_counts_present · side_split · two_patterns

Model's actual answer

show the model's full answer

Here is the classification of lost deals in the last 6 months, based **only** on the provided data:

---

### **Classification by Category and Side**
| **Deal ID**       | **Deal Alias**   | **Closed Lost Tag**                     | **Free-Text Reason**                          | **Category**          | **Side**       |
|--------------------|------------------|------------------------------------------|-----------------------------------------------|-----------------------|----------------|
| 63027745829        | Deal-DB0AAC      | Lost- Timing (1 year or more)           | Rescheduled for 2027                          | Timing                | Buyer          |
| 63683330727        | Deal-F7F635      | Competitor                              | "Go in another direction"                    | Competitor            | Buyer          |
| 63327490589        | Deal-AC944F      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 63027809948        | Deal-214060      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 49134744746        | Deal-91A056      | Lost- Timing (1 year or more)           | Reconnect in 2027                             | Timing                | Buyer          |
| 48988037529        | Deal-29326C      | Lost- Timing (1 year or more)           | Timing                                        | Timing                | Buyer          |
| 64524670260        | Deal-5DB9B0      | Lost- Does not fit ICP (write in notes)  | Spam                                          | Other                 | Bonusly        |
| 63836912221        | Deal-831B7B      | Lost- Timing (1 year or more)           | Reconnect in new year                         | Timing                | Buyer          |
| 63680220945        | Deal-F97C37      | Competitor                              | "Other vendor had more diversified offerings" | Competitor            | Buyer          |
| 41554388661        | Deal-13E9CF      | Doing nothing/Not a priority/Cost       | R&R program deprioritized                     | No Decision           | Buyer          |
| 63222333276        | Deal-39E25C      | Lost- Timing (1 year or more)           | Reconnect next year                           | Timing                | Buyer          |
| 63291006863        | Deal-7ED004      | Lost- Budget/Price                      | Did not get budget approval                   | Pricing               | Buyer          |
| 59275344824        | Deal-21B045      | MIA                                      | MIA                                           | MIA                   | Unknown        |
| 58754552851        | Deal-B3ABED      | Lost- Timing (1 year or more)           | Revisit Q2 2028                               | Timing                | Buyer          |
| 62455767176        | Deal-422BA6      | Competitor                              | Preferred ADP TotalSource PEO partner          | Competitor            | Buyer          |
| 61050677765        | Deal-ED9AE7      | Lost DM                                 | Timing, budget, authority                    | Timing                | Buyer          |
| 61038826051        | Deal-988493      | MIA                                      | MIA                                           | MIA                   | Unknown        |
| 63222778291        | Deal-381C8C      | Competitor                              | Not moving forward with Bonusly                | Competitor            | Buyer          |
| 59418526836        | Deal-F308CA      | MIA                                      | No contact since intro                        | MIA                   | Unknown        |
| 62750632013        | Deal-F1E8A6      | Competitor                              | Not moving forward with Bonusly                | Competitor            | Buyer          |
| 60035957084        | Deal-B6AC09      | Lost- Timing (1 year or more)           | Revisiting in 2027                            | Timing                | Buyer          |
| 62750599045        | Deal-70F704      | Lost DM                                 | MIA, anniversary awards only                  | MIA                   | Unknown        |
| 61873010467        | Deal-E6E80A      | Lost- Timing (1 year or more)           | Pushed to early 2027                          | Timing                | Buyer          |
| 54322940958        | Deal-B038F0      | Lost- Timing (1 year or more)           | Pushed to early 2027                          | Timing                | Buyer          |
| 61625438845        | Deal-4664E1      | MIA                                      | No contact after intro                       | MIA                   | Unknown        |
| 63222258948        | Deal-175756      | Lost- Timing (1 year or more)           | On hold until 2027                            | Timing                | Buyer          |
| 63717524046        | Deal-E74A73      | Doing nothing/Not a priority/Cost       | Test points calculation manually              | No Decision           | Buyer          |
| 63661381816        | Deal-DDAB52      | Competitor                              | Rippl offers more at same cost                | Competitor            | Buyer          |
| 63514024330        | Deal-ACE061      | Competitor                              | Went with HeyTaco                             | Competitor            | Buyer          |
| 62852981522        | Deal-BB78F3      | Lost- Timing (1 year or more)           | Roll out plant-specific actions first         | Timing                | Buyer          |
| 60984778911        | Deal-D48E0B      | MIA                                      | MIA                                           | MIA                   | Unknown        |
| 61054009677        | Deal-15DA99      | Lost- Timing (1 year or more)           | Reconnect early 2027                          | Timing                | Buyer          |
| 49530802588        | Deal-F4AF5D      | Lost- Timing (1 year or more)           | Timing, early next year                       | Timing                | Buyer          |
| 62115565909        | Deal-79B7A1      | Lost- Timing (1 year or more)           | Timing                                        | Timing                | Buyer          |
| 62487728289        | Deal-583ADB      | MIA                                      | MIA                                           | MIA                   | Unknown        |
| 63680238945        | Deal-8E27DA      | Feature Request                         | Swag provider only, no R&R                     | Product Gap           | Buyer          |
| 63433935544        | Deal-2D2F8D      | Competitor                              | Moved in a different direction                | Competitor            | Buyer          |
| 60694374202        | Deal-E0441F      | MIA                                      | Stale, no contact                             | MIA                   | Unknown        |
| 60897501515        | Deal-7CB44D      | MIA                                      | No meaningful contact                         | MIA                   | Unknown        |
| 60848492546        | Deal-0F96AA      | Competitor                              | Not advancing to finalist demo                | Competitor            | Buyer          |
| 60355222018        | Deal-1BCA50      | Competitor                              | Budget and gift cards details                 | Pricing               | Buyer          |
| 61625560885        | Deal-7CC678      | Competitor                              | Nothing specific provided                    | Competitor            | Buyer          |
| 59370037379        | Deal-FAC17C      | Lost DM                                 | Contract approval pending                     | Timing                | Buyer          |
| 61052858247        | Deal-242273      | Competitor                              | Digitize internal points currency             | Competitor            | Buyer          |
| 56896716581        | Deal-50E5D8      | Doing nothing/Not a priority/Cost       | Leadership paused                             | No Decision           | Buyer          |
| 62706569880        | Deal-A2C349      | Competitor                              | Stick with Awardco                            | Competitor            | Buyer          |
| 59729560611        | Deal-9F176A      | Lost- Timing (1 year or more)           | Pause until end of year                      | Timing                | Buyer          |
| 61764780962        | Deal-7B2236      | Doing nothing/Not a priority/Cost       | Budget and shift in Kudos board needs         | No Decision           | Buyer          |
| 57663815975        | Deal-AFA56C      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 61129576246        | Deal-C7156E      | Competitor                              | Selected another vendor                       | Competitor            | Buyer          |
| 60866104098        | Deal-C33D91      | Lost- Budget/Price                      | Budget cuts                                   | Pricing               | Buyer          |
| 59086317965        | Deal-9048EB      | MIA                                      | Bad fit, multiple feature gaps               | Product Gap           | Buyer          |
| 60857702003        | Deal-5E64CE      | Doing nothing/Not a priority/Cost       | Nectar agreement fee too high                 | No Decision           | Buyer          |
| 61415737717        | Deal-8A0992      | Competitor                              | Canadian provider alignment                  | Competitor            | Buyer          |
| 63085142442        | Deal-D0C698      | Competitor                              | Past Kudos user                               | Competitor            | Buyer          |
| 56549284976        | Deal-69CF3D      | Lost- Timing (1 year or more)           | On Hold                                       | Timing                | Buyer          |
| 61507337022        | Deal-ECBF89      | Lost- Timing (1 year or more)           | On Hold for now                               | Timing                | Buyer          |
| 57663820059        | Deal-3618CC      | Lost DM                                 | Wanted Surveys                                | Product Gap           | Buyer          |
| 60548236897        | Deal-EECC02      | Competitor                              | Went another direction                        | Competitor            | Buyer          |
| 60896018951        | Deal-5AD03E      | Competitor                              | Wanted defined budget access                  | Pricing               | Buyer          |
| 62121718303        | Deal-D1A623      | Lost- Timing (1 year or more)           | Timing                                        | Timing                | Buyer          |
| 63189310018        | Deal-413C56      | Doing nothing/Not a priority/Cost       | Back to school priority                       | No Decision           | Buyer          |
| 60008683142        | Deal-47F1A1      | Competitor                              | Staying with WorkTango                        | Competitor            | Buyer          |
| 54352704007        | Deal-BF2A98      | Competitor                              | Deployed HiThrive                            | Competitor            | Buyer          |
| 62115549771        | Deal-2A292B      | Doing nothing/Not a priority/Cost       | Build internally                              | No Decision           | Buyer          |
| 60868303272        | Deal-D1AABF      | MIA                                      | No response                                   | MIA                   | Unknown        |
| 60331562409        | Deal-FEDBCB      | Doing nothing/Not a priority/Cost       | Reconnect end of year                         | No Decision           | Buyer          |
| 62622503749        | Deal-1E7DA9      | Competitor                              | Selected another platform                     | Competitor            | Buyer          |
| 61625500700        | Deal-2BBA21      | MIA                                      | No contact since intro                       | MIA                   | Unknown        |
| 62852981127        | Deal-286F9C      | Competitor                              | Not a good fit                                | Competitor            | Buyer          |
| 62704591183        | Deal-7FBAC6      | Doing nothing/Not a priority/Cost       | Leadership paused                             | No Decision           | Buyer          |
| 60008716662        | Deal-369281      | Competitor                              | Stayed with Paylocity                         | Competitor            | Buyer          |
| 61475258733        | Deal-386F6E      | MIA                                      | No response                                   | MIA                   | Unknown        |
| 61114491171        | Deal-9FCD0D      | Competitor                              | Canadian company alignment                    | Competitor            | Buyer          |
| 55624236610        | Deal-55867E      | Lost- Timing (1 year or more)           | Not moving forward                            | Timing                | Buyer          |
| 62853160058        | Deal-DAFB82      | Lost- Budget/Price                      | Budget needed for other priorities            | Pricing               | Buyer          |
| 59370028385        | Deal-2FEDDB      | Doing nothing/Not a priority/Cost       | Unsure on timing                              | No Decision           | Buyer          |
| 61024624821        | Deal-64B19A      | Competitor                              | Stayed with Motivosity                        | Competitor            | Buyer          |
| 60419904928        | Deal-3F86A0      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 60675690108        | Deal-096750      | MIA                                      | No meaningful contact                         | MIA                   | Unknown        |
| 61055126627        | Deal-F325A5      | Lost DM                                 | Layoffs and change in leadership              | Timing                | Buyer          |
| 61432497792        | Deal-ABD14C      | Doing nothing/Not a priority/Cost       | Not interested in program                    | No Decision           | Buyer          |
| 60868240474        | Deal-79E61A      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 60551632419        | Deal-8A119B      | Lost- Budget/Price                      | Didn't get approval                           | Pricing               | Buyer          |
| 60644185922        | Deal-AE7C4E      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 62115387928        | Deal-DAB4F1      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 61432389647        | Deal-B4B50F      | MIA                                      | Unresponsive                                  | MIA                   | Unknown        |
| 60786197933        | Deal-981AD4      | Feature Request                         | Doesn't fit UI, not UK focused                | Product Gap           | Buyer          |
| 62121470977        | Deal-DC77FE      | Competitor                              | Customization (label points as dollars)       | Competitor            | Buyer          |
| 61129575303        | Deal-5885B9      | MIA                                      | MIA                                           | MIA                   | Unknown        |

---

### **Summary**
#### **Category Counts**
- **Timing**: 19
- **Competitor**: 22
- **No Decision**: 12
- **Pricing**: 4
- **MIA**: 20
- **Product Gap**: 3
- **Other**: 1

#### **Side Split**
- **Buyer**: 68
- **Bonusly**: 1
- **Unknown**: 20

#### **Discrepancies Between `closed_lost_tag` and Free-Text**
There are **7 discrepancies** where the structured tag (`closed_lost_tag`) does not align with the free-text reason:
1. **Deal-5DB9B0**: Tag = "Lost- Does not fit ICP (write in notes)", Free-Text = "Spam" → **Other**
2. **Deal-8E27DA**: Tag = "Feature Request", Free-Text = "Swag provider only, no R&R" → **Product Gap**
3. **Deal-3618CC**: Tag = "Lost DM", Free-Text = "Wanted Surveys" → **Product Gap**
4. **Deal-981AD4**: Tag = "Feature Request", Free-Text = "Doesn't fit UI and not UK focused" → **Product Gap**
5. **Deal-9048EB**: Tag = "MIA", Free-Text = "Bad fit, multiple feature gaps" → **Product Gap**
6. **Deal-5E64CE**: Tag = "Doing nothing/Not a priority/Cost", Free-Text = "Nectar agreement fee too high" → **Pricing**
7. **Deal-5AD03E**: Tag = "Competitor", Free-Text = "Wanted more defined budget access" → **Pricing**

---

#### **Two Patterns Worth Acting On**
1. **Competitor Dominance**:
   - **22 deals** lost to competitors, often due to **integration partnerships** (e.g., ADP TotalSource, Canadian provider alignment) or **feature customization** (e.g., labeling points as dollars, surveying functionality).
   - **Action**: Highlight Bonusly’s **unique differentiators** (e.g., ease of use, integrations, or customization options) in demos and sales pitches. Address **specific competitor weaknesses** (e.g., "Unlike [Competitor], Bonusly offers X, Y, Z").

2. **Timing and No Decision Overlap**:
   - **31 deals** (Timing + No Decision) are **paused or deprioritized** due to **budget shifts, leadership changes, or internal priorities** (e.g., testing manually, back-to-school season, layoffs).
   - **Action**: **Re-engage strategically** with a **light-touch follow-up** (e.g., quarterly check-ins, case studies, or pilot offers) to **reopen discussions** when priorities shift. Avoid aggressive outreach—focus on **value-added content** (e.g., ROI calculators, success stories).

---

pipeline-tiering0.60

Plain-English question: Score all 156 open deals into exactly one of LOCK / ACTION / BUILD / REVIVE / WATCH / RISKY.

Prompt — what the model saw

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

cost $0.0035 · 93s · in 1,284 / out 2,788 tok

✓ json_returned✓ tier_vocabulary✗ tier_counts_sum✓ risky_deals_listed✗ no_lock_violation_claimed

✓ Correct Answer (ground truth)

Plain-English answer: All 156 open deals tiered exactly once across the six tiers using forecast category, stage, meetings, contacts, and recency.

The answer in one line: total = 156

total156

graded checksjson_returned · tier_vocabulary · tier_counts_sum · risky_deals_listed · no_lock_violation_claimed

Model's actual answer

show the model's full answer

```json
{
  "tier_counts": {
    "LOCK": 7,
    "RISKY": 13,
    "WATCH": 84,
    "ACTION": 7,
    "BUILD": 19,
    "REVIVE": 20
  },
  "tier_examples": {
    "LOCK": [
      "Deal-25F752",
      "Deal-D348E1",
      "Deal-C26D20"
    ],
    "RISKY": [
      "Deal-E53952",
      "Deal-5408B0",
      "Deal-9AAE5F"
    ],
    "WATCH": [
      "Deal-A5E80A",
      "Deal-499BF6",
      "Deal-2D1F1B"
    ],
    "ACTION": [
      "Deal-C6FE92",
      "Deal-EE195F",
      "Deal-46988D"
    ],
    "BUILD": [
      "Deal-D73B89",
      "Deal-CA7DC0",
      "Deal-3795AD"
    ],
    "REVIVE": [
      "Deal-012CB1",
      "Deal-523604",
      "Deal-483B2D"
    ]
  },
  "risky_deals": [
    "Deal-E53952",
    "Deal-5408B0",
    "Deal-9AAE5F",
    "Deal-6787C2",
    "Deal-C61CF7",
    "Deal-62D607",
    "Deal-C6D97A",
    "Deal-7B3B0F",
    "Deal-F9A08A",
    "Deal-0660B4",
    "Deal-BA571A",
    "Deal-3EED2C",
    "Deal-57FF13"
  ],
  "lock_violations": 6,
  "pipeline_shape": "The pipeline is heavily skewed toward early-stage deals (DS1-DS3) with a notable concentration of PIPELINE-forecast deals. LOCK tier deals are clustered in DS4-DS5 with strong meeting activity, while RISKY deals are primarily DS1-DS3 PIPELINE deals with no recent meetings or low contact counts. Engagement recency is uneven, with many DS1 deals showing no activity in 30 days despite forecasted pipeline status."
}
```

call-transcript-extractionPASS 1.00

Plain-English question: Extract the CRM write-back fields from each call transcript as JSON.

Prompt — what the model saw

For each transcript, extract CRM write-back fields as JSON: why-buys (prospect statements only), pain points, stakeholders from the speaker list, budget signal (prospect-stated or null), timeline signal, competitor mentioned (only if the prospect raised it), next step (only if explicitly agreed), objections, confidence. Never fill a field from what the rep said or hoped.

cost $0.0019 · 27s · in 4,730 / out 1,254 tok

✓ json_emitted✓ budget_signal✓ null_discipline✓ stakeholders✓ competitor_prospect_only

✓ Correct Answer (ground truth)

Plain-English answer: Per transcript: prospect-only why-buys, pain points, stakeholders, budget and timeline signals, and competitors — e.g. TX-001: '$40k earmarked', live before open enrollment.

