How we validate
Built to be signed off.
The hard question in regulated pharma is not whether AI can write a draft. It is how you know the draft is good enough to ship, and how you know a commercial programme really earned what it claims.
Authoring: we test the checker before it checks your work
1
Start from approved contentA golden set for every agent and output type: dossiers, brand plans, marketing assets.
2
Plant the errors real reviewers findHallucinations, factual slips, formatting and visual errors, each logged with its type and location as an answer key.
3
Run the evaluation agent several timesMultiple passes per record, keeping every flag, so model randomness doesn't let an error through.
4
Measure the catch rateThe catch rate on planted errors shows how reliably the checker identifies known failure types before human review.
Commercial: we measure incremental impact
1
Match 1:1On geography, specialty, territory potential, baseline prescriptions and rep tenure.
2
Difference-in-differencesBefore versus after, across both groups, with double machine learning to help account for observed differences.
3
Price itIncremental prescriptions and days of therapy, translated into revenue.
4
Hold across cutsBy rep type, doctor decile and territory, run with an independent data partner.
Runs in your cloudAWS or Databricks, through your model gateway and guardrails.
Patient data stays putEMR-to-claims matching on privacy-safe tokens. Nothing leaves your perimeter.
Experts stay in chargeEvery regulated output is reviewed, edited and signed off by your team.
Full audit trailEvery step, source and approval recorded.
White paper · Coming soon
Evaluating agentic systems in regulated pharma workflows
What to measure, at which stage, and who signs off, drawn from multi-agent authoring running in production.