AI systems for life sciences.
The workflow pattern we see in this industry, and what we would build for it.
01
Data locks
Clinical data is ready. Medical writers start assembling submission sections by hand from tables and prior documents.
02
Drafts loop
Every section cycles through review after review. The calendar, not the science, sets the pace.
03
Safety reports triaged
Adverse-event reports are classified one at a time for severity and reportability. Volume keeps growing.
04
Records reviewed by hand
Batch records and deviations are reviewed page by page. Quality staff read for completeness, not signal.
A regulated drafting and triage agent.
An agent that assembles first-draft submission sections from source data, classifies safety reports for human confirmation, and pre-reviews batch records for completeness. Built to GxP discipline: validation documentation, audit trails, and human sign-off on every regulated decision. The agent removes the assembly work, not the accountability.
10 to 14 weeks from kickoff to a live production system. We stay through adoption.
Eval harness
Checks agent outputs against a test set before release.
Reliability gates
Hard checks the system must pass before it ships.
Live production system
The working system in your stack, not a report.
Adoption Mile
To the 70 percent weekly-active bar by day 90.
Medical writer time on first-draft assembly
50 to 70 percent
IllustrativeAverage cost to bring a new drug to market
Above $2 billion
IllustrativeSubmission cycle compression from drafted-first work
Varies by document type
Illustrative
Ranges are industry observations for orientation. They are not ClearForge results. Our results live on the proof page.
See the proofStart where every engagement starts. The fixed-fee Diagnostic maps this workflow in your regulatory team in 2 weeks.
Starting point · Forge Diagnostic