The answer in one line: rep = Alex Franklin

allowed_newTX-001, TX-002, TX-003, TX-004, TX-005, TX-006, Achievers, Bucketlist, Nectar

per_transcript{"TX-001": {"why_buys": ["automating anniversary and birthday awards"], "pain_points": ["HR team of three cannot keep up with it manually", "track everything in a spreadsheet, and people slip through

repAlex Franklin

rep_sourced_nulls{"TX-003": ["budget_signal \u2014 only the rep mentioned pricing/budget"], "TX-004": ["next_step \u2014 rep proposed, prospect said 'no promises'"], "TX-006": ["budget_signal \u2014 rep decoy, prospec

graded checksjson_emitted · budget_signal · null_discipline · stakeholders · competitor_prospect_only

Model's actual answer

show the model's full answer

Here are the CRM write-back fields for each transcript, extracted directly from prospect statements:

---

### **Deal-CFE7F4 (TX-001)**
```json
{
  "why_buys": [
    "Automate anniversary and birthday awards to reduce manual workload for HR team of three",
    "Eliminate spreadsheet tracking to prevent people slipping through the cracks"
  ],
  "pain_points": [
    "Manual tracking of awards is unsustainable for HR team",
    "Spreadsheet-based process leads to errors and missed recognitions"
  ],
  "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 scheduled for September 12",
  "objections": ["Need SSO and audit logs for IT sign-off"],
  "confidence": "MEDIUM"
}
```

---

### **Deal-70BB30 (TX-002)**
```json
{
  "why_buys": [
    "Tie recognition to retention for hourly workforce (30% regretted turnover)",
    "Need a solution to address high turnover in hourly roles"
  ],
  "pain_points": [
    "Regretted turnover is over 30% in hourly workforce",
    "No current system to link recognition to retention"
  ],
  "stakeholders": ["Head of Total Rewards", "CFO"],
  "budget_signal": "$25k pilot budget approved for this quarter",
  "timeline_signal": "Decision by end of September",
  "competitor_mentioned": null,
  "next_step": "Pilot agreement sent to legal for review this week",
  "objections": ["Integration with Workday must be rock solid"],
  "confidence": "HIGH"
}
```

---

### **Deal-530B50 (TX-003)**
```json
{
  "why_buys": [
    "Make recognition visible across 12 retail locations",
    "Enable store managers to provide on-the-spot recognition"
  ],
  "pain_points": [
    "Store managers lack budget autonomy for recognition",
    "Recognition is currently invisible across retail locations"
  ],
  "stakeholders": ["People Ops Manager", "CEO"],
  "budget_signal": null,
  "timeline_signal": "No rush until Q1",
  "competitor_mentioned": "Bucketlist",
  "next_step": "Call scheduled with CEO (times to be sent by People Ops Manager)",
  "objections": ["CEO must be sold first; she decides all people-related decisions"],
  "confidence": "LOW"
}
```

---

### **Deal-180D02 (TX-004)**
```json
{
  "why_buys": [
    "Consolidate three separate recognition tools into one",
    "Integrate with HRIS to eliminate siloed tools"
  ],
  "pain_points": [
    "Paying for three separate tools with no HRIS integration",
    "Procurement cycle is slow (6-8 weeks minimum)",
    "Security review delays (3 months for last vendor)"
  ],
  "stakeholders": ["VP People", "IT Security Lead", "CFO"],
  "budget_signal": "$15k annually (approval threshold for VP People)",
  "timeline_signal": null,
  "competitor_mentioned": null,
  "next_step": null,
  "objections": [
    "Procurement cycle is slow (6-8 weeks minimum)",
    "Security review delays are a concern"
  ],
  "confidence": "LOW"
}
```

---

### **Deal-F8767A (TX-005)**
```json
{
  "why_buys": [
    "Automate service milestones",
    "Provide analytics on recognition equity across departments",
    "Address engagement gaps for night-shift teams (20 points lower engagement)"
  ],
  "pain_points": [
    "Night-shift teams feel invisible (20-point engagement gap)",
    "Exec team skeptical after failed rollout two years ago"
  ],
  "stakeholders": ["HR Director", "People Ops Coordinator", "Exec Team"],
  "budget_signal": "$12k approved under engagement line",
  "timeline_signal": "Live before January all-hands",
  "competitor_mentioned": "Nectar",
  "next_step": "Presentation to exec team scheduled for October 2",
  "objections": [
    "Must beat Nectar's pilot experience",
    "Exec team skepticism due to past failure"
  ],
  "confidence": "MEDIUM"
}
```

---

### **Deal-EE195F (TX-006)**
```json
{
  "why_buys": [
    "Reduce admin time for service awards (5 hours/month spent manually)",
    "Automate ordering and shipping of plaques"
  ],
  "pain_points": [
    "Time spent on manual admin for service awards is unsustainable",
    "COO prefers building things in-house"
  ],
  "stakeholders": ["HR Manager", "COO"],
  "budget_signal": null,
  "timeline_signal": "Q1 start (fiscal year resets in February)",
  "competitor_mentioned": null,
  "next_step": "One-pager sent to COO for review this week",
  "objections": [
    "COO prefers in-house solutions",
    "No urgency until fiscal year reset in February"
  ],
  "confidence": "LOW"
}
```

next-to-close0.75

Plain-English question: Which 3 deals are closest to signature right now?

Prompt — what the model saw

Which 3 deals are closest to signature right now? Use the CRM data and the Slack thread; Slack often has the real status before the CRM does. For each: deal, amount, why it is close, what is left. Under 250 words.

cost $0.0007 · 7s · in 1,642 / out 223 tok

✓ exactly_three✗ top3_named✓ slack_first✓ blocked_excluded

✓ Correct Answer (ground truth)

Plain-English answer: Deal-547B2B ($11,200), Deal-B7EBD1 ($9,000), and Deal-403845 ($9,000) — all DS5 COMMIT deals closing September 10-11, with what is left on each.

blocked_excludeddeal_alias=Deal-2465CE; reason=champion left + procurement freeze (Slack #sales-sparring 2026-09-04)

exactly_three1

ranking_ruleSlack signature-imminent deal ranks first; remaining ranked by stage (DS5>DS4), forecast (COMMIT>BEST_CASE>PIPELINE), close_date asc, amount desc

top3rank=1; deal_alias=Deal-547B2B; amount=11200; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11 | rank=2; deal_alias=Deal-B7EBD1; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-10 | rank=3; deal_alias=Deal-403845; amount=9000; stage=DS5; forecast_category=COMMIT; close_date=2026-09-11

graded checksexactly_three · top3_named · slack_first · blocked_excluded

Model's actual answer

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1. **Deal-547B2B** ($11,200)
   - **Why close**: Redlines returned clean, signing page sent, VP People confirmed signature for tomorrow (2026-09-05).
   - **What’s left**: Final e-signature.

2. **Deal-403845** ($9,000)
   - **Why close**: Order form with finance team (DS5/COMMIT stage, close date 2026-09-11).
   - **What’s left**: Finance approval.

3. **Deal-A2B47C** ($6,360)
   - **Why close**: Legal review in progress (DS5/COMMIT stage, close date 2026-09-11).
   - **What’s left**: Legal sign-off.

---
**Note**: Deal-2465CE ($5,400) is blocked (procurement freeze) and pulled from commit.

product-gap-detection0.75

Plain-English question: Which prospect complaints are true product gaps versus rollout or enablement issues?

Prompt — what the model saw

Surface true product gaps raised by prospects in active deals. For each candidate: quote the prospect line, classify as real gap, plan gate, or rollout/enablement issue using the product docs excerpt, and name the deal and amount. Only real gaps go in the summary. Rep language does not count as prospect voice.

cost $0.0011 · 15s · in 1,650 / out 609 tok

✓ real_gaps✗ plan_gate_classified✓ enablement_classified✓ gap_summary

✓ Correct Answer (ground truth)

Plain-English answer: Two real gaps (SCIM provisioning, ADP Workforce Now integration) separated from plan gates and enablement issues, each with the prospect quote and deal.

allowed_newTG-001, TG-002, TG-003, TG-004, TG-005, SCIM user provisioning, ADP Workforce Now integration, Custom report builder

enablement_issuestranscript_id=TG-004; deal_alias=Deal-84DBA6; amount=16000; feature=Slack integration recognition adoption; quote=We turned on the Slack integration in July, but half our managers still haven't given a single recognition — nobody ever trained them on it.; why_enablement=Slack integration exists on all plans; managers were never trained

excluded_rep_voicetranscript_id=TG-005; line=Full transparency — we don't have a native mobile app for hourly workers yet, though it's on the roadmap.; reason=rep voice, not prospect-raised

plan_gatestranscript_id=TG-003; deal_alias=Deal-CFE7F4; amount=32000; feature=Custom report builder; tier_required=Enterprise; quote=I tried to build a custom report combining recognition frequency with turnover and couldn't — is that possible on our current plan?

real_gapstranscript_id=TG-001; deal_alias=Deal-EC3025; amount=62000; feature=SCIM user provisioning; quote=We need SCIM user provisioning — our security team will not approve any tool that requires manual account management.; why_real=not listed in product docs at any tier | transcript_id=TG-002; deal_alias=Deal-D0D6B5; amount=23800; feature=ADP Workforce Now integration; quote=Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us.; why_real=supported HRIS list is Workday/BambooHR/Gusto/Rippling only

graded checksreal_gaps · plan_gate_classified · enablement_classified · gap_summary

Model's actual answer

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The file `transcripts_gaps.csv` does not exist in the current working directory (`/Users/amaniphipps`). However, the data was provided directly in the question, so I will analyze it directly.

---

### **Summary of True Product Gaps Raised by Prospects**

| **Prospect Line**                                                                                     | **Classification**               | **Deal Alias** | **Deal Amount** | **Notes**                                                                                     |
|------------------------------------------------------------------------------------------------------|----------------------------------|----------------|-----------------|-----------------------------------------------------------------------------------------------|
| *"We need SCIM user provisioning — our security team will not approve any tool that requires manual account management."* | **Real Gap**                    | Deal-EC3025    | N/A            | SCIM is not listed in product docs as a supported capability.                                |
| *"Our payroll runs on ADP Workforce Now and I don't see ADP anywhere in your integration list — that's a dealbreaker for us."* | **Real Gap**                    | Deal-D0D6B5    | N/A            | ADP Workforce Now is not listed in product docs as a supported HRIS integration.             |

---

### **Breakdown by Deal**
1. **Deal-EC3025**
   - **Gap:** SCIM user provisioning is required but not supported.
   - **Classification:** Real Gap

2. **Deal-D0D6B5**
   - **Gap:** ADP Workforce Now integration is required but not supported.
   - **Classification:** Real Gap

---

### **Excluded Items**
- **Deal-CFE7F4:** Custom report builder is an **Enterprise-tier feature**, not a gap. The prospect is on a lower tier.
- **Deal-84DBA6:** Slack integration adoption is an **enablement/rollout issue**, not a product gap.
- **Deal-36C33F:** Mobile app for hourly workers is on the **roadmap**, not a current gap.

---

rep-performance 5 tests

stale-pipeline-by-rep0.33

Plain-English question: Which open deals have had no email, call, or meeting in the last 7 days?

Prompt — what the model saw

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

cost $0.0300 · 86s · in 140,808 / out 3,145 tok

✗ stale_recall_ge_80✓ grouped_by_owner✓ jake_top_amount✗ kevin_stale_amt✗ jenn_stale_amt✗ stale_count

✓ Correct Answer (ground truth)

Plain-English answer: 65 open deals are stale; the largest is Deal-2D1F1B at $240,000, with Bryce Harmon carrying the most stale dollars at $626,243.

The answer in one line: jake_amt = 626,243; jenn_amt = 261,645; kevin_amt = 252,905.03; top_amount = 240,000; stale_count = 65; top_alias = Deal-2D1F1B

jake_amt626,243

jenn_amt261,645

kevin_amt252,905.03

stale_count65

top_amount240,000

top_aliasDeal-2D1F1B

graded checksstale_recall_ge_80 · grouped_by_owner · jake_top_amount · kevin_stale_amt · jenn_stale_amt · stale_count

Model's actual answer

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Here are the stale deals (no email, call, or meeting in the last 7 days) grouped by owner, sorted by amount (descending):

---
**Bryce Harmon**
- Deal-25F752, Bryce Harmon, DS4, 24000.0, 4 days
- Deal-E53952, Bryce Harmon, DS4, 19656.0, 5 days
- Deal-6787C2, Bryce Harmon, DS4, 7000.0, 2 days
- Deal-2D1F1B, Bryce Harmon, DS1, 240000.0, 19 days
- Deal-66D1FC, Bryce Harmon, DS1, 99000.0, 25 days
- Deal-950043, Bryce Harmon, DS1, 70000.0, 18 days
- Deal-332637, Bryce Harmon, DS2, 36000.0, 9 days
- Deal-036E80, Bryce Harmon, DS1, 30275.0, 3 days
- Deal-1BEEBF, Bryce Harmon, DS1, 31500.0, 18 days
- Deal-40522D, Bryce Harmon, DS3, 21000.0, 18 days
- Deal-1CCE5C, Bryce Harmon, DS3, 20880.0, 8 days
- Deal-7BBDFA, Bryce Harmon, DS3, 37440.0, 44 days
- Deal-93C8BF, Bryce Harmon, DS2, 36000.0, 2 days
- Deal-333EBB, Bryce Harmon, DS3, 2880.0, 2 days

**Stale Deal Stats for Bryce Harmon:**
13 stale deals, $567,671.25 total

---

**Alex Franklin**
- Deal-5408B0, Alex Franklin, DS4, 14850.0, 4 days
- Deal-547B2B, Alex Franklin, DS5, 11200.0, 6 days
- Deal-944310, Alex Franklin, DS4, 10500.0, 3 days
- Deal-403845, Alex Franklin, DS5, 9000.0, 2 days
- Deal-A2B47C, Alex Franklin, DS5, 6360.0, 2 days
- Deal-62D607, Alex Franklin, DS4, 4800.0, 2 days
- Deal-0660B4, Alex Franklin, DS4, 1920.0, 15 days
- Deal-927338, Alex Franklin, DS1, 10920.0, 4 days
- Deal-499BF6, Alex Franklin, DS2, 1249.0, 2 days
- Deal-3EED2C, Alex Franklin, DS2, 7200.0, 2 days
- Deal-6883F3, Alex Franklin, DS1, 2400.0, 15 days
- Deal-60C2C2, Alex Franklin, DS3, 19000.0, 2 days
- Deal-5296C9, Alex Franklin, DS3, 10000.0, 2 days
- Deal-885F45, Alex Franklin, DS2, 9300.0, 11 days
- Deal-278DEC, Alex Franklin, DS3, 2700.0, 2 days
- Deal-4A13AD, Alex Franklin, DS3, 2160.0, 25 days
- Deal-8AD4A5, Alex Franklin, DS3, 1800.0, 2 days
- Deal-15D24F, Alex Franklin, DS3, 3600.0, 2 days
- Deal-9D0060, Alex Franklin, DS3, 3840.0, 11 days
- Deal-36C33F, Alex Franklin, DS2, 15000.0, 2 days
- Deal-293AF3, Alex Franklin, DS3, 9000.0, 2 days
- Deal-712010, Alex Franklin, DS3, 7200.0, 2 days
- Deal-13FEBD, Alex Franklin, DS2, 4680.0, 11 days
- Deal-6C60D4, Alex Franklin, DS3, 4800.0, 11 days
- Deal-3BA5EA, Alex Franklin, DS3, 7200.0, 2 days
- Deal-5FDCE4, Alex Franklin, DS3, 1600.0, 11 days

**Stale Deal Stats for Alex Franklin:**
26 stale deals, $188,209.85 total

---

**Dana Mercer**
- Deal-9AAE5F, Dana Mercer, DS4, 11250.0, 2 days
- Deal-2465CE, Dana Mercer, DS5, 5400.0, 15 days
- Deal-584EE5, Dana Mercer, DS5, 4600.0, 4 days
- Deal-87DDD1, Dana Mercer, DS1, 5000.0, 18 days
- Deal-8952F0, Dana Mercer, DS3, 2100.0, 8 days
- Deal-BA3DDC, Dana Mercer, DS3, 23400.0, 14 days
- Deal-7E2131, Dana Mercer, DS3, 5400.0, 2 days
- Deal-7599B8, Dana Mercer, DS3, 7350.0, 17 days

**Stale Deal Stats for Dana Mercer:**
8 stale deals, $61,150.00 total

---

**Cole Ingram**
- Deal-7B3B0F, Cole Ingram, DS4, 2760.0, 3 days
- Deal-FD9F4E, Cole Ingram, DS5, 1330.0, 9 days
- Deal-42326B, Cole Ingram, DS3, 2480.4, 4 days
- Deal-FA32A0, Cole Ingram, DS3, 11116.0, 4 days

**Stale Deal Stats for Cole Ingram:**
4 stale deals, $16,686.40 total

---
No stale deals found for **Farid Osman** or **Elena Sinclair**.

activity-mix-vs-outcomePASS 1.00

Plain-English question: Per rep, what did the last 30 days of emails, calls, and meetings look like next to the deals that advanced?

Prompt — what the model saw

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

cost $0.0354 · 164s · in 147,382 / out 2,428 tok

✓ alex_ds2_30d✓ justin_volume_high✓ efficient_named✓ mix_present✓ no_causal_claim

✓ Correct Answer (ground truth)

Plain-English answer: Alex Franklin is the standout signal with 18 deals entering DS2 in 30 days (next closest: Bryce Harmon with 4), alongside per-rep activity totals.

The answer in one line: alex_ds2_30d = 18; bryce_ds2_30d = 4; note = legacy alias justin_ds2=4 actually held Bryce Harmon count; Alex Franklin entered 18 deals into DS2 in the 30d window

alex_ds2_30d18

bryce_ds2_30d4

notelegacy alias justin_ds2=4 actually held Bryce Harmon count; Alex Franklin entered 18 deals into DS2 in the 30d window

graded checksalex_ds2_30d · justin_volume_high · efficient_named · mix_present · no_causal_claim

Model's actual answer

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Here is the analysis of rep efficiency based on last-30-day activity and DS2 entries:

### Rep Efficiency Ranking (Lowest Activities/DS2 Entry = Most Efficient)
| Rank | Owner Name      | Total Activities | DS2 Entries | Efficiency Ratio (Activities/DS2) | Email % | Call % | Meeting % |
|------|-----------------|------------------|-------------|-----------------------------------|---------|--------|----------|
| 1    | Alex Franklin   | 384              | 18          | 21.33                            | 79.95   | 9.38   | 10.68    |
| 2    | Bryce Harmon      | 205              | 4           | 51.25                            | 79.02   | 0.00   | 20.98    |
| 3    | Cole Ingram | 111           | 2           | 55.50                            | 86.49   | 12.61  | 0.90     |
| 4    | Farid Osman     | 72               | 1           | 72.00                            | 52.78   | 0.00   | 47.22    |
| 5    | Dana Mercer      | 113              | 1           | 113.00                           | 74.34   | 15.93  | 9.73     |

### Summary
- **Most efficient rep**: Alex Franklin (Efficiency Ratio: **21.33 activities/DS2 entry**).
- **Highest-volume rep**: Alex Franklin (Total Activities: **384**).
- **Note**: The most efficient and highest-volume reps are the same.

### Activity Mix (Percentages of Total Activities)
- **Alex Franklin**: 80% emails, 9% calls, 11% meetings.
- **Bryce Harmon**: 79% emails, 0% calls, 21% meetings.
- **Cole Ingram**: 86% emails, 13% calls, 1% meetings.
- **Farid Osman**: 53% emails, 0% calls, 47% meetings.
- **Dana Mercer**: 74% emails, 16% calls, 10% meetings.

ae-qtd-scorecard0.67

Plain-English question: Build Alex Franklin's QTD scorecard: bookings versus quota, pipeline, and conversion.

Prompt — what the model saw

Build a QTD scorecard for the named AE as of 2026-09-05: bookings vs quota with attainment percent, new vs expansion split, active pipeline by stage with amount, rolling 90-day DS2-to-won rate, win and loss counts with top loss reason, activity volume by type in the last 30 days. Exclude any closed-won deal dated before the quarter. Close with three coaching observations grounded in the numbers.

cost $0.0069 · 59s · in 27,185 / out 2,913 tok

✓ bookings✓ quota✓ attainment✗ new_expansion_split✗ top_loss_reason✓ coaching_obs

✓ Correct Answer (ground truth)

Plain-English answer: 75% attainment ($150,000 on a $200,000 quota), $113,500 new versus $36,500 expansion, pipeline by stage, and the rolling DS2-to-won rate.

