Compliance & Validation — Documented
HIPAA, SOC 2 Type II
OpenEvidence
AI medical information platform that answers clinical questions with citation-backed summaries of published evidence.
OpenEvidence, founded in 2022 and headquartered in Miami, Florida, provides an AI platform that answers clinical questions with summaries grounded in and cited to published medical literature, used by clinicians at the point of care and free for verified U.S. clinicians. OpenEvidence documents HIPAA compliance and SOC 2 Type II certification. For pharma and biotech teams its relevance is primarily in medical affairs and evidence engagement; it is not a validated GxP system. OpenEvidence raised a $250 million Series D in January 2026, bringing total funding to roughly $700 million.
- Headquarters
- Miami, United States
- Founded
- 2022
- Compliance
- HIPAA, SOC 2 Type II
- Deployment options
- cloud
- Website
- https://www.openevidence.com
- https://www.linkedin.com/company/openevidence
Categories
Vendor Scorecard
71% — Complete coverage. Coverage score — how completely Atlas documents OpenEvidence across 7 buyer-evaluation areas. Reflects available Atlas data, not solution quality.
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
Large Pharma, Top 20 Pharma, Academic / Research, Medical Affairs
Solution Scope — Documented
Natural Language Processing
Company Viability — Documented
Series D, $700M raised, Founded 2022
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
- How are answers grounded and cited, and how current is the underlying literature?
- What guardrails exist around off-label or unsupported clinical claims?
- How is clinician usage data handled and protected?
- What engagement options exist for medical affairs teams?