AI scribe accuracy benchmark — United States
These results were graded on the Australian deployment, which runs the same speech recognition and AI drafting stack as the United States. The methodology behind these results, including how each number was measured and its limitations, is in the methodology document.
Headline metrics
Coverage breakdown
Jury composition
Each detail is graded by every juror configured in the panel. Verdicts disagree on hard cases — that's the signal. Per-juror reasoning is shown verbatim below.
Sessions
Click any recording to drill in: source audio, ground-truth transcript, what the STT pipeline heard, every generated document, and every per-LLM verdict with its reasoning.
Methodology & provenance
Word Error Rate, panel-of-LLM jury, prompt-scoped expectations, calibration sanity-check, and corpus graduation roadmap (synthetic → role-play → real-world).
Read the methodology document →The full machine-readable artefact this page renders from. Every number on this page is computable directly from this file.
Open raw data →Corpus, juror prompts, and judge model versions are all version-controlled. Anyone can re-run the harness against the same commit and reproduce these numbers.
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