Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
Graph AI
AI-native pharmacovigilance platform (Graph Safety) unifying case intake, processing, signal detection, and aggregate reporting.
Graph AI, founded in 2024 and headquartered in Pleasanton, California, develops the Graph Safety platform, an AI-native pharmacovigilance system that aims to unify source intake, individual case processing, aggregate reporting, signal detection, and regulatory compliance in one workflow with human-in-the-loop review. The company raised a $3M seed round led by Bessemer Venture Partners in 2025 and has publicly discussed a subsequent Series A. It is an early-stage company; efficiency figures are vendor-reported and should be validated on your own data. Atlas found no independently verifiable security-certification documentation on its public site.
- Headquarters
- Pleasanton, United States
- Founded
- 2024
- Deployment options
- cloud
- Website
- https://graphsafety.ai
- https://www.linkedin.com/company/graphsafety
Categories
AI Use Cases
- Case Processing Automation
- Signal Detection & Aggregate Reporting
Vendor Scorecard
57% — Partial coverage. Coverage score — how completely Atlas documents Graph 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
Biotech Startup, Mid-Size Biotech, Large Pharma, Pharmacovigilance
Solution Scope — Documented
Adverse Event Detection, Natural Language Processing
Company Viability — Documented
Seed, $3.0M raised, Founded 2024
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 case-processing and signal-detection steps are automated versus human-reviewed?
- What computer-system-validation and 21 CFR Part 11 documentation is available?
- Can you share an audited SOC 2 report or ISO 27001 certificate and its scope?
- What production quality metrics exist from deployments comparable to ours?