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Platform

Software that searches published studies, patents, and trial listings first—then drafts plain-English answers with the sources attached. Built for fundraising and partnering, not for replacing your scientists or counsel.

For teams raising money or chasing partnerships

When someone new asks a hard question, the answer should not require three days in PubMed.

Most of what a serious outsider needs is already in the public record—journal articles, patents, clinical trial listings, and reputable summaries. This platform keeps that material in a searchable library your team controls. When a question comes in, the system pulls the relevant pages, then writes a draft answer in everyday language with citations—similar to how a junior researcher prepares a memo before the partner reviews it.

Why public-first, not “deal room only”

A classic data room is built for confidentiality. That matters at term sheet. But before you get there—and during the long tail of BD—outsiders are mostly trying to answer: What is the mechanism, what is validated, what is comparable, and why should I believe you can execute? Those answers are overwhelmingly assembled from what is already published. The platform is designed so your narrative and that public record reinforce each other: same concepts, same comparators, same trail of sources—so momentum builds instead of stalling on “send me another PDF.”

How the software works (simple version)

Each document is split into short passages and stored in a way the computer can search by meaning, not just keywords. When someone asks a question, the system finds the best-matching passages first, then a large language model writes an answer using only that retrieved text as its working set. Engineers call that pattern retrieval-augmented generation; you can think of it as mandatory exhibit review before the draft memo.

In practice we use a hosted vector file service and retrieval API (for example OpenAI's) so your team spends time on which sources belong in the library—because that curation step is what separates a serious tool from a gimmick.

What founders get in fundraising mode

  • Faster first meetings that don’t waste cycles. A partner or associate can interrogate mechanism, differentiation, and precedent programs against the same literature spine you care about—before they ever ask you for a bespoke memo.
  • One coherent story across channels. Your evidence-room pages, deck claims, and corpus-backed answers stay aligned on entities, indications, and comparators—so you are not accidentally telling three slightly different stories to three different firms.
  • Legibility without dilution. The goal is not to oversimplify the biology. It is to make complexity navigable—with paths from summary to supporting passages when the room wants depth.

What BD teams get in partnering mode

  • Benchmark and landscape pulls on demand. Comparator modalities, standard-of-care context, and “who else has tried this shape of claim” are exactly the questions that slow outbound—unless the retrieval layer is already warm.
  • Outbound that feels informed, not cold. When your team can cite the same public sources the counterparty already respects, you move from generic intros to specific, defensible hooks.
  • Internal alignment before external outreach. BD, med affairs, and founders can query the same corpus so messaging stays sharp across functions and time zones.

Credibility over hype

The software is only as honest as the documents you place in it. Final judgment on science, regulation, and deal terms still sits with your experts and your counsel. The platform handles the repetitive part: finding and summarizing what is already on the record so those people spend less time on the hundredth version of the same briefing—and more time on the decisions that actually move a round or a partnership.

Evidence for how buyers and allocators behave

Independent work on B2B evaluation, disclosure, and VC process—see tiered sources below. None of it replaces your obligation to be accurate; it explains why a public, well-structured narrative layer is commercially rational.

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