LLMs & Knowledge for Life Sciences

Scientific teams are overwhelmed by the volume of new publications, preprints, supplementary data, and competing claims. Nexco builds AI-assisted literature mining workflows that help you rapidly screen large collections of papers, extract the findings relevant to your question, compare evidence across studies, and trace every conclusion back to its source.

The result is not a generic summary, but a usable scientific digest: key papers, structured findings, evidence tables, conflicting results, open questions, and source-backed excerpts that can feed directly into reports, grants, reviews, or internal decisions. Each workflow is adapted to the scientific question at hand and reviewed by a biologist, so speed does not come at the expense of accuracy.

Retrieval-Augmented Generation (RAG) is the technical name for connecting a language model to a private body of documents — your SOPs, protocols, internal reports, past lab notebooks, slide decks — so that anyone in your team can ask a question in plain English and get an answer that cites the source document. Think of it as a private ChatGPT that only answers from material you trust, and always shows its work.

Concrete uses that labs can deploy: onboarding new hires, searching across years of electronic lab notebooks, cross-team knowledge transfer when a key person leaves, and answering recurring protocol questions without pulling a senior scientist off the bench. We handle the full pipeline — ingestion, embeddings, retrieval, a simple chat interface, and on-premise or private-cloud deployment if your data can't leave your network.

An AI agent is a system that takes actions, not just produces text. In a lab context that means software that monitors a sequencing run and re-launches it on failure, triages incoming samples by metadata and routes them to the correct pipeline, drafts a first-pass analysis report and posts it to your Slack, or scans a panel of databases every morning and emails you anything new on your target.

We design and deploy agents that plug into the tools your lab already uses — LIMS, electronic lab notebooks, instrument APIs, Slack or Teams, your cloud storage. The right starting point is usually one well-scoped task that currently costs your team hours per week; once that pattern proves itself on your operation, the agent portfolio can grow from there.