The answer in one line: quota = 200000; bookings = 150000; new_bookings = 113500; expansion_bookings = 36500; open_deals_count = 125; attainment_pct = 75; owner = Alex Franklin (+12 more below)

quota200,000

attainment_pct75

bookings150,000

expansion_bookings36,500

expansion_count3

losses_qtd27

new_bookings113,500

new_count5

open_deals_count125

wins_qtd8

activity_30d_totalsemails_30d=807; calls_30d=112; meetings_30d=128; notes_30d=50

allowed_new18 items — e.g. Deal-A1C3E5, Deal-B7D2F4, Deal-C9E1A6, Deal-D4B8C2

ds2_to_wonwindow_start=2026-06-07; won_in_window=8; closed_in_window=35; rate=0.2286

ownerAlex Franklin

pipeline_by_stage{"DS1": {"count": 20, "amount": 284621}, "DS2": {"count": 28, "amount": 353760}, "DS3": {"count": 67, "amount": 552705}, "DS4": {"count": 5, "amount": 23574}, "DS5": {"count": 5, "amount": 45730}}

prior_quarter_excludeddeal_alias=Deal-B3E6F1; close_date=2026-06-20; amount=24000

quarter2026-Q3

snapshot_date2026-09-05

top_loss_reasonreason=Lost- Timing (1 year or more); count=13

graded checksbookings · quota · attainment · new_expansion_split · top_loss_reason · coaching_obs

Model's actual answer

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### QTD Scorecard for Alex Franklin (as of 2026-09-05)

#### **1. Bookings vs. Quota**
- **Quota**: $200,000
- **Bookings (QTD, closed-won deals dated 2026-07-01 or later)**:
  - **New**: $118,500 (Deal-A1C3E5: $40,000 + Deal-B7D2F4: $35,000 + Deal-C9E1A6: $21,000 + Deal-D4B8C2: $11,000 + Deal-E6F3A9: $6,500 + Deal-C5D9E2: $4,500)
  - **Expansion**: $32,000 (Deal-F2C7D8: $20,000 + Deal-A8B4D6: $12,000)
  - **Total Bookings**: **$150,500**
  - **Attainment**: **75.25%**

---

#### **2. New vs. Expansion Split**
- **New**: $118,500 (79% of bookings)
- **Expansion**: $32,000 (21% of bookings)

---

#### **3. Active Pipeline by Stage (Amount)**
| Stage | Amount   |
|-------|----------|
| DS1   | $169,400 |
| DS2   | $184,400 |
| DS3   | $181,800 |
| DS4   | $19,080  |
| DS5   | $30,370  |
| **Total Pipeline** | **$585,050** |

---

#### **4. Rolling 90-Day DS2-to-Won Rate**
- **DS2 Deals Entered in Last 90 Days (2026-06-05 to 2026-09-05)**:
  - **Deal-50D386 (2026-06-12)**: $36,000 (open)
  - **Deal-4B0BEB (2026-06-12)**: $12,000 (open)
  - **Deal-6691E0 (2026-05-15)**: $5,700 (open)
  - **Deal-92D97D (2026-09-02)**: $60,000 (open)
  - **Deal-EC3025 (2026-06-12)**: $62,000 (open)
  - **Deal-9F2E43 (2026-08-03)**: $7,690 (open)
  - **Deal-5913B3 (2026-09-03)**: $7,500 (open)
  - **Deal-84DBA6 (2026-08-28)**: $16,000 (open)
  - **Deal-13FEBD (2026-08-04)**: $4,680 (open)
  - **Deal-A181B3 (2026-08-11)**: $7,200 (open)
  - **Deal-36C33F (2026-08-11)**: $15,000 (open)
  - **Deal-600CD9 (2026-08-10)**: $5,400 (open)
  - **Deal-7FA0C3 (2026-08-07)**: $1,400 (open)
  - **Deal-F436DA (2026-08-19)**: $2,520 (open)
  - **Deal-6A544F (2026-08-04)**: $3,240 (open)
  - **Deal-46988D (2026-08-26)**: $3,780 (open)
  - **Deal-05CBC9 (2026-06-12)**: $2,700 (open)
  - **Deal-57F4C2 (2026-07-29)**: $15,000 (open)
  - **Deal-885F45 (2026-07-02)**: $9,300 (open)
  - **Deal-690476 (2026-07-06)**: $3,600 (open)
  - **Deal-5AD94B (2026-07-17)**: $4,000 (open)
  - **Deal-40FFDE (2026-08-28)**: $16,800 (open)
  - **Deal-71590D (2026-08-28)**: $6,000 (open)
  - **Deal-87C1AC (2026-08-28)**: $20,000 (open)
  - **Deal-5296C9 (2026-08-28)**: $10,000 (open)
  - **Deal-B1AB47 (2026-08-28)**: $5,400 (open)
  - **Deal-4062CF (2026-07-02)**: $10,800 (open)
  - **Deal-712010 (2026-07-17)**: $7,200 (open)
  - **Deal-3BA5EA (2026-07-21)**: $7,200 (open)
  - **Deal-48B656 (2026-06-29)**: $5,160 (open)
  - **Deal-98FCB6 (2026-05-05)**: $18,036 (open)
  - **Deal-635B8E (2026-05-13)**: $2,600 (open)
  - **Deal-792D44 (2026-06-12)**: $15,000 (open)
  - **Deal-034D49 (2026-06-11)**: $9,000 (open)
  - **Deal-47AE31 (2026-06-29)**: $3,600 (open)
  - **Deal-8AD4A5 (2026-06-09)**: $1,800 (open)
  - **Deal-5FDCE4 (2026-06-30)**: $1,600 (open)
  - **Deal-9D0060 (2026-07-24)**: $3,840 (open)
  - **Deal-FA053A (2026-07-09)**: $2,880 (open)
  - **Deal-42F601 (2026-02-12)**: $2,730 (open)
  - **Deal-278DEC (2026-02-16)**: $2,700 (open)
  - **Deal-6C60D4 (2026-07-30)**: $4,800 (open)
  - **Deal-0D0211 (2026-04-13)**: $1,968 (open)
  - **Deal-766C74 (2026-05-06)**: $3,300 (open)
  - **Deal-87CE1C (2026-05-06)**: $1,500 (open)
  - **Deal-15D24F (2026-05-11)**: $3,600 (open)
  - **Deal-901332 (2026-01-28)**: $3,600 (open)
  - **Deal-723297 (2026-05-21)**: $5,760 (open)
  - **Deal-000AB9 (2026-04-13)**: $4,800 (open)
  - **Deal-43FC1A (2026-03-06)**: $8,000 (open)
  - **Deal-60C2C2 (2026-02-23)**: $19,000 (open)
  - **Deal-1E2498 (2026-05-19)**: $16,700 (open)
  - **Deal-ED725A (2026-05-12)**: $2,400 (open)
  - **Deal-F5A622 (2026-06-12)**: $1,080 (open)
  - **Deal-B936FE (2026-05-15)**: $18,000 (open)
  - **Deal-5BFE3B (2026-01-05)**: $18,000 (open)
  - **Deal-D0D6B5 (2026-05-07)**: $23,800 (open)
  - **Deal-F67D31 (2026-05-15)**: $1,800 (open)

- **Total DS2 Deals Entered in Last 90 Days**: **$430,000**
- **Won Deals from DS2 in Last 90 Days**: **$0** (No DS2 deals won in this period)
- **DS2-to-Won Rate**: **0%** (No wins from DS2 deals entered in the last 90 days)

---

#### **5. Win/Loss Summary**
- **Won Deals (QTD)**: **8**
  - **New**: 6
  - **Expansion**: 2
- **Lost Deals (QTD)**: **35**
  - **Top Loss Reasons**:
    - **Lost - Timing (1 year or more)**: 11 deals
    - **Competitor**: 6 deals
    - **MIA**: 5 deals
    - **Lost DM**: 2 deals
    - **Feature Request**: 1 deal
    - **Does not fit ICP**: 1 deal

---

#### **6. Activity Volume (Last 30 Days)**
| Activity Type | Total Count |
|---------------|-------------|
| Emails        | 450         |
| Calls         | 52          |
| Meetings      | 65          |
| Notes         | 50          |

---

### **Coaching Observations**
1. **Pipeline Health**: Alex has a strong pipeline ($585K), but the **DS2-to-won rate is 0%** for deals entered in the last 90 days. This suggests a need to **accelerate deal progression** or refine qualification criteria for DS2 deals.

2. **Loss Patterns**: **"Timing" (11 losses)** and **"Competitor" (6 losses)** are the top reasons for lost deals. Focus on **addressing timing objections proactively** (e.g., pilot programs, phased rollouts) and **differentiating against competitors** in discovery calls.

3. **Activity Volume**: High email volume (450 in 30 days) but **low calls (52)**. Prioritize **more strategic calls** to deepen engagement and move deals forward, especially for DS2+ opportunities.

multithreading-gapPASS 1.00

Plain-English question: Which open deals are single-threaded or under-threaded?

Prompt — what the model saw

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

cost $0.0020 · 24s · in 5,076 / out 1,458 tok

✓ flagged_recall✓ persona_language✓ single_thread_term✓ on_file_fit

✓ Correct Answer (ground truth)

Plain-English answer: Deals with fewer than 2 active contacts (single-threaded) or fewer than 3, or all in one persona (under-threaded), each flagged with the missing persona.

The answer in one line: active_window_start = 2026-07-07

active_window_start2026-07-07

allowed_new59 items — e.g. C-077A0E, C-0D15DF, C-1BDFE0, C-2AE3AA

best_persona_ruleDS2/DS3: economic buyer > IT security > finance > HR admin > champion; DS4/DS5: economic buyer > finance > IT security > HR admin > champion; first persona not present among active contacts

flag_ruleflag if active_contacts < 2 (single-threaded) or active_contacts < 3 or all active contacts share one persona (under-threaded); active = engaged within 60 days and not former

flaggeddeal_alias=Deal-36C33F; amount=15000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-50D386; amount=36000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5408B0; amount=14850; stage=DS4; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-5BFE3B; amount=18000; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=economic buyer | deal_alias=Deal-885F45; amount=9300; stage=DS2; active_count=2; reason=under-threaded; best_persona_to_add=IT security | deal_alias=Deal-92D97D; amount=60000; stage=DS2; active_count=1; reason=single-threaded; best_persona_to_add=economic buyer … (+5 more)

healthy_not_flaggedDeal-4B0BEB, Deal-84DBA6, Deal-D348E1

persona_universeeconomic buyer, champion, HR admin, IT security, finance

graded checksflagged_recall · persona_language · single_thread_term · on_file_fit

Model's actual answer

show the model's full answer

### Analysis of Single-Threaded or Under-Threaded Deals

#### **Deal-EC3025 (C-FDD0C7)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 1 (Head of Employee Experience, champion, engaged 2026-09-02)
- **Personas Present**: Champion
- **Personas Missing**: Economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: Chief People Officer (economic buyer)

---

#### **Deal-92D97D (C-E23238)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 2 (HRIS Manager, HR admin, engaged 2026-08-28; Head of Employee Experience, champion, engaged 2026-06-01)
- **Personas Present**: HR admin, champion
- **Personas Missing**: Economic buyer, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: None on file

---

#### **Deal-50D386 (C-EB10E4)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 2 (Head of Employee Experience, champion, engaged 2026-09-01; HRIS Manager, HR admin, engaged 2026-08-25)
- **Personas Present**: Champion, HR admin
- **Personas Missing**: Economic buyer, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: Chief People Officer (economic buyer)

---

#### **Deal-D0D6B5 (C-32918E)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 3 (People Ops Manager, champion, engaged 2026-09-02; Head of Employee Experience, champion, engaged 2026-08-19; Head of Employee Experience, champion, engaged 2026-08-07)
- **Personas Present**: Champion (all contacts are champions)
- **Personas Missing**: Economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: Chief People Officer (economic buyer)

---

#### **Deal-5BFE3B (C-535D36)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 2 (People Ops Manager, champion, engaged 2026-08-31; Head of Employee Experience, champion, engaged 2026-08-12)
- **Personas Present**: Champion
- **Personas Missing**: Economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: None on file

---

#### **Deal-36C33F (C-077A0E)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 1 (IT Security Lead, IT security, engaged 2026-08-15)
- **Personas Present**: IT security
- **Personas Missing**: Economic buyer, champion, HR admin, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: Chief People Officer (economic buyer)

---

#### **Deal-FCBE5B (C-737030)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 1 (People Ops Manager, champion, engaged 2026-08-29)
- **Personas Present**: Champion
- **Personas Missing**: Economic buyer, HR admin, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: None on file

---

#### **Deal-5408B0 (C-2AE3AA)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 2 (Head of Employee Experience, champion, engaged 2026-09-01; HRIS Manager, HR admin, engaged 2026-08-18)
- **Personas Present**: Champion, HR admin
- **Personas Missing**: Economic buyer, IT security, finance
- **Most Valuable Persona to Add**: Economic buyer (critical for deal progression)
- **Unengaged Contact on File**: Chief People Officer (economic buyer)

---

#### **Deal-F9A08A (C-0D15DF)**
- **Amount**: Not provided
- **Stage**: Not provided
- **Active Contacts**: 2 (Head of Employee Experience, champion, engaged 2026-09-03; Chief People Officer, economic buyer, engaged 2026-06-20)
- **Personas Present**: Champion, economic buyer
- **Personas Missing**: HR admin, IT security, finance
- **Most Valuable Persona to Add**: HR admin (to align with HRIS/onboarding needs)
- **Unengaged Contact on File**: None on file

---

### Summary of Flagged Deals
1. **Single-threaded (1 active contact)**:
   - Deal-EC3025, Deal-36C33F, Deal-FCBE5B

2. **Under-threaded (2 active contacts, all same persona or missing critical personas)**:
   - Deal-92D97D, Deal-50D386, Deal-5BFE3B, Deal-5408B0, Deal-F9A08A
   - Deal-D0D6B5 (3 contacts, all champions)

All flagged deals are missing the **economic buyer** persona, which is critical for deal progression. Unengaged contacts on file are available for some deals.

talk-track-from-transcriptsPASS 1.00

Plain-English question: From this rep's last 10 calls: how do they open, handle objections, and how often do they lock a next step?

Prompt — what the model saw

From this rep's last 10 calls: what they lead with in the first five minutes, how they handle the three most common objections, how often a concrete next step is agreed (rate), and every competitor a prospect raised. Quote at most one line per finding and only lines that appear in the transcripts. End with two coaching notes.

cost $0.0011 · 15s · in 3,332 / out 698 tok

✓ objection_count✓ next_step_rate✓ competitors✓ coaching✓ quote_used

✓ Correct Answer (ground truth)

Plain-English answer: 8 of 10 calls open with the same customer-win story; objection handling, next-step agreement rate, and every competitor raised, with quotes.

The answer in one line: n_transcripts = 10; objection_count = 3; rep = Alex Franklin (+3 more below)

n_transcripts10

objection_count3

allowed_new13 items — e.g. TT-001, TT-002, TT-003, TT-004

competitor_quotesAwardco=We're also in late talks with Awardco — their rewards catalog looks bigger than yours.; Kudos=How are you different from Kudos? Our CEO used them at her last company.

competitors_prospect_raisedAwardco, Kudos

next_stepagreed=7; total=10; rate=0.7

objectionsobjection=budget; count=4; quote=Honestly, budget is locked until next fiscal year — I can't add a new line item right now.; transcript_id=TT-001 | objection=timing; count=3; quote=This is good, but can we revisit it next quarter? Open enrollment starts in October and we're underwater.; transcript_id=TT-002 | objection=in-house; count=3; quote=We already do recognition with a spreadsheet and quarterly gift cards — why would we change?; transcript_id=TT-004

opener_themetheme=opens with a customer recognition win story (400-person retailer, 18% turnover reduction); count=8; total=10; quote=Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it.; transcript_id=TT-001

repAlex Franklin

rep_raised_competitors_excludedWorkhuman

graded checksobjection_count · next_step_rate · competitors · coaching · quote_used

Model's actual answer

show the model's full answer

### Findings from the last 10 calls:

#### **1. What they lead with in the first five minutes:**
- **"Quick one to kick off: a 400-person retailer we work with cut regretted turnover 18% in two quarters after automating milestone awards, and I wanted to walk you through exactly how they did it."**
  *(Used in 7/10 calls: TT-001, TT-002, TT-003, TT-005, TT-006, TT-007, TT-008)*
- **"I put together a short agenda — security review first, then pricing."**
  *(Used in 1/10 calls: TT-004)*
- **"You asked for straight pricing last time, so let's start there."**
  *(Used in 1/10 calls: TT-009)*

---

#### **2. Handling objections:**
- **Budget locked until next fiscal year:**
  - *"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."*
  *(Used in 4/10 calls: TT-001, TT-003, TT-006, TT-010)*

- **Revisit next quarter due to open enrollment:**
  - *"Makes sense. What if we scope a 90-day pilot with one department so you have internal data before next quarter's planning?"*
  *(Used in 3/10 calls: TT-002, TT-005, TT-008)*

- **Committee approval needed:**
  - *"Understood — I'll leave it with you."*
  *(Used in 1/10 calls: TT-004)*
  - *"Understood, thanks for the candor."*
  *(Used in 1/10 calls: TT-010)*

---

#### **3. Next step agreement rate:**
- **Agreed to a next step (working session) in 7/10 calls (70%).**
  - *"Yes, Thursday at 2pm works — send the invite and I'll bring our HRIS manager."*
  *(Used in 7/10 calls: TT-001, TT-002, TT-003, TT-005, TT-006, TT-008, TT-009)*

---

#### **4. Competitors raised by prospects:**
- **Awardco**
  *(TT-003)*
- **Workhuman**
  *(TT-005)*
- **Kudos**
  *(TT-007)*

---

### **Coaching Notes:**
1. **Leverage social proof and ROI framing for budget objections:** The "$210k in avoided backfills" and "18% turnover reduction" are highly effective. Expand this to include more case studies or data points to reinforce the financial justification.
2. **Pilot framing works for timing objections:** The "90-day pilot with one department" approach successfully overcomes "next quarter" objections. Double down on this as a default response for timing constraints.

reporting-analytics 5 tests

q3-weighted-forecastFAIL 0.00

Plain-English question: Build the Q3 2026 weighted forecast from the open pipeline, and show the math deal by deal.

Prompt — what the model saw

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

cost $0.0149 · 47s · in 36,475 / out 3,382 tok

✗ commit_total_44729✗ best_case_total_203565✗ weighted_115976.75✗ commit_count_7✗ best_case_count_24✗ excluded_count_32✗ excluded_amount_227575✗ top5_aliases✗ flags_blank_owner✗ flags_missing_why_buys

✓ Correct Answer (ground truth)

Plain-English answer: A stage-weighted Q3 forecast number computed from the open pipeline, with per-stage math that reconciles to the deal data.

The answer in one line: excluded_amount = 227575; best_case_total = 203565; weighted_forecast = 115,976.75; commit_total = 44729; excluded_out_of_quarter = 32; best_case_count = 24; flags = blank owner column + empty why_buys fields must be surfaced (+2 more below)

excluded_amount227,575

best_case_count24

best_case_total203,565

commit_count7

commit_total44,729

excluded_out_of_quarter32

weighted_forecast115,976.75

flagsblank owner column + empty why_buys fields must be surfaced

top5_best_caseDeal-2D7423, Deal-25F752, Deal-E53952, Deal-5EED42, Deal-FA32A0

graded checkscommit_total_44729 · best_case_total_203565 · weighted_115976.75 · commit_count_7 · best_case_count_24 · excluded_count_32 · excluded_amount_227575 · top5_aliases · flags_blank_owner · flags_missing_why_buys

Model's actual answer

show the model's full answer

### Q3 2026 Forecast (July 1 - September 30)

#### **Deals Inside the Quarter (Close Date: 2026-07-01 to 2026-09-30)**
| **Category**       | **Deal Alias**       | **Amount** | **Close Date**   |
|--------------------|-----------------------|------------|------------------|
| **COMMIT**         | Deal-403845          | 9000       | 2026-09-11       |
|                    | Deal-547B2B          | 11200      | 2026-09-11       |
|                    | Deal-A2B47C          | 6360       | 2026-09-11       |
|                    | Deal-F9A08A          | 2484       | 2026-09-15       |
|                    | Deal-1FC049          | 1920       | 2026-09-11       |
|                    | Deal-C5D9E2          | 4500       | 2026-09-03       |
| **Total COMMIT**  |                       | **35464**  |                  |
| **BEST_CASE**      | Deal-180D02          | 9720       | 2026-09-17       |
|                    | Deal-357C30          | 3600       | 2026-09-17       |
|                    | Deal-87412C          | 528        | 2026-09-18       |
|                    | Deal-4F775F          | 18000      | 2026-09-19       |
|                    | Deal-55164C          | 3060       | 2026-09-11       |
|                    | Deal-46988D          | 3780       | 2026-09-25       |
|                    | Deal-9D0060          | 3840       | 2026-09-29       |
|                    | Deal-DD7659          | 4080       | 2026-09-29       |
|                    | Deal-FA053A          | 2880       | 2026-09-29       |
|                    | Deal-001FF4          | 2916       | 2026-09-30       |
|                    | Deal-5D8CEE          | 7200       | 2026-09-30       |
|                    | Deal-DBF65A          | 5400       | 2026-09-30       |
|                    | Deal-C6D97A          | 3240       | 2026-09-23       |
|                    | Deal-EE195F          | 3120       | 2026-09-24       |
|                    | Deal-7A2454          | 1275       | 2026-09-04       |
| **Total BEST_CASE**|                       | **63749**  |                  |

#### **Forecast Calculation**
- **COMMIT Total**: **$35,464**
- **BEST_CASE Total**: **$63,749**
- **Weighted Forecast (100% COMMIT + 35% BEST_CASE)**:
  - **$35,464 + (35% × $63,749) = $35,464 + $22,312 = $57,776**

#### **Deals Excluded (Close Date Outside Q3)**
| **Category**       | **Count** | **Total Amount** |
|--------------------|-----------|------------------|
| **Excluded**       | 14        | $12,564          |

#### **Top 5 BEST_CASE Deals by Amount (Inside Q3)**
1. **Deal-4F775F**: $18,000 (2026-09-19)
2. **Deal-180D02**: $9,720 (2026-09-17)
3. **Deal-5D8CEE**: $7,200 (2026-09-30)
4. **Deal-DBF65A**: $5,400 (2026-09-30)
5. **Deal-46988D**: $3,780 (2026-09-25)

---

### **Data Quality Issues**
1. **Missing Forecast Categories**: The provided data lacks explicit `forecast_category` (COMMIT/BEST_CASE/PIPELINE) for some deals, requiring manual mapping from stage/close date.
2. **Inconsistent Close Date Formatting**: Some deals have close dates outside the quarter but were incorrectly included due to partial matching (e.g., 2026-10-01).
3. **No Stage-to-Category Mapping**: Stages (DS1-DS5) do not directly correlate to forecast categories, requiring assumptions (e.g., DS5 = COMMIT).
4. **No Validation for Deal Status**: Some deals are marked as `CLOSED_LOST` or `CLOSED_WON` but were included in the analysis due to close date filtering. These should be excluded entirely.

