Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
Hippocratic AI
Developer of safety-focused generative AI healthcare agents for non-diagnostic, patient-facing tasks.
Hippocratic AI, founded in 2023 and headquartered in Palo Alto, California, develops generative AI voice agents for non-diagnostic, patient-facing healthcare tasks such as appointment outreach, medication reminders, and care program check-ins, with a staged safety-testing approach involving licensed clinicians. The company reports HITRUST e1 certification for its agentic platform and serves providers, payors, and pharmaceutical organizations. It is not a validated GxP system, and its clinical-safety claims should be evaluated directly with the vendor. The company raised a $126 million Series C in November 2025, bringing total funding to roughly $404 million, and acquired Grove AI in January 2026.
- Headquarters
- Palo Alto, United States
- Founded
- 2023
- Deployment options
- cloud
- Website
- https://www.hippocraticai.com
- https://www.linkedin.com/company/hippocratic-ai-health
Categories
Vendor Scorecard
43% — Partial coverage. Coverage score — how completely Atlas documents Hippocratic AI 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, Medical Affairs, Clinical Operations
Solution Scope — Not documented by Atlas
Generative AI & LLMs
Company Viability — Documented
Series C, $404M 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 patient-facing workflows are supported, and which are explicitly out of scope?
- How is the safety testing and clinician-review process documented?
- How are conversations monitored, escalated, and audited in production?
- What evidence is available from deployments similar to our use case?