Peer AI

Generative AI platform for drafting clinical and regulatory documents such as patient narratives and CSR sections.

Peer AI, founded in 2023 and headquartered in San Francisco, California, applies generative AI to clinical and regulatory document drafting — starting with patient safety narratives and expanding toward clinical study report authoring — with human medical writers reviewing and finalizing output. The company has announced venture funding and reports adoption by large pharmaceutical companies, though most customer references are not publicly named. Output quality should be validated on your own study data before scaled use.

Headquarters
San Francisco, United States
Founded
2023
Deployment options
cloud
Website
https://www.getpeer.ai

Categories

AI Use Cases

  • Regulatory Submission Authoring

Vendor Scorecard

57% — Partial coverage. Coverage score — how completely Atlas documents Peer AI across 7 buyer-evaluation areas. Reflects available Atlas data, not solution quality.

Compliance & Validation — Not documented by Atlas

Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?

Deployment & Hosting — Documented

cloud

Pricing Transparency — Not documented by Atlas

Ask the vendor: What is your pricing model, and what drives total cost of ownership?

Implementation & Onboarding — Not documented by Atlas

Ask the vendor: What is a realistic implementation timeline, and what onboarding support is included?

Organization & Role Fit — Documented

Mid-Size Biotech, Large Pharma, Top 20 Pharma, Regulatory Affairs, Medical Affairs

Solution Scope — Documented

Natural Language Processing, Regulatory Submission Authoring

Company Viability — Documented

Seed, $12.1M raised, Founded 2023

Always ask the vendor

Not captured in Atlas data — confirm directly. Excluded from the score above.

Data Rights & IP

Who owns model outputs? Is our proprietary data used to train models others can access, and where does our data reside?

Model Credibility & Explainability

Can you share validation studies or benchmarks, explain how the model reaches its conclusions, and describe how bias and drift are monitored?

Questions to Ask This Vendor

  • Which document types are supported in production today?
  • How does the human review workflow document writer sign-off?
  • How is our clinical data protected and segregated during generation?
  • Can accuracy be measured in a pilot against writer-only baselines?