Forward-deployed AI engineering for biopharma
Custom AI agents, built inside your team.
We work as forward-deployed engineers: embedded with your organisation, building agents around your data, templates, and standards, and helping you scale AI past the first workflow.
What we help with.
Engineers who sit with your scientists, regulatory writers, and program leads, and build with them rather than for them.
- 01
Build agents for scientific, regulatory, and operational work
Drafting, checking, summarizing, and assembling the deliverables your teams already owe.
- 02
Connect them to your documents and data, safely
Permissioned access to the files a task needs, nothing more, inside the boundaries your IT and QA set.
- 03
Set up review and QC around every output
Assumptions listed, sources cited, and a clear line between draft and final.
- 04
Find where AI earns its place, and where it doesn't
An honest map of your workflows before anything is built.
- 05
Train your people to run and extend what we build
The agents are yours. So is the ability to build the next one.
- 06
Stay on call as the workflow changes
Templates move, standards move, models move. We keep the agents current.
How an engagement works.
Not a product to license. Engineers embedded with your team, who leave you with agents you own and the ability to build the next one.
Step 01
Embed
We join your team for a few weeks, map where AI can carry real work, and pick the first workflow together.
Step 02
Build and calibrate
We build the agent on your data and templates and test it against deliverables your team has already produced.
Step 03
Hand over and scale
Your scientists review every output. Your people learn to run and extend the agent, and we add the next workflow when you are ready.
Built for scientific review, not blind automation.
- 01
Your data, your templates
Agents are built on the files, house style, and acceptance criteria your teams already use.
- 02
Calculations stay deterministic
Numbers come from code you can inspect. The language model drafts and checks; it does not compute.
- 03
Every number cited
Outputs carry assumptions and a citation to the source table for each figure.
- 04
A draft until you approve
Approval, signature, and the record of record stay in your document system. Nothing is sent outside your team.
- 05
Built by practitioners
Drug-development scientists and software engineers who know the difference between a plausible answer and a reviewable deliverable.
Common questions.
What is forward-deployed AI engineering?
- Engineers who work inside your organisation rather than selling you a product. At Clintel, they sit with your scientists, regulatory writers, and program leads, build custom AI agents on your data and templates, and hand them over with the training your team needs to run and extend them.
How do you validate LLM outputs for regulatory documents?
- We do not rely on the language model being right. Calculations run in deterministic code you can inspect; the model drafts and checks. Every figure carries a citation to its source table, assumptions are listed, and each agent is tested against deliverables your team has already produced before it is used. Your scientists review every output, and approval and signature stay in your document system.
Can you build an AI assistant over our clinical and regulatory documents?
- Yes. We connect agents to the documents a task needs through permissioned access, inside the boundaries your IT and QA teams set, and every answer cites the document and section it came from so a reviewer can check it.
Which teams do you build AI agents for?
- Scientific, regulatory, and operational teams across drug development. We start with a workflow where the work is repetitive, the output is reviewable, and a past deliverable exists to test against.
Is Clintel a software product?
- No. Clintel is an engineering practice. You own the agents we build, and your people learn to maintain them and build the next one.
Does our data leave our organisation?
- Agents get access only to the files a task needs. Nothing is sent outside your team, and hosting and data handling are agreed with your IT and QA teams before anything is built.
How does an engagement start?
- With one workflow. Tell us the job you want an agent to take on and, if you can, share a past deliverable; it becomes the ground truth we build and test against.
Start a conversation
Start with one workflow.
Tell us the job you want an agent to take on. We'll come back with how we would scope it, what we would need from you, and what the first deliverable would look like.
- A scientist replies within two business days
- Bring a past deliverable if you can; it becomes the ground truth we build against
- Data handling, hosting, and pricing are covered on the first call