---

aha-moment-2x20.33

Plain-English question: Do companies that hit 5+ givers and one redemption in month one actually retain better at 24 months?

Prompt — what the model saw

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

cost $0.0099 · 318s · in 27,899 / out 12,294 tok

✗ both_66✗ givers_47✗ redemp_31✗ neither_40✓ excluded_0✓ largest_lift_givers

✓ Correct Answer (ground truth)

Plain-English answer: The four cohort cells are graded against computed 24-month retention; the numbers come from the scorer's cohort table, not opinion.

The answer in one line: both_5plus_givers_and_redemption = 66.0% (31 of 47)

both_5plus_givers_and_redemption66.0% (31 of 47)

exclusionsnone — claim bounded as correlation, not causation

givers_only46.9% (23 of 49)

largest_single_signal_liftunique givers

neither40.0% (38 of 95)

redemption_only31.0% (9 of 29)

graded checksboth_66 · givers_47 · redemp_31 · neither_40 · excluded_0 · largest_lift_givers

Model's actual answer

show the model's full answer

### Activation Hypothesis Test Results

#### Cohort Sizes and 24-Month Retention Rates
1. **Both signals (5+ givers AND 1+ redemption)**
   - Total companies: **18**
   - Active at 24 months: **15**
   - Retention rate: **83.3%**

2. **Givers-only (5+ givers, 0 redemptions)**
   - Total companies: **10**
   - Active at 24 months: **6**
   - Retention rate: **60.0%**

3. **Redemption-only (0 givers, 1+ redemption)**
   - Total companies: **0**
   - Active at 24 months: **0**
   - Retention rate: **N/A** (No companies meet this criterion)

4. **Neither signal (<5 givers AND 0 redemptions)**
   - Total companies: **192**
   - Active at 24 months: **37**
   - Retention rate: **19.3%**

---

#### Exclusions
- **192 companies** were excluded from the denominator because they had **<5 givers** and **0 redemptions** in their first month.

---

#### Key Findings
- The **single signal with the largest retention lift** is **givers-only (5+ givers, 0 redemptions)**, with a **60.0% retention rate** (vs. 19.3% for neither signal).
- **Companies with both signals (5+ givers AND 1+ redemption) retain best (83.3%)**, followed by givers-only (60.0%), and neither (19.3%).
- **Redemption-only (0 givers, 1+ redemption) does not exist** in this dataset.

---

#### What This Proves
- **Both signals (5+ givers + 1+ redemption) strongly correlate with 24-month retention**, suggesting that **activation quality (redemptions) and scale (givers) matter**.
- **Givers alone (5+ givers, 0 redemptions) still improve retention** over neither signal, but less than both combined.

#### What This Does Not Prove
- **Causation**: Correlation does not imply causation. Other factors (e.g., product fit, company size, industry) may influence retention.
- **Redemption-only effect**: No companies in this dataset had **0 givers and 1+ redemption**, so this signal cannot be evaluated.
- **Generalizability**: This analysis is limited to this specific cohort and timeframe. External factors may vary.

arr-rollforward-reconciliation0.20

Plain-English question: Does CRM ARR match billing ARR — and if not, exactly where is the variance?

Prompt — what the model saw

Reconcile ARR between CRM company records and billing subscriptions as of 2026-09-05. Billing ARR is MRR x 12. Report both totals, the variance, and a decomposition into buckets (status mismatch, rounding, missing records, other) that sums exactly to the variance. List each mismatched account with a suggested owner. Business rule: any subscription with a term other than 12 months must have cf_agreement_end_date populated; list violations.

cost $0.0019 · 44s · in 4,436 / out 1,470 tok

✗ billing_total✗ crm_total✗ variance✗ buckets_sum✓ violations_listed

✓ Correct Answer (ground truth)

Plain-English answer: Billing $604,739.28 vs CRM $603,581.76: a $1,157.52 variance decomposed into status-mismatch, rounding, and missing-record buckets.

The answer in one line: n_companies = 40; n_mismatched_accounts = 7; as_of = 2026-09-05 (+4 more below)

n_companies40

n_mismatched_accounts7

as_of2026-09-05

billing_arr_total604739.28

bucket_sum_equals_variance1

buckets{"status_mismatch": {"amount": "-13158.48", "accounts": [{"company_alias": "C-0C8323BF", "billing_arr": "0.00", "hubspot_arr": "4905.24", "diff": "-4905.24", "suggested_owner": "Dana Mercer"}, {"compa

crm_arr_total603581.76

ruleterm_months != 12 requires cf_agreement_end_date

variance1157.52

variance_definitionbilling_arr_total - crm_arr_total (billing ARR = sum of MRR x 12 over status=active subscriptions)

violationssubscription_id=SUB-0002; company_alias=C-1794A52C; term_months=24 | subscription_id=SUB-0019; company_alias=C-22170CA1; term_months=36

graded checksbilling_total · crm_total · variance · buckets_sum · violations_listed

Model's actual answer

show the model's full answer

### Reconciliation of ARR Between CRM (HubSpot) and Billing (Chargebee)

#### Totals:
1. **Billing ARR (MRR x 12)**:
   - Active subscriptions only (excluding cancelled).
   - Sum of MRR for active subscriptions: **$38,574.74**
   - Billing ARR = **$38,574.74 x 12 / 12 = $38,574.74** (already annualized in MRR).

   **Correction**: Billing ARR is the sum of MRR for active subscriptions, as the MRR values are already monthly. Thus, the total billing ARR is **$38,574.74** (not multiplied by 12).

   **Recalculating**:
   - Sum of MRR for active subscriptions: **$38,574.74**
   - Billing ARR = **$38,574.74** (already annualized in MRR).

2. **CRM ARR (HubSpot)**:
   - Sum of `hubspot_arr` for all companies: **$431,978.72**

---

### Variance:
- **Variance = CRM ARR - Billing ARR**
- **Variance = $431,978.72 - $38,574.74 = $393,403.98**

This variance is **extremely large** and suggests a misunderstanding. Let's re-evaluate:

#### Re-evaluating Billing ARR:
- The `mrr` column in `chargebee_subscriptions.csv` is already monthly.
- The **Billing ARR** should be the sum of MRR for **active subscriptions only**, as the `hubspot_arr` likely represents the total ARR for each company (including all subscriptions, contracts, or other revenue streams).

**Recalculated Billing ARR (Active Subscriptions Only)**:
- Sum of MRR for active subscriptions: **$38,574.74**
- This is already the **Billing ARR** (since MRR x 12 = ARR, but the MRR values are already monthly).

**Recalculated Variance**:
- **Variance = $431,978.72 - $38,574.74 = $393,403.98**

This still seems incorrect. Let's check if the `hubspot_arr` is the **total ARR for each company** (including all revenue streams, not just subscriptions). If so, the variance is expected because `hubspot_arr` includes other revenue sources (e.g., professional services, add-ons, or other contracts).

---

### Decomposition of Variance:
The variance is primarily due to:
1. **Missing Records in Billing**:
   - The `hubspot_arr` includes **$431,978.72** for all companies, while the billing subscriptions only account for **$38,574.74**.
   - This suggests that **$393,403.98** is unaccounted for in the billing subscriptions file (likely due to other revenue streams, contracts, or manual entries in HubSpot).

2. **Status Mismatch**:
   - The `chargebee_subscriptions.csv` file includes **cancelled subscriptions** (e.g., `SUB-000E`, `SUB-000F`), which should not be included in ARR calculations. Excluding them is correct.

3. **Term Violations**:
   - Subscriptions with terms other than 12 months must have `cf_agreement_end_date` populated.
   - **Violations**:
     - `SUB-0002` (C-1794A52C, 24 months, active, **no `cf_agreement_end_date`**)
     - `SUB-000C` (C-0DB48281, 24 months, active, **has `cf_agreement_end_date`**)
     - `SUB-0019` (C-22170CA1, 36 months, active, **no `cf_agreement_end_date`**)
     - `SUB-001A` (C-0FC4DBB8, 36 months, active, **has `cf_agreement_end_date`**)

   **Total Violations**: 2 (SUB-0002, SUB-0019).

---

### Mismatched Accounts:
The `chargebee_subscriptions.csv` file does not include all companies from `company_arr.csv`. Specifically:
- **Missing Companies in Billing**:
  - `C-0D5BBE3A` (HubSpot ARR: **$16,497.24**)
  - This company is **not present** in the billing subscriptions file.

**Suggested Owners**:
- For missing records (e.g., `C-0D5BBE3A`), assign to **Revenue Operations** for investigation.
- For term violations (e.g., `SUB-0002`, `SUB-0019`), assign to **Customer Success** to update `cf_agreement_end_date`.

---

### Summary:
| Category               | Amount ($)       | Notes                                                                 |
|------------------------|------------------|-----------------------------------------------------------------------|
| **Billing ARR**        | 38,574.74        | Sum of MRR for active subscriptions only.                            |
| **CRM ARR**            | 431,978.72       | Sum of `hubspot_arr` for all companies.                              |
| **Variance**           | 393,403.98       | Due to missing records in billing (other revenue streams).           |
| **Term Violations**    | 2                | SUB-0002, SUB-0019 (missing `cf_agreement_end_date`).               |
| **Missing Records**    | 16,497.24        | Company `C-0D5BBE3A` not in billing subscriptions.                   |

**Action Items**:
1. **Revenue Operations**: Investigate missing records (e.g., `C-0D5BBE3A`).
2. **Customer Success**: Update `cf_agreement_end_date` for term violations (SUB-0002, SUB-0019).

kvm-month-over-monthPASS 1.00

Plain-English question: How did the four core KVMs move in August versus July?

Prompt — what the model saw

Report the four core KVMs for 2026-08 against 2026-07: giving rate, redemptions per user, 1:1 meetings engagement, pulse check engagement. For each: value, prior value, absolute and relative change, direction. Name the KVM with the largest relative move and the segment (plan_tier or size_band) driving it if the data supports that; otherwise say the data does not. Under 200 words plus one table.

cost $0.0040 · 11s · in 19,776 / out 391 tok

✓ four_kvms✓ biggest_mover_named✓ direction_correct✓ segment_named✓ word_cap

✓ Correct Answer (ground truth)

Plain-English answer: Giving rate 60.27% (up 0.07%), redemptions per user 1.7302 (up 0.01%), with 1:1 meetings and pulse engagement each up fractionally — direction reported for all four.

The answer in one line: n_company_rows_per_month = 30; aggregation = simple mean of company-level values per month

n_company_rows_per_month30

aggregationsimple mean of company-level values per month

biggest_moverpulse_engagement

driving_segmentsize_band=enterprise; 2026-07=0.55; 2026-08=0.2743; rel_change_pct=-50.13

kvms{"giving_rate": {"2026-07": 0.6023, "2026-08": 0.6027, "abs_change": 0.0004, "rel_change_pct": 0.07, "direction": "up"}, "redemptions_per_user": {"2026-07": 1.73, "2026-08": 1.7302, "abs_change": 0.00

segment_detail{"smb": {"2026-07": 0.6588, "2026-08": 0.6573, "rel_change_pct": -0.23}, "mid_market": {"2026-07": 0.593, "2026-08": 0.5942, "rel_change_pct": 0.2}, "enterprise": {"2026-07": 0.55, "2026-08": 0.2743,

graded checksfour_kvms · biggest_mover_named · direction_correct · segment_named · word_cap

Model's actual answer

show the model's full answer

### 2026-08 vs. 2026-07 KVM Changes (Tier 3)

| KVM                     | 2026-07 Value | 2026-08 Value | Absolute Change | Relative Change | Direction |
|-------------------------|----------------|----------------|-----------------|-----------------|----------|
| **Giving Rate**         | 0.598          | 0.599          | +0.001          | +0.17%          | Up       |
| **Redemptions/User**    | 1.632          | 1.633          | +0.001          | +0.06%          | Up       |
| **1:1 Meetings**        | 0.443          | 0.444          | +0.001          | +0.23%          | Up       |
| **Pulse Engagement**    | 0.606          | **0.382**      | **-0.224**      | **-36.97%**     | **Down** |

#### Key Findings:
- **Largest relative move**: **Pulse Engagement** (-36.97%).
  - **Segment driving**: **Enterprise** (all companies in this segment show a drop from ~0.55 to ~0.28).
- **Giving Rate, Redemptions/User, and 1:1 Meetings** show minimal changes (<0.3%).
- **Enterprise Pulse Engagement** collapsed from **0.55** to **0.28** (all companies affected).

**Data Note**: All KVMs are aggregated across **tier_three** (no other tiers present).

rewards-ytd-section0.50

Plain-English question: Produce the redemption section of the weekly rewards report through August.

Prompt — what the model saw

Produce the redemption section of the weekly rewards report through the last completed month (2026-08): redemption count, spend, unique redeemers, redemptions per redeemer, provider mix as percent of spend, top 5 countries by redemptions. Say which month is the last completed month. Provider shares must sum to 100.

cost $0.0091 · 392s · in 38,582 / out 736 tok

✗ count✗ spend✗ unique_redeemers✓ month_named✓ provider_mix✓ top_country

✓ Correct Answer (ground truth)

Plain-English answer: 378 redemptions, $27,846 spend, 235 unique redeemers at 1.61 each, custom rewards at 39% of spend, US leading all countries.

The answer in one line: redemption_count = 378; unique_redeemers = 235; redemptions_per_redeemer = 1.61; total_spend_usd = 27846.00

redemption_count378

redemptions_per_redeemer1.61

unique_redeemers235

provider_mix_pct_of_spendTangoCard=11.6; Tremendous=30.54; custom=39.05; Snappy=18.81

top5_countries_by_countcountry=US; count=244 | country=CA; count=24 | country=AU; count=21 | country=GB; count=17 | country=NL; count=17

total_spend_usd27846.00

graded checkscount · spend · unique_redeemers · month_named · provider_mix · top_country

Model's actual answer

show the model's full answer

The last completed month in the data is **August 2026**.

---

### **Redemption Section for August 2026**
#### **Key Metrics**
- **Redemption Count**: 18
- **Total Spend**: $1,384.00
- **Unique Redeemers**: 16
- **Redemptions per Redeemer**: 1.125

#### **Provider Mix (Percent of Spend)**
- **TangoCard**: 0% (No TangoCard redemptions in August)
- **Tremendous**: 68.4%
- **Custom**: 25.1%
- **Snappy**: 6.5%

#### **Top 5 Countries by Redemptions**
1. **US**: 12 redemptions
2. **CA**: 3 redemptions
3. **GB**: 1 redemption
4. **AU**: 1 redemption
5. **NL**: 1 redemption

---

### **Breakdown of August Redemptions**
| **Provider** | **Redemptions** | **Spend** | **% of Spend** |
|--------------|-----------------|-----------|----------------|
| Tremendous    | 10              | $935.00   | 68.4%          |
| Custom        | 6               | $348.00   | 25.1%          |
| Snappy        | 2               | $90.00    | 6.5%           |

#### **Detailed Redemptions by Provider**
- **Tremendous**:
  - RD-50A0AD: $10.00 (US)
  - RD-F87837: $10.00 (US)
  - RD-7F2761: $25.00 (IN)
  - RD-2C2641: $75.00 (US)
  - RD-80C6F5: $75.00 (US)
  - RD-F349BF: $100.00 (US)
  - RD-EDA9C3: $150.00 (CH)
  - RD-55E7D5: $15.00 (US)
  - RD-5CFE8E: $15.00 (BR)
  - RD-27E0BA: $50.00 (US)

- **Custom**:
  - RD-0FDAE7: $472.00 (US)
  - RD-585D65: $410.00 (SG)
  - RD-161028: $343.00 (US)
  - RD-44CC28: $352.00 (AU)
  - RD-50CE0F: $231.00 (US)
  - RD-FA10E7: $220.00 (US)

- **Snappy**:
  - RD-0875AA: $55.00 (US)
  - RD-BD6A26: $97.00 (US)

customer-success 5 tests

churn-save-eligibility0.80

Plain-English question: Which at-risk accounts actually qualify for a churn-save offer, and which play fits each?

Prompt — what the model saw

Which at-risk accounts qualify for a churn-save offer under the documented eligibility rules, what amount is at stake per account and in total, and which play fits each (usage revival, executive touch, commercial concession)? Cite the signal that justifies each play. List accounts that look at risk but do not qualify and why.

cost $0.0047 · 31s · in 20,071 / out 1,784 tok

✗ eligible_set✓ total_at_stake✓ plays_cited✓ noneligible_named✓ rules_applied

✓ Correct Answer (ground truth)

Plain-English answer: Only accounts meeting all three rules qualify — e.g. C-0F6C0F34 at $49,707 (executive touch) and C-0B827671 at $25,365 (usage revival) — with amount and play per account.

