Life-sciences research.
Traceable evidence.

The volume of relevant literature in any active area exceeds what a person can read, and the practical consequence is not that researchers miss papers at random — it is that they miss the ones outside their usual search vocabulary. A system that surfaces relevant work phrased differently from how you would have phrased it is doing something genuinely useful.

But only if every statement it makes is anchored to a specific passage in a specific paper. Not a bibliography at the end — a span-level link from the sentence to the source. A researcher's first action on any interesting claim is to check it, and a system that makes that check slow will be abandoned regardless of how good its synthesis is.

The second need is structured extraction across many papers: study populations, endpoints, effect sizes, methods, exclusion criteria, pulled into a table you can sort. This is the work that takes weeks by hand and is where the time actually goes in a systematic review.

The system does not assess study quality. It can extract what a paper reports; whether the study was well conducted is a judgement requiring domain expertise, and presenting an extracted effect size without that judgement invites exactly the wrong kind of confidence.

It does not generate hypotheses that should be trusted without verification. It can surface adjacencies and unexpected connections, which is legitimately useful as a prompt for a researcher's own thinking — and every one needs checking, because plausible-sounding connections are precisely what a language model is best at producing.

And it will miss things. Retrieval recall is never complete, and a system presented as exhaustive when it is not is worse than one presented honestly. We build these with the recall characteristics measured on your corpus and stated, so researchers calibrate their reliance correctly.