Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
Datafoundry
AI safety-vigilance platform (DF mSafety AI, mSignal AI) automating case management, signal detection, and literature monitoring.
Datafoundry, founded in 2016 and headquartered in Bengaluru, India with a US office in the Princeton, New Jersey area and an AI lab in Yerevan, Armenia, provides an AI-driven safety vigilance platform for drugs, medical devices, and cosmetics: DF mSafety AI for case management, DF mSignal AI for signal detection, and a literature monitoring product. Public pages emphasize adherence to regulatory requirements but do not document third-party security certifications, and named production customers were not identified at profiling time — request references and compliance evidence directly.
- Headquarters
- Bengaluru, India
- Founded
- 2016
- Deployment options
- cloud
- Website
- https://www.datafoundry.ai
- https://www.linkedin.com/company/datafoundryai
Categories
AI Use Cases
- Case Processing Automation
- Signal Detection & Aggregate Reporting
Vendor Scorecard
50% — Partial coverage. Coverage score — how completely Atlas documents Datafoundry 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
Mid-Size Biotech, Large Pharma, CRO / CDMO, Pharmacovigilance
Solution Scope — Documented
Adverse Event Detection
Company Viability — Partially documented
Private, Founded 2016
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
- Can we speak to production customers running mSafety AI at comparable case volumes?
- What third-party security audits or certifications can be evidenced?
- How does the AI case-processing accuracy get measured and monitored on our data?
- What does migration from our current safety database involve?