AI agents for biostatistics and trial design
Statistical programming consumes weeks per trial, your team is permanently over-allocated, and headcount is not the lever — a senior biostatistician is a scarce and costly hire. TrialMind generates validated SAS and R from a real SAP — SDTM to ADaM to TLF — inside the toolchain you already use, with an audit trail on every output.
Throughput, without lowering the bar
More throughput from the same team
SAP development, SDTM to ADaM programming, and TLF generation consume weeks per trial while the team stays permanently over-allocated. TrialMind generates validated SAS and R from a real SAP, inside the toolchain your programmers already use.
Trial designs you can defend
Protocol synopsis, eligibility criteria, endpoint selection, and sample size, informed by regulatory precedent and the trial landscape — then simulated against virtual patient populations before you commit resources.
Output that survives the submission
The quality bar is not internal — it is what the FDA holds you to. Every deliverable carries source citations and traceable logic, so the reviewer's question has an answer before it is asked.
Every output is built to be defended
This is the function where the productivity gain is most measurable — and where a hallucination is least tolerable. Both have the same answer.
Built for GxP scrutiny, not retrofitted
Audit trail, source citations, and traceable logic on every output — designed from the start for the scrutiny a submission attracts.
Validated SAS and R in your toolchain
Code your programmers can read, review, and sign off, following your conventions. Nobody is asked to abandon SAS or R.
Integrates with what you run today
Connects to the EDC, CTMS, and analytics systems already in place, and deploys where your trial data already lives.
Why not just use a general-purpose model?
Your team can stand up general-purpose tooling in weeks — that option is in the room whether or not anyone names it. The difference is not raw capability. It is whether the output can be defended when a regulator asks how a number was derived.
Run TrialMind against one of your own SAPs and compare the output to what your team produced. That comparison is the pitch — we would rather you test it than take our word for it.
Frequently Asked Questions
How does this fit our GxP validation and QA sign-off?
TrialMind produces code and datasets your team reviews and validates under your existing SOPs — it does not ask you to accept an unreviewable output. Every deliverable carries an audit trail from source data through to the final table, which is what your validation and QA process needs to sign off on.
Does it work in SAS, or does everything move to R?
Both. TrialMind generates SAS and R inside your existing toolchain and follows your macro standards and SAP structure. The point is to augment the workflow your programmers already have, not to migrate them onto something new.
Does this replace biostatisticians?
No. It removes the programming and assembly work that consumes weeks per trial, so a scarce and costly team spends its time on design, interpretation, and defending the analysis. Headcount is rarely the available lever; throughput per statistician is.
How do we know it is better than a general-purpose model at this?
Run it against one of your own SAPs and compare the output to what your team produced. Our benchmarks on clinical programming and trial design tasks are published and independently checkable, but your own SAP is the comparison that matters.
Can it follow our SAP structure and macro library?
Yes. Customisation to your SAP structure, macro standards, and validation SOPs is how we deploy by default, not a professional-services upsell.
Where does our trial data go?
TrialMind deploys into your environment and runs where the data already lives. Raw trial data does not move, which is what makes security and data governance review passable.
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From top pharma companies to academic medical centers
