AI infrastructure for biotech

Science doesn't hold still. A finding gets superseded, a result fails to replicate, a preprint overturns something everyone relied on, and a research tool that treated the literature as fixed is out of date the moment the field moves. If you're building research tools for biotech or life sciences, the hard part isn't reading papers, the model does that well, it's keeping what the system knows current as the underlying science changes.

This page is for developers and teams building those tools. Exabase gives you extraction from papers and reports through Extract, a memory layer that resolves contradictions as findings change, and Workers that re-process updated sources on a schedule, so the literature database your product runs on keeps up with the research rather than freezing at ingestion. Whether you're building for an internal research team or shipping a tool to many labs, each can have its own isolated, self-updating knowledge base.

What you can build

Biotech research tools tend to be one of a few shapes, each built on infrastructure that already exists.

A literature-tracking agent that reads papers, resolves contradictions as new findings arrive, and keeps an up-to-date picture of the state of a question rather than a static index. That's the research agents with evolving knowledge pattern: Extract for papers, Memory for contradiction resolution, Workers for re-processing, Deep Search across everything read.

A self-maintaining literature database that absorbs new publications as they appear and prunes what's been superseded, without anyone manually re-ingesting, the self-maintaining knowledge bases pattern applied to a research corpus.

A paper and report processing pipeline that turns PDFs of papers, filings, and lab reports into clean, searchable text at volume, the document extraction at scale pattern.

A cross-corpus research assistant that searches everything the system has ever read by meaning, surfacing relevant findings regardless of the terminology each paper used, deep search over an evolving body of literature.

Biotech problems, solved

The problems research-tool builders run into are specific, and each has an answer.

Knowledge that goes stale. A static index is wrong the moment the science moves. Memory resolves contradictions through entity resolution: when a new finding overturns an earlier one, the new state supersedes the old rather than sitting beside it, so the system reasons from current knowledge and can flag that a prior result was superseded. This is the difference between a memory layer and a store that just accumulates papers.

Keeping up without manual re-ingestion. New papers appear constantly and sources get revised. Workers run on a schedule and re-process changed sources and pull in new ones, so the literature database stays current on its own rather than only when someone runs ingestion.

Getting text out of papers. Papers, preprints, and reports are PDFs, often long and densely formatted. Extract turns them into clean text chunked with page references, so a synthesis can cite where a claim came from, and it handles the volume a real literature involves.

Finding a concept across papers that name it differently. The same idea travels under different terms across the literature. Deep Search matches by meaning rather than keyword, so a query surfaces relevant findings regardless of each paper's terminology, and holds quality as the corpus grows past where naive search collapses.

The infrastructure underneath

Four primitives carry most biotech research tools. Extract turns papers and reports into clean, searchable text. Memory holds findings and resolves contradictions as the science changes. Workers keep the literature current by re-processing sources on a schedule. Deep Search finds relevant work across the whole corpus by meaning. Bases isolate per team or per customer if you're shipping to multiple labs. All through one API key, rather than four services you assemble and keep aligned.

Knowledge that gets sharper, not staler

A biotech research tool on this foundation gets more valuable in two directions at once, which is the opposite of how a static index ages. The corpus grows: every paper read enlarges what the system can search and synthesise across, and because extraction is paid once, the accumulated library is a lasting asset. At the same time the knowledge stays current, because Workers keep re-processing and Memory keeps resolving contradictions as findings land. A static research index gets staler every day after you build it; this gets both larger and fresher, while the work of running it stays flat. The hard part, the contradiction handling that keeps the picture coherent, is handled rather than something you build from scratch.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: research agents with evolving knowledge, self-maintaining knowledge bases, and document extraction at scale. The topic researcher example is a concrete Worker-driven build, and there's a free tier to build against.

FAQs

How does the system handle a finding that's been overturned?

The new finding supersedes the old one through contradiction resolution. Rather than holding both as equally true, the memory updates to the current state, so the system synthesises from up-to-date knowledge and can flag that an earlier result was superseded.

What keeps the literature database current without manual work?

Workers run on a schedule and re-process changed sources and pull in new papers, so the database stays current on its own. This is the self-maintaining knowledge bases pattern applied to a research corpus.

Can it extract from papers as PDFs?

Yes. Extract reads PDFs and returns clean text chunked with page references, so a synthesis can cite where each claim came from. It handles long, densely formatted documents.

Can it find a concept across papers that use different terminology?

Yes. Deep Search matches by meaning, so a query surfaces relevant findings regardless of the exact terms each paper used, which matters in a literature where the same idea travels under different names.

Will search hold up across thousands of papers?

Yes. Deep Search is built to hold retrieval quality at scale, where naive vector search tends to suffer semantic collapse, so a large and growing corpus stays searchable.

Can I run separate literature databases for different teams or customers?

Yes. Give each its own Base for full isolation, following the multi-tenant SaaS pattern, if you're building a research tool for multiple labs or teams.

Is this a finished research tool or something I build on?

Something you build on. Exabase is the infrastructure, extraction, contradiction-resolving memory, scheduled upkeep, and search, and you build the literature-tracking tool or research assistant on top.