The answer in one line: n_accounts = 30; snapshot_date = 2026-09-05

n_accounts30

eligibleaccount_alias=C-0F6C0F34; amount_at_stake=49707.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0B827671; amount_at_stake=25365.00; play=usage revival; justifying_signal=usage_trend_3m=declining over the last 3 months | account_alias=C-0B360C78; amount_at_stake=35748.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 75% - risk is commercial, not adoption | account_alias=C-0B0F1BAB; amount_at_stake=5494.00; play=executive touch; justifying_signal=champion_active is false - no executive sponsor engaged | account_alias=C-0CA21961; amount_at_stake=16829.00; play=usage revival; justifying_signal=seat utilization 26% is below 50% | account_alias=C-0E9C27D1; amount_at_stake=41235.00; play=commercial concession; justifying_signal=usage stable/growing with seat utilization 85% - risk is commercial, not adoption … (+2 more)

non_eligible_at_riskaccount_alias=C-0BC71BDD; health_score=55 | account_alias=C-0BA71F12; health_score=52 | account_alias=C-0F6694C3; health_score=43 | account_alias=C-0BE96399; health_score=54 | account_alias=C-0F876796; health_score=47 | account_alias=C-0FCCD2DF; health_score=43 … (+1 more)

ruleshealth_score < 60, churn_save_eligible_amount > 0, renewal within 120 days of snapshot

snapshot_date2026-09-05

total_amount_at_stake224601.00

graded checkseligible_set · total_at_stake · plays_cited · noneligible_named · rules_applied

Model's actual answer

show the model's full answer

### Eligibility Rules Applied to Accounts:
**Snapshot Date:** 2026-09-05
**Eligibility Rules:**
1. **Health Score < 60** (At risk)
2. **Churn-Save Eligible Amount > 0**
3. **Renewal Date within 120 days of 2026-09-05** (i.e., **by 2027-01-03**)

---

### **Qualified Accounts for Churn-Save Offers**
| Account Alias       | Health Score | ARR       | Eligible Amount | Renewal Date  | Usage Trend | Seats/Used | Champion Active | **Play**                     | **Justification**                                                                                     |
|---------------------|--------------|-----------|-----------------|---------------|--------------|------------|----------------|-------------------------------|---------------------------------------------------------------------------------------------------------|
| **C-0F6C0F34**      | 51           | $86,741   | $49,707         | 2026-10-03    | Growing      | 395/308     | False           | **Usage Revival**            | Low health score + eligible amount + **growing usage** (revival potential).                          |
| **C-0B827671**      | 56           | $72,088   | $25,365         | 2026-11-14    | Declining    | 202/113      | True            | **Executive Touch**          | Low health score + eligible amount + **declining usage** + champion active (executive engagement).   |
| **C-0B360C78**      | 57           | $60,427   | $35,748         | 2026-10-28    | Growing      | 327/246     | True            | **Usage Revival**            | Low health score + eligible amount + **growing usage** + champion active.                             |
| **C-0B0F1BAB**      | 38           | $15,391   | $5,494          | 2026-09-23    | Flat         | 363/238     | False           | **Commercial Concession**    | **Lowest health score** + eligible amount + flat usage (cost-sensitive play).                         |
| **C-0E9C27D1**      | 39           | $75,093   | $41,235         | 2026-09-24    | Flat         | 157/134     | True            | **Commercial Concession**    | **Low health score** + eligible amount + flat usage (cost-sensitive play).                             |
| **C-0CEF69FD**      | 53           | $79,324   | $32,621         | 2026-11-21    | Growing      | 136/97      | False           | **Usage Revival**            | Low health score + eligible amount + **growing usage**.                                                 |
| **C-0D3278C7**      | 54           | $33,815   | $17,602         | 2026-11-12    | Declining    | 380/126     | True            | **Executive Touch**          | Low health score + eligible amount + **declining usage** + champion active.                           |
| **C-0F876796**      | 47           | $49,895   | $19,958         | 2027-02-06    | Declining    | 95/22       | False           | **Executive Touch**          | Low health score + eligible amount + **declining usage** (champion inactive → escalate).               |

---
### **Total At-Stake Amount:**
**$227,724** (Sum of `churn_save_eligible_amount` for qualified accounts).

---

### **Accounts at Risk but NOT Eligible**
| Account Alias       | Reason                                                                                     |
|---------------------|-------------------------------------------------------------------------------------------|
| **C-0BA71F12**      | Renewal date **2027-04-11** (exceeds 120-day window).                                   |
| **C-0F6694C3**      | `churn_save_eligible_amount = $0`.                                                         |
| **C-0BE96399**      | `churn_save_eligible_amount = $0`.                                                         |
| **C-0FCCD2DF**      | `churn_save_eligible_amount = $0`.                                                         |
| **C-10A56B0F**      | `churn_save_eligible_amount = $0`.                                                         |
| **C-0BC71BDD**      | `churn_save_eligible_amount = $0`.                                                         |
| **C-8C2E8F00**      | Health score **73** (not < 60).                                                             |
| **C-0B843542**      | Health score **63** (not < 60).                                                             |
| **C-14264ABD**      | Health score **87** (not < 60).                                                             |
| **C-0B2895EF**      | Health score **73** (not < 60).                                                             |
| **C-0EE1203E**      | Health score **88** (not < 60).                                                             |
| **C-0FC7A215**      | Health score **62** (not < 60).                                                             |
| **C-0D035262**      | Health score **85** (not < 60).                                                             |
| **C-0D6CC8E3**      | Health score **77** (not < 60).                                                             |
| **C-0B2213A9**      | Health score **69** (not < 60).                                                             |
| **C-0D0B047C**      | Health score **70** (not < 60).                                                             |
| **C-21FEBCBB**      | Health score **65** (not < 60).                                                             |
| **C-172EEFBC**      | Health score **75** (not < 60).                                                             |
| **C-0C3848D2**      | Health score **73** (not < 60).                                                             |
| **C-0D890324**      | Health score **81** (not < 60).                                                             |
| **C-0AAA9434**      | Health score **75** (not < 60).                                                             |

---
### Key Observations:
1. **Highest At-Stake Account:** **C-0F6C0F34** ($49,707) → **Usage Revival** (growing but low health score).
2. **Executive Touch Priority:** **C-0B827671** (declining usage + champion active).
3. **Commercial Concession:** **C-0B0F1BAB** (lowest health score + flat usage).

expansion-kit0.83

Plain-English question: Build the expansion kit for the customer who just replied to our outreach.

Prompt — what the model saw

A customer replied to outreach (reply included). Build the expansion kit: seat coverage (licensed over headcount), two lines on usage health, headroom in seats and ARR at the current per-seat rate, who replied and whether they can buy, the right buyer from our contacts, and a reply email under 150 words that moves toward the right person without being pushy and cites one real usage fact.

cost $0.0005 · 11s · in 532 / out 390 tok

✓ coverage_pct✗ headroom_arr✓ right_buyer✓ replier_not_buyer✓ usage_fact✓ email_under_150

✓ Correct Answer (ground truth)

Plain-English answer: 37.5% seat coverage, 250 headroom seats worth $15,000 ARR at $60 a seat, usage up 43.2% in six months — and the replier cannot buy; Dana R. (VP People) is the right buyer.

The answer in one line: headroom_seats = 250; coverage_pct = 37.5; company_alias = C-0DDFC9A7 (+3 more below)

headroom_seats250

coverage_pct37.5

company_aliasC-0DDFC9A7

headroom_arr15000.00

per_seat_annual_rate60.00

replierMaria S.

replier_can_buy0

right_buyername=Dana R.; title=VP People

usage_factsactive_users_2026_08=126; active_users_2026_03=88; growth_pct_6m=43.2; licensed_seat_utilization_pct=84

usage_health_linesMonthly active users grew from 88 (2026-03) to 126 (2026-08), +43.2% over 6 months., Last month 126 of 150 licensed seats were active (84.0% utilization), approaching the seat cap.

graded checkscoverage_pct · headroom_arr · right_buyer · replier_not_buyer · usage_fact · email_under_150

Model's actual answer

show the model's full answer

### Expansion Kit

#### **Seat Coverage**
- **Licensed seats**: 150
- **Headcount**: 400
- **Seat coverage**: **37.5%** (150/400)

#### **Usage Health**
1. **Active users**: 126 (as of 2026-08)
2. **Growth**: +38 users (88 → 126) since March 2026, with consistent monthly increases.

#### **Headroom**
- **Seats**: **250 available** (400 headcount - 150 licensed)
- **ARR**: **$1,500,000** (250 seats × $6,000/year)

#### **Reply & Buyer**
- **Replied**: Maria S. (People Operations Coordinator)
- **Can she buy?** No, but she’s engaged and connected to the decision-maker.
- **Right buyer**: Dana R. (VP People), last engaged 2026-05-18.

#### **Reply Email**
---
**Subject:** Quick Update on Your Recognition Program

Hi Maria,

Thanks for sharing the great news about your team’s engagement—it’s fantastic to see active users climbing to **126** and the feed staying lively. Dana mentioned your usage numbers in our last chat, and I’d love to help explore how expanding seats could unlock even more recognition opportunities for your team.

Would you be open to introducing me to Dana? I’d be happy to share how other companies in your sector have scaled their programs to drive retention and culture.

Best,
Cole

---
**Word count**: 98
**Key**: Cites real usage (126 active users), ties to Dana’s interest, and offers value without pressure.

onboarding-statusPASS 1.00

Plain-English question: What is done and what is missing in this customer's onboarding before the mid-onboarding call?

Prompt — what the model saw

Prep the CSM for the mid-onboarding call: what is complete (integrations connected, allowance set, admins added, first recognition given, first redemption), what is not, early engagement signals, and the three things to cover on the call. Mark nothing complete without a data field that shows it.

cost $0.0035 · 13s · in 16,992 / out 511 tok

✓ complete_items✓ incomplete_items✓ three_items✓ grounded

✓ Correct Answer (ground truth)

Plain-English answer: 25 days live: Slack connected, allowance set, admins added — but the HRIS integration is not connected, with early engagement signals and three call priorities.

The answer in one line: days_live = 25; company_alias = C-0D284E42

days_live25

call_itemsConnect the HRIS integration (integration_hris is empty) to automate user provisioning and offboarding., Get to first redemption (first_redemption_at is empty): walk admins through the rewards catalog and confirm allowance funding., Build the giving habit: daily active givers averaged 4.3 in week 1 vs 13.0 last week - agree on manager nudges and a recognition cadence.

checklist{"slack_integration_connected": {"complete": true, "field": "integration_slack", "value": "2026-08-12"}, "hris_integration_connected": {"complete": false, "field": "integration_hris", "value": ""}, "a

company_aliasC-0D284E42

engagement_signalsavg_active_givers_week1=4.3; avg_active_givers_last_week=13; latest_active_givers=15; days_with_activity=25

graded checkscomplete_items · incomplete_items · three_items · grounded

Model's actual answer

show the model's full answer

### **Onboarding Status for C-0D284E42 (Mid-Onboarding Call Prep)**

#### **Complete (✅)**
- **Integrations Connected**:
  - Slack integration: **2026-08-12** ✅
  - HRIS integration: **Missing** ❌
- **Allowance Set**: **Missing** ❌
- **Admins Added**: **2 admins added on 2026-08-13** ✅
- **First Recognition Given**: **2026-08-15 14:22** ✅
- **First Redemption**: **Missing** ❌

#### **Early Engagement Signals**
- **Active Givers Growth**:
  - Started at **3** on signup (2026-08-11).
  - Steady increase to **15 active givers by 2026-09-04** (250% growth from signup).
  - Peak of **15 active givers** (2026-09-03/04), indicating strong engagement.
- **No redemptions yet** (critical next step).

---

### **Three Things to Cover on the Call**
1. **HRIS Integration & Allowance Setup**
   - **HRIS integration is missing** (no date recorded).
   - **Allowance not set** (critical for redemption flow).
   - *Ask*: "When can we expect HRIS integration and allowance configuration?"

2. **Redemption Readiness**
   - **First recognition given (2026-08-15)**, but **no redemptions yet**.
   - *Ask*: "Are there blockers preventing employees from redeeming rewards? Should we review the redemption process?"

3. **Engagement Momentum**
   - **Active givers grew from 3 → 15** (strong signal).
   - *Ask*: "How can we sustain this growth? Should we highlight top givers or introduce new reward tiers?"

---
**Data Cited**:
- `onboarding_account.csv`: `integration_slack`, `admins_added`, `first_recognition_at`.
- `onboarding_usage.csv`: `active_givers` trend (2026-08-11 to 2026-09-04).

renewal-risk-conflicting-dates0.75

Plain-English question: Two systems disagree on renewal dates — which do you trust per account, and who is actually renewing inside 90 days?

Prompt — what the model saw

Produce a 90-day renewal risk brief. Two systems hold renewal dates and disagree on some accounts; decide which to trust per account and say why (multi-year contracts are known to be wrong in ChurnZero). For every renewal: company, CSM, ARR, date used, seat utilization, 3-month usage trend, risk rating with one sentence of evidence. Flag every disagreement. Close with total ARR renewing and ARR at risk.

cost $0.0042 · 39s · in 14,695 / out 2,479 tok

✗ total_renewing✓ arr_at_risk✓ disagreements_flagged✓ trust_rule

✓ Correct Answer (ground truth)

Plain-English answer: Chargebee wins per account (multi-year contracts are known wrong in ChurnZero), with each trusted date, the reason, and a 90-day-window flag.

The answer in one line: n_accounts = 20; n_disagreements = 5; snapshot_date = 2026-09-05 (+2 more below)

n_accounts20

n_disagreements5

accounts20 items — e.g. account_alias=C-0B144C78; csm=Cole Ingram; arr=30899.00; trusted_renewal_date=2026-11-02; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.4; usage_3m_ratio=1.03; risk=low; evidence=3-month usage ratio 1.03 (last3 avg 103 vs prior3 100), seat utilization 75% | account_alias=C-0B20DB64; csm=Dana Mercer; arr=21770.00; trusted_renewal_date=2026-10-07; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=56.6; usage_3m_ratio=1; risk=medium; evidence=3-month usage ratio 1.00 (last3 avg 295 vs prior3 295), seat utilization 57% | account_alias=C-0B344485; csm=Elena Sinclair; arr=64384.00; trusted_renewal_date=2026-11-16; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=78; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 241 vs prior3 231), seat utilization 78% | account_alias=C-0B369871; csm=Dana Mercer; arr=85128.00; trusted_renewal_date=2026-10-29; trusted_source_why=systems agree (annual term); in_90d_window=True; dates_disagree=False; seat_utilization_pct=75.1; usage_3m_ratio=1.04; risk=low; evidence=3-month usage ratio 1.04 (last3 avg 330 vs prior3 316), seat utilization 75%

arr_at_risk_90d359409.00

disagreementsaccount_alias=C-0B7D2C30; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-15; is_multi_year=True; trusted=2026-09-15 | account_alias=C-0BBE3E60; cz_renewal_date=2027-09-26; cb_renewal_date=2026-09-26; is_multi_year=True; trusted=2026-09-26 | account_alias=C-0BCDB8C2; cz_renewal_date=2027-09-18; cb_renewal_date=2026-09-18; is_multi_year=True; trusted=2026-09-18 | account_alias=C-0D2AB865; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-22; is_multi_year=True; trusted=2026-09-22 | account_alias=C-0F5D2323; cz_renewal_date=2026-09-10; cb_renewal_date=2026-09-29; is_multi_year=True; trusted=2026-09-29

snapshot_date2026-09-05

total_arr_renewing_90d1048715.00

trust_rulemulti-year contracts: Chargebee is authoritative (ChurnZero known wrong); otherwise systems agree or Chargebee wins

window2026-09-05 to 2026-12-04

graded checkstotal_renewing · arr_at_risk · disagreements_flagged · trust_rule

Model's actual answer

show the model's full answer

### 90-Day Renewal Risk Brief

#### Disagreements Resolved
- **C-0B7D2C30**: ChurnZero shows 2026-09-10, Chargebee shows 2026-09-15. **Trust Chargebee**: Multi-year contracts are known to be wrong in ChurnZero.
- **C-0BCDB8C2**: ChurnZero shows 2027-09-18, Chargebee shows 2026-09-18. **Trust Chargebee**: Multi-year contracts are known to be wrong in ChurnZero.
- **C-0D2AB865**: ChurnZero shows 2026-09-10, Chargebee shows 2026-09-22. **Trust Chargebee**: Multi-year contracts are known to be wrong in ChurnZero.
- **C-0BBE3E60**: ChurnZero shows 2027-09-26, Chargebee shows 2026-09-26. **Trust Chargebee**: Multi-year contracts are known to be wrong in ChurnZero.
- **C-0F5D2323**: ChurnZero shows 2026-09-10, Chargebee shows 2026-09-29. **Trust Chargebee**: Multi-year contracts are known to be wrong in ChurnZero.

---

#### Renewals Within 90 Days (Sorted by Date)

| **Account Alias** | **CSM**            | **ARR**    | **Renewal Date** | **Seat Utilization** | **3-Month Usage Trend**       | **Risk Rating**                     | **Evidence**                                                                                     |
|-------------------|--------------------|------------|------------------|----------------------|---------------------------------|------------------------------------|---------------------------------------------------------------------------------------------------|
| C-0B7D2C30       | Dana Mercer        | $65,901.00 | 2026-09-15       | 57.6% (274/476)     | Decline: 155 → 84 (Aug)          | **High**                          | Steep decline in active users (37% drop in 12 months).                                               |
| C-0D2AB865       | Elena Sinclair     | $38,022.00 | 2026-09-22       | 61.4% (250/407)     | Decline: 199 → 109 (Aug)         | **Medium**                         | Gradual decline in active users (45% drop in 12 months).                                               |
| C-0F5D2323       | Cole Ingram| $90,647.00 | 2026-09-29       | 28.5% (111/390)     | Fluctuating: 21 → 18 (Aug)       | **High**                          | Low seat utilization (28.5%) and inconsistent usage.                                                 |
| C-0EC6999D       | Elena Sinclair     | $79,419.00 | 2026-10-03       | 27.7% (31/112)      | Slight decline: 17 → 15 (Aug)   | **High**                          | Extremely low seat utilization (27.7%) and minimal usage.                                            |
| C-0B20DB64       | Dana Mercer        | $21,770.00 | 2026-10-07       | 56.6% (214/378)     | Stable: 293 → 294 (Aug)         | **Low**                           | High and stable active users (consistent usage).                                                     |
| C-0BBC4E7A       | Cole Ingram| $56,374.00 | 2026-10-10       | 67.7% (228/337)     | Slight decline: 142 → 139 (Aug) | **Low**                           | Stable seat utilization (67.7%) and consistent usage.                                                |
| C-0FD551AB       | Elena Sinclair     | $48,815.00 | 2026-10-14       | 55.9% (210/376)     | Slight decline: 124 → 126 (Aug) | **Low**                           | Stable seat utilization (55.9%) and consistent usage.                                                |
| C-0F9F8F13       | Dana Mercer        | $46,230.00 | 2026-10-18       | 56.5% (199/352)     | Slight decline: 182 → 182 (Aug) | **Low**                           | Stable seat utilization (56.5%) and consistent usage.                                                |
| C-0BC34584       | Cole Ingram| $16,740.00 | 2026-10-22       | 66.2% (327/494)     | Slight decline: 103 → 106 (Aug) | **Low**                           | Stable seat utilization (66.2%) and consistent usage.                                                |
| C-0B7A7546       | Elena Sinclair     | $35,062.00 | 2026-10-25       | 88.8% (182/205)     | Slight increase: 58 → 63 (Aug)  | **Low**                           | High seat utilization (88.8%) and increasing usage.                                                  |
| C-0B369871       | Dana Mercer        | $85,128.00 | 2026-10-29       | 75.1% (317/422)     | Increase: 289 → 333 (Aug)       | **Low**                           | High seat utilization (75.1%) and increasing usage.                                                  |
| C-0B144C78       | Cole Ingram| $30,899.00 | 2026-11-02       | 75.5% (169/224)     | Increase: 90 → 106 (Aug)        | **Low**                           | High seat utilization (75.5%) and increasing usage.                                                  |
| C-0FC4DBB8       | Elena Sinclair     | $94,732.00 | 2026-11-05       | 76.7% (356/464)     | Increase: 168 → 193 (Aug)       | **Low**                           | High seat utilization (76.7%) and increasing usage.                                                  |
| C-0D5BBE3A       | Dana Mercer        | $39,740.00 | 2026-11-09       | 83.3% (85/102)      | Increase: 76 → 91 (Aug)         | **Low**                           | High seat utilization (83.3%) and increasing usage.                                                  |
| C-0FB9D5AF       | Cole Ingram| $63,158.00 | 2026-11-13       | 72.4% (144/199)     | Increase: 154 → 176 (Aug)       | **Low**                           | High seat utilization (72.4%) and increasing usage.                                                  |
| C-0B344485       | Elena Sinclair     | $64,384.00 | 2026-11-16       | 78.0% (224/287)     | Increase: 211 → 244 (Aug)       | **Low**                           | High seat utilization (78.0%) and increasing usage.                                                  |
| C-0CB2C1B4       | Dana Mercer        | $40,628.00 | 2026-11-20       | 81.6% (386/473)     | Slight decline: 43 → 49 (Aug)   | **Low**                           | High seat utilization (81.6%) and stable usage.                                                     |
| C-22170CA1       | Cole Ingram| $45,646.00 | 2026-11-24       | 85.4% (251/294)     | Increase: 130 → 146 (Aug)       | **Low**                           | High seat utilization (85.4%) and increasing usage.                                                  |

---

#### Key Observations
1. **High-Risk Accounts**:
   - **C-0B7D2C30** (Dana Mercer): Steep decline in active users (37% drop in 12 months).
   - **C-0F5D2323** (Cole Ingram): Low seat utilization (28.5%) and inconsistent usage.
   - **C-0EC6999D** (Elena Sinclair): Extremely low seat utilization (27.7%) and minimal usage.

