Federal Rule of Evidence 702 and Daubert list the known or potential rate of error as an explicit reliability factor. If software touched your methodology, opposing counsel can fairly ask what its error rate is. This page is our answer — every figure with the method behind it, the date it was measured, and an honest statement of what it does not cover.
Each card states the figure, the exact method, the date, and the limits. A number without a method is marketing, so we give you both — including where the method is weaker than we would like.
How accurately the trainer's speech pipeline turns spoken deposition language, including medical and orthopedic vocabulary, into text.
GET /api/diag-roundtrip on the Deposition Trainer backend, implemented in deposition-trainer/backend/src/server.js. The reference sentence, the streaming parameters, and the scoring function are all in that source.The measurement that would actually answer "what is your error rate in a deposition?" We are not publishing a number for it, because we have not finished measuring it.
GET /api/diag-real-audio) streams a 15-second chunk of the publicly published Tolson v. St. Agnes Healthcare deposition — a defense orthopedic surgeon, released as legal-education content, no protected health information — through the same production speech-to-text path and returns the transcript.The Mata v. Avianca failure mode: an AI tool inventing a case citation, a paper title, or a quote that does not exist.
A post-processing layer that reads model output before you ever see it and flags any citation pattern it cannot tie to a source.
node --test shared/hallucination-scrubber.test.js, 35 tests, 35 pass, 0 fail.ANTI-HALLUCINATION RULES).Not a percentage — a design constraint. Every factual assertion in a MedLegal AI output carries the source file and the page it came from.
[filename.pdf p14]-style page cites, PubMed PMIDs, and pinpoint cites into uploaded CVs and prior depositions. A shared verifier component scans the rendered output, counts the citations, and shows a running "Verified: X/Y citations linked to source" banner. Click any citation and the source page opens.Whether you are defending your own methodology or attacking an opponent's, here is which reliability factor each item on this page speaks to — and where we do not have an answer.
| Daubert / FRE 702 factor | What on this page speaks to it | Honest status |
|---|---|---|
| Whether the technique can be tested | Page-level Bates citation on every asserted fact; the fabricated-citation evaluation re-runnable from public inputs; error-rate diagnostics that are live HTTP endpoints rather than claims in a deck. | Strong. Every claim here is checkable by someone other than us. |
| Known or potential rate of error | Speech-to-text word error rate 0.094 under synthesized-speech conditions (2026-08-04, nova-3-medical); 0 fabricated citations across 138 chunks of two public depositions. | Partial. We can state a measured rate — which most vendors cannot — but the courtroom-audio word error rate is pending, and the citation figure is a two-matter sample. Say so plainly if you are asked. |
| Peer review and publication | The audit methodology is published openly and invites third parties to re-run it against us. | Weak, and we will not dress it up. Published is not peer-reviewed. There is no peer-reviewed literature on this software, and you should not suggest otherwise on the record. |
| Existence of standards controlling operation | Fixed production parameters for the speech pipeline; a tight known-authority allowlist in the citation scrubber; a scrubber test suite that runs on every change; documented operating rules for experts on the disclosure page. | Internal standards only. These are our controls, consistently applied and documented — not an external standard body's. |
| General acceptance in the relevant community | The underlying speech and language models are in broad commercial use; page-cited, source-linked output is the direction the legal-AI field is moving after Mata v. Avianca. | Do not overclaim this one. AI-assisted expert workflows are new. There is no general acceptance to assert. The defensible position is the one in the disclosure paragraph: the software organized and cited the record, and you formed every opinion — so the methodology on trial is yours, which does have general acceptance. |
The strategic point: the reliability inquiry should be about your methodology, not the software's. Keeping the software on the "organize and cite" side of the line — and disclosing it — is what keeps it there.
A credibility page that only lists strengths is an advertisement. These are the claims we could make but will not, because we have not measured them.
Daubert v. Merrell Dow and Federal Rule of Evidence 702 list the known or potential rate of error as an explicit factor a court weighs when assessing whether an expert's methodology is reliable. If software touched the methodology, opposing counsel can fairly ask what its error rate is. Most AI vendors have never measured one, so the expert has nothing to answer with.
The Deposition Trainer's speech pipeline measured a word error rate of 0.094 (9.4%) on 2026-08-04 using Deepgram nova-3-medical. That figure comes from a synthesized-speech round trip: a fixed 32-word reference sentence containing orthopedic and deposition vocabulary is spoken by a text-to-speech voice, transcribed, and scored by word-level edit distance. It is a self-measured internal diagnostic on synthesized audio, not a benchmark against real human courtroom audio. A word error rate against human deposition audio with a human-verified reference transcript is pending and will be published here when it is measured.
Every asserted fact in a MedLegal AI output carries the source file and the page it came from, rendered as a clickable citation that opens the source page. That makes the tool's output independently checkable: opposing counsel does not have to take the software's word for anything, and the expert can verify each fact against the record before it enters a report. It speaks to the testability factor, because any assertion can be falsified by opening the cited page.
Yes, and the test is public and reproducible. The published methodology report evaluated 138 chunks across two public depositions — Tolson v. St. Agnes and Crowden v. Emory — using only public inputs, and found zero fabricated case citations, zero fabricated paper titles, and zero fabricated witness quotes. The inputs, prompt, and scoring code are described so a third party can re-run it. Read the methodology →
No. These are litigation-support tools. Nothing on this page is a claim of clinical validation, regulatory clearance, or diagnostic accuracy, and nothing here is legal advice. The figures describe internal measurements of software behavior, with their limits stated.
Whenever a measurement changes and whenever an underlying model changes. Speech and language models are versioned and get replaced; a figure measured on one model does not carry over to the next. Each card is dated so you can tell how fresh it is, and the version history below records what changed.