Healthcare and regulated services
AI over documents, where a confident wrong answer is the risk
Retrieval across a large document set, and speech to text for clinical notes, built so the system admits what it does not know.
The problem
People needed answers buried in thousands of documents, and clinicians needed notes captured without typing them. In a regulated setting, a confident wrong answer is far worse than no answer, which rules out the naive approach of handing everything to a model and hoping.
What we built
- Retrieval grounded in the source documents, with the answer traceable back to the passage it came from.
- Explicit handling of the case where the documents simply do not contain the answer, so the system says so rather than inventing one.
- Speech to text for clinical dictation, tuned for domain vocabulary that general models get wrong.
- Data boundaries treated as a first class concern, because in this sector the question of who can see what is the whole ballgame.
The role
Senior engineer on the platform: retrieval design, the transcription pipeline, and the guardrails around both.
The stack
What changed
Answers came back with their sources attached, and the system was trusted precisely because it was willing to say it did not know.
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