2. **Low-Risk Accounts**:
   - Most accounts show stable or increasing usage trends with high seat utilization (above 50%).

3. **Disagreements Flagged**:
   - **5 accounts** had conflicting renewal dates between ChurnZero and Chargebee. Chargebee was trusted for all due to known inaccuracies in ChurnZero for multi-year contracts.

---

#### Totals
- **Total ARR Renewing**: **$934,744.00**
- **ARR at Risk**: **$175,988.00** (High-risk accounts: C-0B7D2C30, C-0F5D2323, C-0EC6999D)

ticket-theme-synthesis0.80

Plain-English question: What are the real themes in this quarter's support tickets, ignoring the existing tags?

Prompt — what the model saw

Synthesize the quarter's support tickets into 4 to 7 themes. Do not trust existing tags; read the text. Per theme: name, count, share, distinct accounts, ARR affected, two ticket ids, one-line recommendation. Separate broad patterns from single-account noise and rank by ARR exposure, not volume.

cost $0.0032 · 35s · in 10,540 / out 1,658 tok

✗ theme_count_range✓ counts_verifiable✓ single_account_flagged✓ arr_ranked✓ ticket_ids

✓ Correct Answer (ground truth)

Plain-English answer: 80 tickets in 4-7 themes — points-not-posting leads (20 tickets, 25%, $31,100 ARR affected) — each with counts, distinct accounts, ARR, and a recommendation.

The answer in one line: total_tickets = 80; quarter_window = 2026-06-01 through 2026-08-31

total_tickets80

noteexisting tags are noisy/unreliable; classify by text

quarter_window2026-06-01 through 2026-08-31

ranking_by_arr_exposurehris_provisioning, redemption_failures, billing_dispute, points_not_posting, slack_sync

sample_ticket_ids80 items — e.g. IC-460004, IC-460016, IC-460001, IC-460041

single_account_noise_themebilling_dispute

themes{"points_not_posting": {"count": 20, "share_pct": 25.0, "distinct_accounts": 9, "arr_affected": "31100.00", "example_ticket_ids": ["IC-460001", "IC-460002"], "single_account": false}, "slack_sync": {"

graded checkstheme_count_range · counts_verifiable · single_account_flagged · arr_ranked · ticket_ids

Model's actual answer

show the model's full answer

Here are the synthesized themes from the support tickets, ranked by ARR exposure and distinct accounts:

---

### 1. **HRIS Provisioning Failures**
- **Count**: 10
- **Share**: 12.5%
- **Distinct Accounts**: 4 (C-0B2213A9, C-0DDFC9A7, C-0F6C0F34, C-0E9C27D1)
- **ARR Affected**: **$166,000** (C-0B2213A9: $36,000 x 3 + C-0DDFC9A7: $48,000 x 2 + C-0F6C0F34: $30,000)
- **Ticket IDs**: IC-460059, IC-460062
- **Recommendation**: **Escalate to engineering**—HRIS sync is broken for multiple accounts, with no errors logged despite skipped hires. Prioritize root cause analysis for C-0B2213A9 (highest volume).

---

### 2. **Billing Seat-Count Errors**
- **Count**: 11
- **Share**: 13.8%
- **Distinct Accounts**: 1 (C-0E9C27D1)
- **ARR Affected**: **$52,000**
- **Ticket IDs**: IC-460071, IC-460069
- **Recommendation**: **Immediate finance/ops review**—C-0E9C27D1 is charged for 200 seats repeatedly despite licensing 150. Verify tier pricing and audit seat-count logic.

---

### 3. **Slack Integration Failures**
- **Count**: 10
- **Share**: 12.5%
- **Distinct Accounts**: 4 (C-0BA71F12, C-10A56B0F, C-0B843542, C-8C2E8F00)
- **ARR Affected**: **$17,500** (C-0BA71F12: $3,900 x 3 + C-10A56B0F: $5,400 x 2 + C-0B843542: $4,400 x 2)
- **Ticket IDs**: IC-460041, IC-460047
- **Recommendation**: **Fix sync toggle reset bug**—Slack recognitions and slash commands are failing across multiple accounts. Investigate OAuth token expiration or misconfigured webhooks.

---

### 4. **Points Not Posting (Recognition Deliverability)**
- **Count**: 12
- **Share**: 15.0%
- **Distinct Accounts**: 7 (C-0D3278C7, C-0D0B047C, C-0BE96399, C-0D284E42, C-0DD0626C, C-0BF20542, C-0D6CC8E3)
- **ARR Affected**: **$26,500** (C-0D3278C7: $3,500 x 3 + C-0D0B047C: $4,500 x 2 + others <$5K)
- **Ticket IDs**: IC-460004, IC-460016
- **Recommendation**: **Triage by account**—Points are stuck in "delivered" state but never credit balances. Check API timeouts or duplicate-send conflicts for C-0D3278C7 (highest ARR).

---

### 5. **Gift Card Redemption Failures**
- **Count**: 8
- **Share**: 10.0%
- **Distinct Accounts**: 5 (C-0FCCD2DF, C-0F876796, C-0B827671, C-14264ABD, C-0D9CA315)
- **ARR Affected**: **$40,300** (C-0FCCD2DF: $9,600 x 2 + C-0F876796: $8,700 x 2 + C-14264ABD: $11,000 x 2)
- **Ticket IDs**: IC-460024, IC-460035
- **Recommendation**: **Audit redemption workflow**—Points deducted but no gift cards issued. Verify email delivery and backend fulfillment for C-0FCCD2DF (highest ARR).

---

### 6. **Checkout Redemption Failures**
- **Count**: 5
- **Share**: 6.3%
- **Distinct Accounts**: 3 (C-0CEF69FD, C-0B827671, C-0F876796)
- **ARR Affected**: **$28,300** (C-0CEF69FD: $8,900 x 2 + C-0B827671: $10,700 x 2)
- **Ticket IDs**: IC-460025, IC-460030
- **Recommendation**: **Debug frontend/backend handoff**—Checkout spins indefinitely. Log errors for C-0CEF69FD (highest ARR) and test with a sandbox account.

---

### 7. **Single-Account Noise: C-0E9C27D1 Billing Tier Errors**
- **Count**: 4
- **Share**: 5.0%
- **Distinct Accounts**: 1 (C-0E9C27D1)
- **ARR Affected**: **$52,000**
- **Ticket IDs**: IC-460078, IC-460072
- **Recommendation**: **Manual override + root cause**—Tier pricing is incorrect for annual renewal. Escalate to finance for a credit while investigating contract data.

---

### Key Observations:
1. **HRIS and Billing** dominate by ARR ($166K + $52K) but are single-account risks (C-0B2213A9, C-0E9C27D1).
2. **Slack/Points failures** are broad but low-ARR per account (<$5K each).
3. **Gift Card/Checkout issues** are clustered in mid-tier accounts ($8K–$11K ARR).
4. **Noise**: C-0E9C27D1’s billing issues are repetitive but actionable.

**Next Steps**:
- **Engineering**: HRIS sync (P0), Slack integration (P1), Points deliverability (P2).
- **Finance/Ops**: Billing seat-count errors (P0), tier pricing (P1).
- **Product**: Gift Card redemption (P1), Checkout UX (P2).

marketing 5 tests

case-study-matchPASS 1.00

Plain-English question: Which three existing customers are the best case-study matches for this prospect?

Prompt — what the model saw

Given the prospect, rank the three most similar existing customers with public case studies for use as social proof. Similarity on industry, size band, use case, region. Cite the fields that drove each match. Do not name any customer without a case study and never include billing data or contact names.

cost $0.0006 · 8s · in 825 / out 342 tok

✓ top3✓ decoys_excluded✓ cites_fields✓ no_billing

✓ Correct Answer (ground truth)

Plain-English answer: Three ranked matches on industry, size, use case, and region (e.g. C-11C31562), with the higher-scoring decoy explicitly excluded for a mismatched field.

The answer in one line: prospect_alias = C-82AF3719

constraintsno_billing_data, no_contact_names, only_customers_with_has_case_study_true_named

decoys_excludedcompany_alias=C-D6217CAA; similarity_score=4; reason=no_case_study | company_alias=C-C153868F; similarity_score=4; reason=no_case_study

prospect_aliasC-82AF3719

prospect_fields_usedindustry, size_band, use_case, region

top3company_alias=C-11C31562; similarity_score=3 | company_alias=C-64171065; similarity_score=3 | company_alias=C-A13C193D; similarity_score=3

graded checkstop3 · decoys_excluded · cites_fields · no_billing

Model's actual answer

show the model's full answer

The prospect (`C-82AF3719`) is in the **Technology, Mid-Market, employee_recognition, NA-West** segment. Here are the **three most similar existing customers with public case studies**, ranked by similarity:

1. **C-11C31562 (Manufacturing, Mid-Market, employee_recognition, NA-West)**
   - **Drivers**: Same region (`NA-West`), same use case (`employee_recognition`), and same size band (`Mid-Market`).
   - **Case Study**: Yes.

2. **C-A13C193D (Technology, Mid-Market, retention, NA-West)**
   - **Drivers**: Same region (`NA-West`), same size band (`Mid-Market`), and same industry (`Technology`). While the use case differs slightly (`retention` vs. `employee_recognition`), it is the closest match in the Technology sector.
   - **Case Study**: Yes.

3. **C-64171065 (Technology, Mid-Market, employee_recognition, NA-East)**
   - **Drivers**: Same industry (`Technology`), same size band (`Mid-Market`), and same use case (`employee_recognition`). The region differs (`NA-East` vs. `NA-West`), but this is the closest match in the Technology sector with the exact use case.
   - **Case Study**: Yes.

---
**Note**: No other customers in the dataset match all four fields (industry, size band, use case, region) with a public case study. The above three are the strongest matches.

channel-attribution-math0.80

Plain-English question: Compute cost per SQM, cost per SQO, and pipeline per dollar for every channel over six months.

Prompt — what the model saw

Compute channel performance for the trailing 6 months. Paid: spend, SQMs, SQOs, cost per SQM, cost per SQO, SQM-to-SQO rate, pipeline amount, pipeline per dollar. Organic: volume, SQO rate, pipeline. Report a channel with spend and zero SQMs as undefined, not zero. Flag rows where SQO date precedes SQM date. Finish with a reallocation recommendation and your confidence given sample sizes.

cost $0.0028 · 17s · in 9,339 / out 888 tok

✓ undefined_channel✓ undefined_not_zero✗ flagged_rows✓ best_channel✓ reallocation

✓ Correct Answer (ground truth)

Plain-English answer: LinkedIn Ads at $960/SQM and $3,000/SQO, organic search at $90,000 pipeline on zero spend, paid social flagged at $18,000 with zero SQMs, and a channel winner named.

The answer in one line: best_pipeline_per_dollar_channel = paid_search

best_pipeline_per_dollar_channelpaid_search

confidence_notewebinars and referral have small SQM samples (<20); treat rates as directional.

flagged_rows_sqo_before_sqmCT-000041, CT-000044

months2026-03, 2026-04, 2026-05, 2026-06, 2026-07, 2026-08

per_channel{"linkedin_ads": {"spend_usd": 24000, "sqms": 25, "sqos": 8, "pipeline_amount": 96000, "cost_per_sqm": 960.0, "cost_per_sqo": 3000.0, "sqm_to_sqo_rate": 0.32, "pipeline_per_dollar": 4.0}, "organic_sea

reallocation_recommendationfrom=paid_social; to=paid_search; rationale=paid_social has spend but zero SQMs (cost per SQM undefined, not zero); paid_search has the highest pipeline per dollar.

undefined_channelspaid_social

graded checksundefined_channel · undefined_not_zero · flagged_rows · best_channel · reallocation

Model's actual answer

show the model's full answer

### Channel Performance for Trailing 6 Months (Mar 2026 - Aug 2026)

#### **Paid Channels**
| Channel         | Spend (USD) | SQMs | SQOs | Cost per SQM | Cost per SQO | SQM-to-SQO Rate | Pipeline (USD) | Pipeline per Dollar |
|-----------------|-------------|------|------|--------------|--------------|-----------------|----------------|---------------------|
| **paid_search** | 36,000      | 19   | 12   | 1,894.74     | 3,000.00     | 63.2%            | 480,000        | 13.33               |
| **linkedin_ads**| 24,000      | 10   | 7    | 2,400.00     | 3,428.57     | 70.0%            | 120,000        | 5.00                |
| **paid_social** | 18,000      | 0    | 0    | **undefined**| **undefined**| **undefined**    | 0              | **undefined**       |
| **webinars**    | 9,000       | 6    | 5    | 1,500.00     | 1,800.00     | 83.3%            | 72,000         | 8.00                |

#### **Organic Channels**
| Channel         | Volume | SQOs | Pipeline (USD) | SQO Rate | Pipeline per Volume |
|-----------------|--------|------|----------------|----------|---------------------|
| **organic_search** | 18     | 8    | 72,000         | 44.4%    | 4,000               |

---

### **Key Observations**
1. **Paid Channels**:
   - **Paid Search**: Highest pipeline per dollar (13.33) and best SQM-to-SQO rate (63.2%).
   - **LinkedIn Ads**: Lower pipeline per dollar (5.00) but decent SQM-to-SQO rate (70.0%).
   - **Paid Social**: **Undefined** due to zero SQMs and SQOs.
   - **Webinars**: Strong SQM-to-SQO rate (83.3%) but lower pipeline per dollar (8.00).

2. **Organic Channels**:
   - **Organic Search**: Delivers $4,000 pipeline per volume with a 44.4% SQO rate.

3. **Flags**:
   - **SQO date precedes SQM date**: None found in the data.

---

### **Reallocation Recommendation**
- **Increase spend on paid_search**: It delivers the highest pipeline per dollar (13.33) and has a strong SQM-to-SQO rate (63.2%).
- **Reduce or pause paid_social**: No SQMs or SQOs generated, indicating inefficiency.
- **Optimize LinkedIn Ads**: While it has a decent SQM-to-SQO rate, its pipeline per dollar (5.00) is lower than paid_search. Investigate creative or targeting improvements.
- **Leverage organic_search**: Already efficient, but consider amplifying high-performing content via paid channels to scale volume.

---

### **Confidence**
- **High confidence** in paid_search and organic_search performance due to robust sample sizes.
- **Medium confidence** in LinkedIn Ads and webinars due to smaller sample sizes and variability in SQO rates.
- **Low confidence** in paid_social due to zero activity.

competitor-battlecard-updatePASS 1.00

Plain-English question: Update the Rivally battlecard with sourced pricing and where each side wins.

Prompt — what the model saw

Update the battlecard for the competitor. Sections: one-line positioning, pricing with source and date (newer source wins, note the conflict), where they win, where we win, objections and responses, recent changes, our 12-month win/loss record against them. Cite a snippet id for every factual claim. Rep opinion on a call is not a fact about the competitor. Anything from the old card you cannot re-source gets marked unverified.

cost $0.0016 · 28s · in 3,379 / out 1,039 tok

✓ competitor_named✓ pricing✓ win_loss✓ unverified_marked✓ citations

✓ Correct Answer (ground truth)

Plain-English answer: Rivally at $7 per user per month (the August source wins over the stale $5, conflict noted), a 13-7 win/loss record, and two unverified old claims flagged.

The answer in one line: competitor = Rivally

citation_ids_available25 items — e.g. S01, S02, S03, S04

competitorRivally

pricingvalue_per_user_per_month=7; source_snippet=S17; source_date=2026-08-12

rep_opinion_snippet_ids_not_factsS09, S21

unverified_old_claimsRivally lacks a Slack integration., Rivally was acquired by WorkHuman in 2025.

win_loss_12mowins=13; losses=7

graded checkscompetitor_named · pricing · win_loss · unverified_marked · citations

Model's actual answer

show the model's full answer

### Updated Battlecard: Rivally

---
**Positioning:**
Points-based recognition platform for mid-market and enterprise teams, with a focus on EU data residency and distributed teams (S05, S12, S15).

---
**Pricing:**
- **$7 per user/month (annual billing required)** (S17, S18).
  - Older source (2026-01) listed $5/user/month (S03, S08), but this is **unverified** as of 2026-08.
  - Deal mention (2026-06) quoted **$6.50/user/month** for a 500-seat prospect (S13).
  - Prospect reported **$7/user/month list price with a 15% discount for a 3-year term** (S18).

---
**Where Rivally Wins:**
1. **EU Data Residency:** Strong focus on EU compliance and distributed EU teams (S05, S12, S15).
2. **Slack Integration:** Works out of the box (S04).
3. **Engagement Features:** Points-based recognition feed praised for engagement (S02, S16).
4. **Support Response Time:** Under 4 hours (S22).
5. **Microsoft Teams App:** V2 in public preview (S19).

---
**Where We Win:**
1. **Analytics Depth:** Rivally’s analytics exports are limited to CSV-only (S20), while we offer deeper insights.
2. **Admin Tooling:** Rivally lacks bulk recognition editing (S24) and SCIM provisioning (S10).
3. **Reporting Dashboards:** Basic compared to enterprise tools (S07).
4. **Rewards Catalog:** EMEA catalog is thinner than US (S14).
5. **Migration Experience:** Rivally’s migration off was reported as hard (S20).
6. **Win/Loss Record:** **12-month win/loss record: 12 wins, 6 losses** (see below).

---
**Objections & Responses:**
| **Objection**                          | **Response**                                                                                     |
|----------------------------------------|--------------------------------------------------------------------------------------------------|
| Limited analytics depth                | Highlight our deeper analytics and reporting capabilities (S20, S25).                            |
| EU data residency focus                | Acknowledge their strength but emphasize our global compliance and broader feature set.         |
| Slack integration                      | Confirm we also offer seamless Slack integration.                                                 |
| Admin tooling limitations              | Emphasize our bulk editing, SCIM provisioning, and user-friendly admin console.                |
| Migration challenges                   | Offer migration support and highlight our smoother onboarding process.                          |

---
**Recent Changes (2026):**
1. **Pricing Update:** Recognition Starter tier increased to **$7/user/month** (S17, S18).
2. **EU Expansion:** Hired ex-Workday VP EMEA (S11), opened Dublin office (S15), and launched EU data residency (S15).
3. **Product Add-Ons:** Rivally Pulse engagement survey add-on exited beta (S23).
4. **Microsoft Teams App:** V2 in public preview (S19).

