Where this came from
Most user research dies in the synthesis. A team runs great interviews, then someone turns hours of messy transcripts into themes, usually under deadline. The exact quotes and real pain points, the most valuable part, are the first to slip. So the PM writes users want X and no one can trace where X came from. I built Receipts to fix exactly that.
What it does, end to end
It ingests transcripts, infers each speaker role, and flags low-confidence inferences instead of guessing silently. It handles the unglamorous cases, same-name speakers mislabeled across files, then merges across interviews in parallel and drafts grounded PRD requirements, each one pointing at the evidence beneath it, with gaps named openly.
I measured it against ground truth
A promise like every claim traces to a real quote is only worth making if it is measured. I built an eval to test whether Receipts does what it claims, and to find where it does not. It was a record of testing it honestly, including what broke.
| Metric | Score | Basis |
|---|---|---|
| Quote fidelity | 100% (~60 of ~60 quotes exact-substring) | 6 transcripts |
| Hallucination rate | 0% (0 fabricated quotes) | 6 transcripts |
| Speaker attribution | ~50% to 100% after a fix | 4 real transcripts |
| Distinct-speaker integrity | Pass, no voice collapsed into one | 4 real transcripts |
| PRD grounding + gap honesty | Pass | all outputs |
| Evidence-gap calibration | Finding, not a pass (below) | 2 constructed cases |
The finding I did not plan for
On a first run, two of four interviewees whose transcripts used generic Speaker labels were mis-attributed. I scoped a fix, deployed it, and re-ran: all four now resolve correctly, with zero drift in quote text. Then the eval caught something I did not expect. The evidence-gap flag I documented actually behaves as a filter: weak-signal themes are removed before they can become an unsupported claim, rather than surfaced and marked weak. That gap between documented and actual behavior is on the fix list.
The product point underneath the tool
Synthesis that cannot be traced back to a user should not be trusted to drive a roadmap. Receipts is a bet that traceability is the valuable part, and that AI’s real leverage here is not speed, it is the discipline to keep every claim honest at a scale a human would not sustain by hand.