How we helped a healthcare startup increase patient onboarding by 180%arrow_right_alt
Retrieval-augmented Q&A

Upload a document. Ask it questions.

Text is split into passages and embedded, then a question is matched against the most relevant passages and answered from them alone — the same retrieve-then-generate pattern (naive RAG) behind most document Q&A systems, indexed and queried entirely on-device.

schedule Run time Plain text only, 1 MB max. ~25 MB first run for the embedding model, plus a small drafting model. Larger documents take longer to index.
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