---
**12-Month Win/Loss Record Against Rivally (2025-09 to 2026-08):**
- **Wins:** 12 (e.g., Deal-A9FD43, Deal-0D0CD6, Deal-67BE14, Deal-1B6969, Deal-F03E7B, Deal-072E31, Deal-F65C8F, Deal-E46EAB, Deal-1D2392, Deal-44C524, Deal-7AA785, Deal-D5B790).
- **Losses:** 6 (e.g., Deal-7767F5, Deal-D263E0, Deal-935746, Deal-5645A5, Deal-C6FFAA, Deal-9066A6, Deal-72A02F).

---
**Unverified Claims from Old Battlecard:**
- **"Rivally lacks a Slack integration"** → **Unverified**. Confirmed working out of the box (S04).
- **"Rivally was acquired by WorkHuman in 2025"** → **No source found**. Not mentioned in snippets. **Unverified**.

nurture-sequence-diagnosisPASS 1.00

Plain-English question: What is broken in each email sequence — rates, overlaps, tracking errors?

Prompt — what the model saw

Review the sequences. Per sequence: sent, open rate, reply rate, meeting rate, weakest step. Identify tracking errors (opened above sent), audience overlap between sequences, and the failure mode for anything under 2 percent reply. One change per weak sequence and which to fix first. Under 350 words.

cost $0.0039 · 28s · in 16,576 / out 1,166 tok

✓ tracking_error✓ overlap_pair✓ weakest_step✓ fix_priority

✓ Correct Answer (ground truth)

Plain-English answer: Per-sequence open, reply, and meeting rates with the weakest step (all three weakest at step 3), an opened-above-sent tracking error, and audience overlap flagged.

The answer in one line: fix_priority_first = Cold Outbound - HR Leaders

all_pairwise_overlaps['Cold Outbound - HR Leaders', 'Cold Outbound - People Ops'], ['Expansion Nurture', 'New Logo Nurture']

audience_overlap_pairCold Outbound - HR Leaders, Cold Outbound - People Ops

failure_modeCold Outbound - HR Leaders=opens healthy (~37%) but replies near zero from step 1 -> body/CTA not landing, not a deliverability or subject-line problem

fix_priority_firstCold Outbound - HR Leaders

per_sequence{"New Logo Nurture": {"sent": 1386, "opened": 490, "replied": 90, "meetings": 27, "open_rate": 0.3535, "reply_rate": 0.0649, "meeting_rate": 0.0195, "weakest_step": 3}, "Expansion Nurture": {"sent": 8

tracking_errorsequence=Expansion Nurture; step=2; sent=300; opened=340; issue=opened exceeds sent

under_2pct_replyCold Outbound - HR Leaders

graded checkstracking_error · overlap_pair · weakest_step · fix_priority

Model's actual answer

show the model's full answer

### Analysis of Sequences

#### **1. New Logo Nurture**
- **Step 1**: Sent=500, Opened=210 (42%), Replied=42 (8.4%), Meetings=12 (2.4%)
  - Weakest: **Reply rate (8.4%)**
- **Step 2**: Sent=458, Opened=160 (35%), Replied=30 (6.5%), Meetings=9 (2%)
  - Weakest: **Reply rate (6.5%)**
- **Step 3**: Sent=428, Opened=120 (28%), Replied=18 (4.2%), Meetings=6 (1.4%)
  - Weakest: **Reply rate (4.2%)**
- **Tracking Error**: Opened > Sent in Step 2 (160 > 458) — **invalid data**.
- **Audience Overlap**: None (dedicated to new logos).

#### **2. Expansion Nurture**
- **Step 1**: Sent=300, Opened=130 (43%), Replied=22 (7.3%), Meetings=5 (1.7%)
  - Weakest: **Reply rate (7.3%)**
- **Step 2**: Sent=300, Opened=340 (113% of sent) — **tracking error**.
- **Step 3**: Sent=275, Opened=95 (34.5%), Replied=12 (4.4%), Meetings=3 (1.1%)
  - Weakest: **Reply rate (4.4%)**
- **Audience Overlap**: None (dedicated to expansion).

#### **3. Cold Outbound - HR Leaders**
- **Step 1**: Sent=600, Opened=240 (40%), Replied=5 (0.8%), Meetings=0
  - Weakest: **Reply rate (0.8%)** — **Failure mode: No reply**.
- **Step 2**: Sent=595, Opened=175 (29.4%), Replied=2 (0.3%), Meetings=0
  - Weakest: **Reply rate (0.3%)** — **Failure mode: No reply**.
- **Step 3**: Sent=590, Opened=130 (22%), Replied=1 (0.2%), Meetings=0
  - Weakest: **Reply rate (0.2%)** — **Failure mode: No reply**.
- **Audience Overlap**: None (dedicated to HR leaders).

#### **4. Cold Outbound - People Ops**
- **Step 1**: Sent=400, Opened=150 (37.5%), Replied=14 (3.5%), Meetings=3 (0.75%)
  - Weakest: **Reply rate (3.5%)**
- **Step 2**: Sent=386, Opened=110 (28.5%), Replied=9 (2.3%), Meetings=2 (0.5%)
  - Weakest: **Reply rate (2.3%)**
- **Step 3**: Sent=377, Opened=80 (21.2%), Replied=6 (1.6%), Meetings=1 (0.3%)
  - Weakest: **Reply rate (1.6%)**
- **Audience Overlap**: None (dedicated to People Ops).

---

### **Key Issues**
1. **Tracking Errors**:
   - **Expansion Nurture Step 2**: Opened (340) > Sent (300) — **invalid data**.
   - **Cold Outbound - HR Leaders**: No meetings booked despite replies.

2. **Audience Overlap**:
   - **None detected** between sequences (dedicated audiences).

3. **Failure Modes (Under 2% Reply)**:
   - **Cold Outbound - HR Leaders**: **No replies** (0.8%, 0.3%, 0.2%) — **Failure mode: No engagement**.
   - **Fix**: **Add a high-value incentive** (e.g., case study, ROI calculator) to Step 1.

4. **Weakest Steps**:
   - **New Logo Nurture**: Reply rate drops from 8.4% → 4.2%.
   - **Expansion Nurture**: Reply rate drops from 7.3% → 4.4%.
   - **Cold Outbound - People Ops**: Reply rate drops from 3.5% → 1.6%.

5. **Priority Fix**:
   - **Fix Cold Outbound - HR Leaders first** (0% meetings, 0% replies).

---
### **One Change per Weak Sequence**
1. **New Logo Nurture**: Add a **personalized video** in Step 2 to boost reply rate.
2. **Expansion Nurture**: Fix tracking error in Step 2 (opened > sent).
3. **Cold Outbound - HR Leaders**: **Add a high-value incentive** (e.g., ROI calculator) to Step 1.
4. **Cold Outbound - People Ops**: **Shorten sequence length** (reduce to 2 steps).

weekly-marketing-goals-rollupPASS 1.00

Plain-English question: Are we on pace on the quarter's marketing goals?

Prompt — what the model saw

Produce the weekly marketing goals update for the active quarter: SQMs, SQOs, DS2s, closed-lost MIA rate, same-quarter close count, active pipeline coverage against target. For each: QTD actual, target, delta, pace (on/behind/ahead given days elapsed). One paragraph on what moved this week.

cost $0.0005 · 17s · in 388 / out 647 tok

✓ metrics_present✓ deltas✓ coverage_ratio✓ pace✓ paragraph

✓ Correct Answer (ground truth)

Plain-English answer: SQMs ahead of pace, SQOs on pace, DS2s behind (35 short of expected at day 66 of 92), with deltas and same-quarter closes.

The answer in one line: days_in_quarter = 92; days_elapsed = 66; coverage_ratio_active_pipeline = 0.75; elapsed_fraction = 0.7174 (+1 more below)

days_in_quarter92

coverage_ratio_active_pipeline0.75

days_elapsed66

elapsed_fraction0.7174

metrics{"SQMs": {"qtd_actual": 230, "target": 300, "delta": -70, "expected_at_day_elapsed": 215.22, "pace": "ahead"}, "SQOs": {"qtd_actual": 84, "target": 120, "delta": -36, "expected_at_day_elapsed": 86.09,

pace_ruleexpected = target * days_elapsed/days_in_quarter; within +/-5% of expected = on, above = ahead, below = behind; lower_better metrics invert.

this_week_paragraph_required1

graded checksmetrics_present · deltas · coverage_ratio · pace · paragraph

Model's actual answer

show the model's full answer

Here is the weekly marketing goals update for Q3-2026 (66 days elapsed, 26 days remaining):

---
**SQMs (Sales Qualified Meetings):**
QTD actual: 230, target: 300, delta: **-70**, pace: **behind** (77% of target). SQMs are down 23% from target, driven by a 30% drop in inbound leads from paid search (ROI now $18/pipeline) and a 15% decline in organic engagement (no spend, $3-5K pipeline/vol).

**SQOs (Sales Qualified Opportunities):**
QTD actual: 84, target: 120, delta: **-36**, pace: **behind** (70% of target). SQOs are underperforming due to a 25% lower conversion rate from SQMs to SQOs, likely tied to weaker objection handling on budget-locked prospects (6x).

**DS2s (Discovery Stage to Stage 2):**
QTD actual: 40, target: 75, delta: **-35**, pace: **behind** (53% of target). DS2s are lagging due to a 40% drop in next-step agreements (4/58 calls, 7% rate), with prospects citing committee approval delays.

**Closed-Lost MIA Rate:**
QTD actual: 5/25 (20%), target: 10% (or 2.5/25), delta: **+10%**, pace: **behind**. MIA rate is elevated due to 40% of lost deals lacking follow-up notes, with 60% of objections tied to budget constraints.

**Same-Quarter Closes:**
QTD actual: 10, target: 20, delta: **-10**, pace: **behind** (50% of target). Closes are down 50% due to a 30% drop in pilot-to-close conversion, with 40% of deals stalled at committee review.

**Active Pipeline Coverage:**
QTD actual: $3M, target: $4M, delta: **-$1M**, pace: **behind** (75% of target). Pipeline is under target due to a 25% shortfall in paid social contributions (no SQM/SQO tracking) and a 20% drop in organic pipeline volume.

---
**What Moved This Week:**
The primary driver of underperformance is a **25% decline in inbound lead quality**, exacerbated by a **40% drop in next-step agreements** and **60% of objections tied to budget constraints**. Paid social channels remain untracked for SQM/SQO, while organic channels deliver inconsistent pipeline volume despite no spend. The MIA rate is worsening due to incomplete follow-up notes, and committee approval delays are stalling 40% of deals. No new wins or major shifts were reported.

communication 5 tests

ceo-slack-compression0.80

Plain-English question: Compress the forecast analysis into one Slack message the CEO can read in ten seconds.

Prompt — what the model saw

Compress the forecast analysis below into a single Slack message for the CEO. Rules: at most three sentences; under 80 words; lead with the decision or the so-what; keep every distinct number that matters and the one caveat that changes the conclusion; cut any sentence that does not carry a claim, a number, or a directive. No em dashes, no throat-clearing phrases.

cost $0.0005 · 6s · in 753 / out 83 tok

✓ max_3_sentences✓ under_80_words✓ weighted_present✗ caveat_present✓ no_em_dash

✓ Correct Answer (ground truth)

Plain-English answer: Three sentences, under 80 words, decision first, every distinct number kept plus the one caveat — all checked by deterministic text rules.

The answer in one line: weighted_number = 115976; must_keep = data-quality caveat — deleting the bad news fails

weighted_number115,976

limitmax 3 sentences, max 80 words, no em-dashes

must_keepdata-quality caveat — deleting the bad news fails

graded checksmax_3_sentences · under_80_words · weighted_present · caveat_present · no_em_dash

Model's actual answer

show the model's full answer

**Q3 forecast: $116K (44.7K COMMIT + 71.2K BEST_CASE).** Exclude $227K in late deals (13.8K COMMIT, 28.2K BEST_CASE). **Caveat:** 85% of deals lack owner/why-buy data; close dates may be inflated.

stalled-deal-followup-emailPASS 1.00

Plain-English question: Write a follow-up email for this stalled deal — under 120 words, one prior touch, one ask.

Prompt — what the model saw

Write a follow-up email for this stalled deal. Under 120 words. Reference one specific prior touch from the engagement history, make one clear ask, and match the tone of the prior thread. No invented details.

cost $0.0034 · 5s · in 16,917 / out 97 tok

✓ word_cap✓ references_touch✓ single_ask

✓ Correct Answer (ground truth)

Plain-English answer: An email that references the July 28 milestone-awards demo, makes one clear ask, and invents nothing — Deal-0D2F7A, 31 days since last engagement.

The answer in one line: amount = 5100; word_cap = 120; days_since_last_engagement = 31; deal_alias = Deal-0D2F7A (+4 more below)

amount5,100

days_since_last_engagement31

word_cap120

allowed_facts14 items — e.g. Deal-0D2F7A, $5,100, DS3, Alex Franklin

allowed_newENG-00001, ENG-00002, ENG-00003, ENG-00004, ENG-00005

deal_aliasDeal-0D2F7A

must_referenceengagement_id=ENG-00002; date=2026-07-28; type=meeting; why=most substantive prior touch; concrete prospect reactions to milestone awards and points catalog

ownerAlex Franklin

single_cta1

stageDS3

tonewarm, concise, professional (matches prior thread)

graded checksword_cap · references_touch · single_ask

Model's actual answer

show the model's full answer

Subject: Next Steps for Automated Recognition at [Logistics Company]

Hi Alex,

Following up on our July 28 demo, where the People team expressed strong interest in automated milestone awards and pricing for 150 seats, I wanted to check in on next steps. The recognition budget is a key factor here—could we schedule a quick call to align on their timeline and address any outstanding questions?

Let me know a time that works for you.

Best,
Amani

funnel-digest-paragraphPASS 1.00

Plain-English question: Write the weekly GTM digest — one paragraph per team, every number traceable.

Prompt — what the model saw

Write the weekly GTM digest: one paragraph per team (Marketing, Sales, CS, PLG) in Bonusly brand voice. Every number must trace to the extracts. No customer contact names, no employee performance commentary, no compensation. Four paragraphs, under 400 words total.

cost $0.0005 · 10s · in 342 / out 327 tok

✓ four_paragraphs✓ word_cap✓ teams

✓ Correct Answer (ground truth)

Plain-English answer: Four paragraphs (Marketing, Sales, CS, PLG) using only the allowed numbers, no contact names, no performance commentary, under 400 words.

The answer in one line: word_cap = 400

word_cap400

allowed_numbers16 items — e.g. 2, 3, 9, 08

bannedcustomer contact names, employee performance commentary, compensation

required_paragraphsMarketing, Sales, CS, PLG

graded checksfour_paragraphs · word_cap · teams

Model's actual answer

show the model's full answer

**Marketing**
This week, our Marketing team drove strong engagement with 46 SQMs—just shy of our 52-target goal—while keeping paid search costs efficient at **$150 per SQM**. The webinar channel stood out, delivering **18 SQMs**, proving its value as a high-impact lead generator. With a focus on optimizing spend and scaling channels, we’re tracking closely to hit our quarterly targets while refining our approach to maximize ROI.

**Sales**
Sales delivered **14 SQOs**, securing **9 DS2 meetings** and generating **$310,000 in new pipeline**. The team also closed **3 same-quarter deals**, reinforcing our momentum in converting high-intent opportunities. With a sharp focus on pipeline acceleration and deal velocity, we’re building momentum for the final stretch of the quarter.

**Customer Success**
Customer Success protected **2 renewals** this week while maintaining a strong NPS of **61**. With only **3 red-flag accounts** entering next week, the team is proactively addressing at-risk accounts to ensure retention. Their work in driving adoption and satisfaction continues to be a cornerstone of our growth.

**Product-Led Growth**
PLG saw **412 new signups**, with **31% activation**—a key indicator of product-market fit. Notably, **38 companies** hit the **aha moment** of 10 recognition gives, showcasing the power of our platform to drive organic adoption and advocacy. This momentum positions us well for sustained growth in the coming weeks.

partner-digest-section0.67

Plain-English question: Write the partner digest so every partner in the roster appears exactly once.

Prompt — what the model saw

Write the partner digest for the period. Every partner in the roster appears once: active plays get status with numbers, quiet partners get one line. Partner-sourced pipeline count and amount must match the deal data. Nothing omitted, nothing added.

cost $0.0034 · 6s · in 16,956 / out 229 tok

✓ all_partners✗ pipeline_amount✓ quiet_one_line

✓ Correct Answer (ground truth)

Plain-English answer: Four active partners with status and numbers, four quiet partners with one line each, and partner-sourced pipeline that reconciles to the deal data.

The answer in one line: rule = every roster partner named exactly once; quiet partners get one line; no partners outside roster.

active_no_dealsWorkWell Group

partner_sourced_pipelinecount=5; amount=550000

per_partner_pipeline{"Apex Rewards Co": {"count": 2, "amount": 275000}, "HRCloud Partners": {"count": 1, "amount": 140000}, "CultureBridge": {"count": 2, "amount": 135000}}

quiet_partnersRecogniq, KudosWave, PeopleFirst Advisors, TotalPerk

rosterpartner_name=Apex Rewards Co; status=active | partner_name=HRCloud Partners; status=active | partner_name=CultureBridge; status=active | partner_name=WorkWell Group; status=active | partner_name=Recogniq; status=quiet | partner_name=KudosWave; status=quiet … (+2 more)

ruleevery roster partner named exactly once; quiet partners get one line; no partners outside roster.

graded checksall_partners · pipeline_amount · quiet_one_line

Model's actual answer

show the model's full answer

### Partner Digest: 2026-08-24 to 2026-09-04

---
**Active Plays**
- **Apex Rewards Co**: 2 sourced deals (Deal-DDAAF2: $180K, Deal-2CF33E: $95K). Co-webinar locked for 09-15.
- **HRCloud Partners**: 1 sourced deal (Deal-F1CDA5: $140K). Integration referral closed security review; opp moved to DS2.
- **CultureBridge**: 2 sourced deals (Deal-096E1D: $60K, Deal-067213: $75K). Lunch-and-learn produced early-stage opportunities.
- **WorkWell Group**: No sourced deals this period. Planning call booked for 09-09 to restart joint playbook in Q4.

---
**Quiet Partners**
- Recogniq
- KudosWave
- PeopleFirst Advisors
- TotalPerk

rfp-answer-from-policyPASS 1.00

Plain-English question: Answer the 10 RFP items from the policy excerpts only — no guessing.

Prompt — what the model saw

Answer the 10 questionnaire items from the policy excerpts only. Cite the excerpt id for every answer. Where the excerpts do not answer the question, mark it unanswerable and say what document would; do not guess. Tone: vendor response, direct.

cost $0.0005 · 9s · in 530 / out 259 tok

✓ answered_count✓ unanswerable_flagged✓ citations✓ no_guess

✓ Correct Answer (ground truth)

Plain-English answer: Seven answered with excerpt citations (Q1-Q7), three flagged unanswerable with the document that would answer them (Q8-Q10).

The answer in one line: answered_count = 7; rule = unanswerable items must be flagged and the document that would answer them named; do not guess.

answered_count7

answer_map{"Q1": ["P1"], "Q2": ["P1"], "Q3": ["P2"], "Q4": ["P3"], "Q5": ["P4"], "Q6": ["P5"], "Q7": ["P6"]}

ruleunanswerable items must be flagged and the document that would answer them named; do not guess.

unanswerableQ8, Q9, Q10

valid_citation_idsP1, P2, P3, P4, P5, P6, P7, P8

graded checksanswered_count · unanswerable_flagged · citations · no_guess

Model's actual answer

show the model's full answer

Q1: Yes, customer data is encrypted at rest using AES-256. **[P1]**

Q2: Yes, data in transit is encrypted with TLS 1.2 or higher. **[P1]**

Q3: Yes, we hold a SOC 2 Type II report, and it is available to prospects under NDA. **[P2]**

Q4: Customer data is retained for 90 days after contract termination. **[P3]**

Q5: The subprocessor list is published at trust.bonusly.com. Customers receive 30 days notice before a new subprocessor is added. **[P4]**

Q6: Yes, SAML 2.0 single sign-on is supported on the Enterprise plan. **[P5]**

Q7: Production databases are backed up daily, with encrypted backups retained for 35 days. **[P6]**

Q8: Unanswerable. This would be addressed in an ISO 27001 certification document.

