AI agents for biotech: literature, SOPs and lab admin handled

Where a small biotech loses time

A seed or Series A biotech may have a dozen people and a pipeline of experiments. The expensive hours are those of the scientists, yet they spend part of each week on tasks that are not science: tracking new papers on a target, finding which version of a protocol is current, checking whether a reagent is in the freezer, and answering a vendor about a quote.

Larger groups have the same issue with more people and more systems. Documents live in a quality management system, a shared drive and a lab notebook, and the search in each is weak. An agent that can read across them and answer with a citation saves real time, as long as it never writes into a controlled record on its own.

What we build

• A literature-watch agent that scans PubMed alerts and preprint feeds for your targets and compounds, groups results, and writes a short digest with the key claim and a link for each paper.
• An SOP and protocol assistant that finds the current approved version, quotes the relevant step and notes when a newer revision exists.
• An inventory and reorder agent that reads a stock sheet or a lab-management export, flags items below a threshold and drafts the purchase request for the lab manager.
• A vendor and quote agent that reads supplier replies, extracts price, lead time and catalogue number, and logs them in one comparison sheet.
• A meeting-notes agent for project and cross-functional meetings that produces minutes and action items by owner.

Tools vary by company. For example, teams may use Benchling, LabArchives, Notion, Google Drive or SharePoint. We use official APIs or exports where they exist, and we state clearly where a vendor's terms limit automation.

Where an agent fits in a regulated lab

If your work falls under FDA good practice regulations or similar rules elsewhere, check with your quality and regulatory leads on whether and how AI tools can be used around those records. We design the agent to stay outside the controlled system of record unless your quality team approves otherwise.

Intellectual property and data

For a biotech, the pipeline is the company. Sequences, assay results and strategy documents must not leak or end up in a model provider's training data. We select providers with enterprise terms that exclude training on your inputs, keep the index inside your own cloud account, and give each agent the minimum access it needs. Every query and retrieval is logged.

Investors and partners often ask about this in diligence. We prepare a one-page data-flow note and a list of subprocessors you can share. Our AI use policy template helps you write the staff rules that go with it.

How the project runs

1. Pick a task with a clear ownerOften the literature digest, because it touches no controlled records. We agree the topics and sources.
2. Test with scientistsTwo or three team members rate digests for a couple of weeks. Their ratings shape the prompts.
3. Add document searchOnly approved, current SOPs are indexed, with version numbers.
4. Review with qualityYour quality lead reads the logs and decides what else the agent may touch.

We work from Pune, with offices in Coventry and New York, and match your lab's time zone for calls.

What it costs

Projects start at $1,500 and our rate is $30 an hour. A literature digest or SOP search is a small project. The price depends on the number of document sources, access controls, validation evidence you require and hosting. Our fixed quote arrives within one working day of a short call. Read what an AI agent costs for general ranges and AI agent development for our method. For pharma manufacturing and distribution, see AI automation for pharmaceutical companies.