SignalExtract turns inconsistent PDFs, emails, and reports into structured, evidence-linked signals — findings, recommendations, amounts, and more — with a hybrid engine and human review you can actually trust.
Patient seen on March 14, 2026. BP remains elevated; recommend adjusting antihypertensive therapy. Follow-up with Dr. Amaka Okafor in two weeks. Dx E11.9.
Extracts signals like
Built for messy, real-world text
Inconsistent formatting, ambiguous language, implied recommendations — handled by layering deterministic rules, LLM understanding, grounding, and review.
Each extracted signal links back to a verbatim source span with offsets. No black-box outputs — claims that can't be grounded never survive review.
Deterministic rules and LLM understanding, merged. If the model is offline or over quota, rule-based extraction still delivers. Never dependent on one path.
Calibrated confidence routes uncertain signals to a fast approve, reject, or edit queue — so reviewers spend time only where it matters.
Typed signals with full provenance — document, page, span, method, confidence — exportable to JSON or CSV for any downstream system.
How it works
Generate candidates broadly, then ground and verify strictly — the arc that turns inconsistent extraction into something you can ship.
“Rule-based was too brittle. Strict pipelines failed on variability. Basic LLM extraction was inconsistent. SignalExtract is the approach that works in practice — broad recall, grounded evidence, and review where it counts.”
The problem this was built to solve.
Upload a document, run hybrid extraction, and review evidence-linked signals — in a workspace that looks as good as it works.
Open the workspace