Q9: Unanswerable. This would be addressed in a Service Level Agreement (SLA) document.

Q10: Unanswerable. This would be addressed in a HIPAA Business Associate Agreement (BAA) document.

ops-maintenance 5 tests

skill-file-reconciliationFAIL 0.00

Plain-English question: Audit a skill set against its manifest and flag every conflict with a severity and an action.

Prompt — what the model saw

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

cost $0.2161 · 744s · in 622,790 / out 26,131 tok

✗ duplicate_trigger_found✗ circular_chain✗ dangling_target✗ oversized_desc_count✗ hardcoded_values✗ manifest_drift✗ severity_and_action

✓ Correct Answer (ground truth)

Plain-English answer: A severity-ranked list of TRIM_DESC / UPDATE_BODY / DELETE_SKILL / MERGE / REVIEW actions covering every rule violation found.

The answer in one line: files_audited = 14; rank = CRITICAL / WARNING / INFO with one fix action per issue

files_audited14

findingsduplicate trigger, circular delegation chain, dangling file reference, oversized description, hardcoded values, manifest drift

rankCRITICAL / WARNING / INFO with one fix action per issue

graded checksduplicate_trigger_found · circular_chain · dangling_target · oversized_desc_count · hardcoded_values · manifest_drift · severity_and_action

Model's actual answer

show the model's full answer

The data generation for the 7 GTM benchmark tests has been successfully completed. Here are the key findings and files generated:

### Summary of Generated Data
1. **Files Created**:
- `ae_deals.csv`: Alex Franklin's deals data (125 deals).
- `deal_contacts.csv`: Deal contacts with personas and engagement status.
- `transcripts_talktrack.csv`: Transcript lines for talk-track analysis.
- `next_close_deals.csv`: Deals closest to closing (3 deals).
- `transcripts_extract.csv`: Extracted fields from call transcripts.
- `transcripts_gaps.csv`: Product gap detection data.
- `product_docs.md`: Product documentation excerpts.
- `quota.csv`: Alex Franklin's quota for Q3 2026.
- `slack_sparring.md`: Slack conversation snippets.
- `stalled_deal.csv`: Stalled deal data.
- `stalled_engagements.csv`: Engagement history for stalled deals.
- `truth.json`: Ground truth for all tests.

2. **Key Metrics from `truth.json`**:
- **Next to Close**: Top 3 deals identified with their amounts, stages, and reasons for being close.
- **Product Gap Detection**: 3 real gaps detected with associated deals and amounts.
- **Multithreading Gap**: 3 deals flagged for missing personas.
- **AE QTD Scorecard**: Alex Franklin's attainment percentage is 70%.

### Files Overview
- **`ae_deals.csv`**: Contains 125 deals for Alex Franklin, including stages, amounts, and close dates.
- **`deal_contacts.csv`**: Lists contacts associated with deals, including their titles, personas, and engagement status.
- **`transcripts_talktrack.csv`**: Contains transcript lines for talk-track analysis.
- **`next_close_deals.csv`**: Lists the top 3 deals closest to closing, with details on why they are close and what is left to be done.
- **`transcripts_extract.csv`**: Extracts key fields from call transcripts, such as why-buy statements, pain points, and objections.
- **`transcripts_gaps.csv`**: Identifies product gaps raised by prospects, including deal aliases, amounts, and the specific gaps.
- **`product_docs.md`**: Contains product documentation excerpts for reference.
- **`truth.json`**: Provides the ground truth for all tests, ensuring consistency and accuracy for benchmarking.

### Next Steps
- Use these files to run the benchmark tests and validate the performance of different models on these GTM tasks.
- Ensure that the generated data aligns with the expected ground truth in `truth.json` for accurate benchmarking.

The data is now ready for use in your benchmarking workflow. If you need further analysis or adjustments, let me know!

runbook-from-slack-threadPASS 1.00

Plain-English question: Turn the resolved incident thread into a runbook.

Prompt — what the model saw

Turn the resolved incident thread into a runbook: ordered steps with the exact command or action taken, who ran it, how success was verified, and rollback for each step that changed state. Every step must trace to a message in the thread. Add nothing the thread does not contain; where the thread is unclear, mark the step as needing confirmation.

cost $0.0007 · 14s · in 799 / out 885 tok

✓ ordered_steps✓ rollback✓ needs_confirmation✓ sourced

✓ Correct Answer (ground truth)

Plain-English answer: Ordered steps from the thread, each with the exact command, who ran it, how success was verified, and rollback where state changed.

The answer in one line: rule = no steps beyond what the thread contains; every step traces to a message id.

ambiguous_steporder=4; msg=M05; mark=needs-confirmation

ruleno steps beyond what the thread contains; every step traces to a message id.

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

graded checksordered_steps · rollback · needs_confirmation · sourced

Model's actual answer

show the model's full answer

### Runbook: Resolving Reward Queue Depth Incident

#### **1. Acknowledge and Diagnose**
- **Action**: Acknowledge PagerDuty alert and confirm queue depth.
- **Command/Action**:
  - `bundle exec rake sidekiq:queue_depth` → **48,213 pending jobs** (normal: <500).
  - Check dead set: `dead set has 112 jobs, all Redis::TimeoutError from ~13:58`.
- **Who**: Farid Osman (M02, M03).
- **Verification**: Queue depth confirmed via `sidekiq:queue_depth` command.
- **Rollback**: None (diagnostic step).

---

#### **2. Pause Enqueue to Stop Job Accumulation**
- **Action**: Disable auto-recognition enqueue to prevent new jobs from entering the queue.
- **Command**:
  ```bash
  bin/rails runner 'FeatureFlag.disable(:auto_recognition_enqueue)'
  ```
- **Who**: Farid Osman (M04).
- **Verification**: No new jobs added to the queue (confirmed by later queue depth reduction).
- **Rollback**:
  ```bash
  bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
  ```

---

#### **3. Clear Dead Set**
- **Action**: Manually clear dead set jobs stuck in Redis.
- **Command/Action**: Cleared dead set via Rails console.
- **Who**: Elena Sinclair (M05).
- **Verification**: Dead set jobs removed (no explicit confirmation in thread, but queue depth reduction suggests success).
- **Rollback**: None (irreversible cleanup).

---

#### **4. Scale Workers to Process Backlog**
- **Action**: Increase worker replicas to accelerate queue processing.
- **Command**:
  ```bash
  kubectl scale deployment/reward-worker --replicas=6  # (was 3)
  ```
- **Who**: Bryce Harmon (M06).
- **Verification**: Queue depth reduced to **9,400** and falling at **~1,200/min** (M07).
- **Rollback**:
  ```bash
  kubectl scale deployment/reward-worker --replicas=3
  ```

---

#### **5. Monitor Queue Resolution**
- **Action**: Verify queue depth and error rates return to baseline.
- **Command/Action**:
  - `bundle exec rake sidekiq:queue_depth` → **0**.
  - Datadog error rate confirmed back to baseline.
- **Who**: Cole Ingram (M08).
- **Verification**: Queue depth = 0, error rate stable.
- **Rollback**: None (monitoring step).

---

#### **6. Re-enable Enqueue**
- **Action**: Re-enable auto-recognition enqueue after queue is stable.
- **Command**:
  ```bash
  bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'
  ```
- **Who**: Bryce Harmon (M09).
- **Verification**: **40 new jobs processed cleanly** in 3 minutes.
- **Rollback**: None (re-enabling is intentional after resolution).

---

#### **7. Scale Workers Back to Normal**
- **Action**: Reduce worker replicas to baseline after queue is cleared.
- **Command**:
  ```bash
  kubectl scale deployment/reward-worker --replicas=3
  ```
- **Who**: Bryce Harmon (M10).
- **Verification**: Queue stable at **0**.
- **Rollback**: None (final step).

---
### **Unclear Steps (Needs Confirmation)**
- **Step 3 (Clear Dead Set)**: No explicit verification in thread. Assume successful based on queue depth reduction, but confirm with team if needed.

---
### **Summary of State Changes**
| Step | Action | Rollback Command |
|------|--------|------------------|
| 2    | Disable enqueue | `bin/rails runner 'FeatureFlag.enable(:auto_recognition_enqueue)'` |
| 4    | Scale workers up | `kubectl scale deployment/reward-worker --replicas=3` |

datadog-incident-timelinePASS 1.00

Plain-English question: Sequence the failure from the logs — what broke first, and what cascaded after it?

Prompt — what the model saw

From the log slice, sequence the failure: first error with timestamp, the cascade in order, the service and job involved, and the Datadog query that would confirm the first error. State what the logs do not show.

cost $0.0010 · 25s · in 2,195 / out 1,202 tok

✓ first_error_ts✓ service_named✓ cascade_ordered✓ query✓ gaps_stated

✓ Correct Answer (ground truth)

Plain-English answer: A reward-service Redis timeout at 14:01:12Z cascades to sidekiq at 14:01:40 and api-gateway at 14:03:05, with the Datadog query that confirms the first error.

The answer in one line: job_class_involved = RewardGiveJob

cascade_orderservice=reward-service; first_error_at=2026-09-03T14:01:12Z | service=sidekiq; first_error_at=2026-09-03T14:01:40Z | service=api-gateway; first_error_at=2026-09-03T14:03:05Z | service=web-app; first_error_at=2026-09-03T14:03:30Z

clean_servicespostgres

confirming_queryservice:reward-service status:error "@message:Redis::TimeoutError"

first_errortimestamp=2026-09-03T14:01:12Z; service=reward-service; message=Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s

job_class_involvedRewardGiveJob

logs_do_not_showroot cause of the Redis timeout, redis-server's own logs, infrastructure metrics (CPU/memory/network), any deploy or config change preceding the incident, who applied mitigation or when (no actor fields in logs)

graded checksfirst_error_ts · service_named · cascade_ordered · query · gaps_stated

Model's actual answer

show the model's full answer

### Failure Sequence

1. **First Error (Root Cause)**
   - **Timestamp:** 2026-09-03T14:01:12Z
   - **Service:** `reward-service`
   - **Error:** `Redis::TimeoutError: Connection to Redis at redis-primary:6379 timed out after 5s`
   - **Job:** `RewardGiveJob` (enqueued at 2026-09-03T13:59:30Z)
   - **Datadog Query to Confirm:**
     ```
     service:reward-service Redis::TimeoutError
     ```

2. **Cascade of Errors (Order)**
   - **2026-09-03T14:01:20Z:** `reward-service` - `Redis::TimeoutError: retry exhausted for RewardGiveJob`
   - **2026-09-03T14:01:30Z:** `reward-service` - `Redis::TimeoutError: retry exhausted for RewardGiveJob` (repeated)
   - **2026-09-03T14:01:40Z:** `reward-service` - `Redis::TimeoutError: retry exhausted for RewardGiveJob` (repeated)
   - **2026-09-03T14:01:40Z:** `sidekiq` - `RewardGiveJob failed: Redis::TimeoutError; retrying in 60s`
   - **2026-09-03T14:02:28Z:** `sidekiq` - `RewardGiveJob failed: Redis::TimeoutError; retrying`
   - **2026-09-03T14:02:30Z:** `sidekiq` - `WARN: Queue reward depth above 10,000`
   - **2026-09-03T14:03:05Z:** `api-gateway` - `502 upstream timeout calling reward-service /gives`
   - **2026-09-03T14:03:30Z:** `web-app` - `Give form submission failed: upstream 502 from api-gateway`
   - **2026-09-03T14:03:31Z:** `sidekiq` - `RewardGiveJob failed: Redis::TimeoutError; retrying`
   - **2026-09-03T14:03:48Z:** `api-gateway` - `502 upstream timeout calling reward-service`
   - **2026-09-03T14:04:13Z:** `api-gateway` - `502 upstream timeout calling reward-service`
   - **2026-09-03T14:04:22Z:** `sidekiq` - `RewardGiveJob failed: Redis::TimeoutError; retrying`
   - **2026-09-03T14:04:45Z:** `web-app` - `Give form submission failed: upstream 502`
   - **2026-09-03T14:05:16Z:** `api-gateway` - `502 upstream timeout calling reward-service`
   - **2026-09-03T14:05:26Z:** `sidekiq` - `RewardGiveJob failed: Redis::TimeoutError; retrying`
   - **2026-09-03T14:05:42Z:** `web-app` - `Give form submission failed: upstream 502`
   - **2026-09-03T14:06:47Z:** `sidekiq` - `RewardGiveJob failed: Redis::TimeoutError; retrying`
   - **2026-09-03T14:06:49Z:** `web-app` - `Give form submission failed: upstream 502`
   - **2026-09-03T14:06:52Z:** `api-gateway` - `502 upstream timeout calling reward-service`

3. **Services and Jobs Involved**
   - **Primary Service:** `reward-service` (failed due to Redis timeout)
   - **Secondary Services:** `sidekiq`, `api-gateway`, `web-app`
   - **Jobs Affected:**
     - `RewardGiveJob` (primary job failing due to Redis timeout)
     - `RecognitionDigestJob` (also failing due to Redis timeout, but not part of the direct cascade)

4. **What the Logs Do Not Show**
   - The root cause of the Redis timeout (e.g., Redis server issues, network problems, or high load).
   - Whether other services or jobs were affected beyond those listed.
   - The exact impact on users or downstream systems (e.g., failed transactions, data loss).
   - Recovery steps taken beyond the Redis connection restoration at 2026-09-03T14:22:10Z.
   - Whether the queue depth issue (`Queue reward depth above 10,000`) was resolved or had lasting effects.

feature-flag-state-summaryPASS 1.00

Plain-English question: Summarize what each feature flag controls and who has it on.

Prompt — what the model saw

Summarize the state of the listed feature flags: what each controls per the code excerpt, which companies or segments have it on, targeting rules, and any flag with no code reference or no targets. Company counts must match the export. Do not describe a flag that is not in the export.

cost $0.0006 · 13s · in 578 / out 593 tok

✓ orphans✓ no_targets✓ counts_match✓ no_invented

✓ Correct Answer (ground truth)

Plain-English answer: Recognition streaks on for 42 beta companies, redeem-flow redesign off with a 12-company target list, plus any flag with no code reference or no targets.

The answer in one line: rule = company counts must equal the export; do not describe flags absent from the export.

code_referencedrecognition_streaks_v2, points_budget_guardrails, slack_dm_nudges, redeem_flow_redesign, analytics_dashboard_v3, ms_teams_app_v2

flags{"recognition_streaks_v2": {"state": "on", "targeting_rules": "segment:beta_companies", "company_count": 42}, "points_budget_guardrails": {"state": "on", "targeting_rules": "all_companies", "company_c

flags_with_no_targetspaused_offboard_cleanup

orphan_flags_no_code_referencelegacy_give_modal, survey_boosters_q3, paused_offboard_cleanup

rulecompany counts must equal the export; do not describe flags absent from the export.

graded checksorphans · no_targets · counts_match · no_invented

Model's actual answer

show the model's full answer

Here is the summary of the feature flags based on the provided data:

---

### **`recognition_streaks_v2`**
- **State**: `on`
- **Controls**: Enables the `StreakTracker.record(give)` logic in `app/models/recognition.rb`.
- **Targeting**: `segment:beta_companies`
- **Companies**: **42**

---

### **`points_budget_guardrails`**
- **State**: `on`
- **Controls**: Enforces budget guardrails via `BudgetService.new(company).enforce!(giver, points)` in `app/services/budget_service.rb`.
- **Targeting**: `all_companies`
- **Companies**: **220**

---

### **`slack_dm_nudges`**
- **State**: `on`
- **Controls**: Triggers Slack DM nudges via `SlackDm.send_nudge(user)` in `app/jobs/nudge_job.rb`.
- **Targeting**: `segment:region_na`
- **Companies**: **87**

---

### **`redeem_flow_redesign`**
- **State**: `off`
- **Controls**: Renders `RedeemV2Component` instead of `RedeemV1Component` in `app/controllers/redeem_controller.rb`.
- **Targeting**: `targeted_list`
- **Companies**: **12**

---

### **`analytics_dashboard_v3`**
- **State**: `on`
- **Controls**: Uses `AnalyticsV3.new(company)` in `app/controllers/analytics_controller.rb`.
- **Targeting**: `segment:tier_three`
- **Companies**: **65**

---

### **`ms_teams_app_v2`**
- **State**: `off`
- **Controls**: Installs `TeamsAppV2` via `TeamsAppV2.install(company)` in `app/services/teams_installer.rb`.
- **Targeting**: `targeted_list`
- **Companies**: **9**

---

### **`legacy_give_modal`**
- **State**: `off`
- **Controls**: **No code reference found** in the provided excerpt.
- **Targeting**: `segment:legacy_plan`
- **Companies**: **14**

---

### **`survey_boosters_q3`**
- **State**: `on`
- **Controls**: **No code reference found** in the provided excerpt.
- **Targeting**: `segment:legacy_plan`
- **Companies**: **7**

---

### **`paused_offboard_cleanup`**
- **State**: `off`
- **Controls**: **No code reference found** in the provided excerpt.
- **Targeting**: **No targeting rules specified**
- **Companies**: **0**

---

nda-triagePASS 1.00

Plain-English question: Triage each NDA green, yellow, or red — and quote the clause that decides it.

Prompt — what the model saw

Triage each NDA as GREEN (standard approval), YELLOW (counsel review), or RED (full legal review). Cite the clause that drives each classification by quoting it. Catch embedded non-solicits, non-competes, missing carve-outs, and unusual term or governing law. One line of reasoning per NDA.

cost $0.0005 · 7s · in 594 / out 205 tok

✓ green✓ yellow✓ red✓ non_solicit_caught✓ clause_quoted✓ correct_labels

✓ Correct Answer (ground truth)

Plain-English answer: NDA-1 green (standard mutual terms), NDA-2 yellow for an embedded 18-month non-solicit, with the driving clause quoted for every classification.

The answer in one line: non_solicit_in = NDA-2

checksclassification_per_nda, clause_quotes_exist_in_document, non_solicit_caught, carveouts_checked

classifications{"NDA-1": {"classification": "GREEN", "driving_reason": "standard mutual NDA: 2-year term, standard carve-outs, Delaware law", "driving_clause_quote": "Carve-outs: information that (a) is or becomes p

non_solicit_inNDA-2

graded checksgreen · yellow · red · non_solicit_caught · clause_quoted · correct_labels

Model's actual answer

show the model's full answer

### NDA-1 — **[PARTY A] and Bonusly**
**Classification: GREEN**
Reasoning: Standard mutual confidentiality with clear carve-outs, Delaware governing law, and no unusual clauses.

---

### NDA-2 — **[PARTY B] and Bonusly**
**Classification: YELLOW**
Reasoning: Contains a **non-solicitation clause** ("During the term of this Agreement and for eighteen (18) months thereafter, neither party shall solicit for employment..."), which requires counsel review.

---

### NDA-3 — **[PARTY C] and Bonusly**
**Classification: RED**
Reasoning: **One-way confidentiality with no carve-outs** and a **non-compete clause** ("For a period of three (3) years following the Effective Date, Recipient shall not... engage in or provide services to any business that competes..."). Additionally, governing law is **Republic of Ireland**, which may introduce complexity. Requires full legal review.