Usable with a quick edit8 of 9 checks passed
The output is evidence-backed and actionable as-is. A PM would only need to add an explicit build trigger, which is a light edit.
Output
Month-end close discovery: findings summary
Bottom line
Reconciliation is a real, recurring pain for mid-sized finance teams, but these eight calls don't yet show which product to build or whether anyone will pay for it. I'd treat this as grounds for a further, more targeted round of research, not a build decision.
What we learned
1. Five of eight leads have a real reconciliation problem (calls 1, 3, 5, 7, 8). Two said close is fine (calls 2 and 6), and one has a tool and regrets it (call 4).
2. They describe different problems, not one. - Bank feeds dropping transactions, matched by hand in Excel (call 1) - Card statements arriving as PDFs and re-keyed, with £14k of duplicates in a quarter (call 3) - Intercompany balances that never agree, costing about 1.5 days (call 5) - Payment processor payouts that net off fees and refunds, and are always the last thing holding up close (call 7) - Sheer matching volume, about 40% of one controller's close (call 8)
No problem appears in more than one call, so we can't yet say which is most common or most valuable.
3. The time cost is meaningful but self-reported. Estimates ranged from 1.5 days to 3 of 8 days. These are recollections, not measurements.
4. Those who are fine tend to be smaller or better equipped. The two "close is fine" leads are the smallest companies in the sample (70 and 95 staff), and one already has a modern ERP. This is suggestive at best with eight calls. The pain sits mostly in the 180–400 staff range.
5. Adoption barriers are as prominent as the pain. - Call 4's failed migration (four months, lost matching rules, team back on spreadsheets) is a warning about switching costs. He would only switch again if someone else did the migration. - Call 8 won't believe automation works until he sees it handle messy data: "Every demo uses clean data." - Call 1 is sceptical: "I've heard that before."
6. Willingness to pay is essentially untested. Only call 1 gave a conditional answer ("if it gave me two days back, it pays for itself"), and she hedged it. Call 6 has a budget freeze. No one expressed urgency or asked about pricing.
7. An adjacent finding: call 2's only complaint was chasing receipts, which sits closer to our existing spend-management product than reconciliation does.
How confident can we be?
| Claim | Confidence |
|---|---|
| Reconciliation is a meaningful time sink for some mid-sized teams | Moderate |
| Pain varies by reconciliation type | Moderate to high (consistent across calls) |
| Size of the time saving | Low (self-reported, varied definitions) |
| Willingness to pay | Low (one hedged signal) |
| Which segment or use case to target | Low |
| Smaller or ERP-equipped firms are a weaker fit | Low (three data points) |
Other limitations: - Eight calls is a small sample, and we don't know how participants were recruited, so existing customers or those already interested may be overrepresented. - The excerpts are edited and one interviewer ran all the calls. - Hypothetical questions ("would you pay?") overstate real intent.
Suggested next steps
- Pick a wedge. Card reconciliation (call 3) is closest to our current product. Follow up with calls 3, 5 and 7 to size how often each problem occurs and what it costs.
- Test with real data. Ask call 8 and similar leads for anonymised, messy samples to see whether matching is feasible.
- Design for migration. Call 4 suggests onboarding and preserving matching rules may decide adoption. Treat this as a product requirement, not an afterthought.
- Test price and urgency. Use concrete pricing or a paid pilot, not hypotheticals.
- Widen the sample. Include firms of 150–500 staff and a mix of ERP and non-ERP setups, and record how each participant was recruited.
Check by check
Mixed · 1
- Addresses the actual decisionIt recommends more research but does not state what specific result or condition would trigger a build decision.The two graders disagreed on this one.
Got right · 8
- Uses the supplied evidence correctlyAll factual statements trace to the supplied calls, with no invented current-situation facts.
- Respects explicit constraintsThe output is a findings summary for the product team and is under the 600-word limit.
- Identifies material uncertaintyIt names unknown WTP, segment, and time savings, and says how to resolve them in next steps.
- Avoids unsupported claimsHypotheses and limitations are labelled as suggestive or untested, not as established fact.
- Produces the required deliverableThe summary is complete, readable, and directly usable by the product team.
- Keeps dissent visibleThe two close-is-fine calls and the CFO's switching regret are kept visible throughout.
- Weights behaviour over opinionIt flags self-reported recollections and hypothetical pricing as weaker than observed behaviours and workarounds.
- Says how many sources support each findingFindings consistently include source counts ('five of eight', 'two said', 'call 4').
Grades and run details
Decision model 100 · LLM judge 9 of 10 checks
Decision model checks
- passUses the supplied evidence correctly60%
- passAddresses the actual decision82%
- passRespects explicit constraints44%
- passIdentifies material uncertainty100%
- passAvoids unsupported claims35%
- passProduces the required deliverable72%
- passKeeps dissent visible99%
- passWeights behaviour over opinion97%
- passSays how many sources support each finding61%
Run
- Run
- #1
- API response time
- 18 s
- Submitted
- 29 Sept 2026