Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
PhaseV
Causal machine-learning platform for clinical trial design, adaptive execution, and real-time analysis.
PhaseV, headquartered in Boston, Massachusetts (with an office in Tel Aviv), builds a causal machine-learning platform that helps biopharma sponsors and CROs design studies, make data-driven adaptations during a trial, and analyze results. The company has raised roughly $65M across seed and a $50M Series A backed by Accel and Insight Partners. Atlas found no vendor-held security certifications documented on its site; buyers should request its current security and compliance posture during evaluation.
- Headquarters
- Boston, United States
- Deployment options
- cloud
- Website
- https://www.phasevtrials.com
- https://www.linkedin.com/company/phasevtrials
Categories
AI Use Cases
- Medical Monitoring & Safety Review
- Protocol Design Optimization
Vendor Scorecard
50% — Partial coverage. Coverage score — how completely Atlas documents PhaseV 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, Top 20 Pharma, CRO / CDMO, Clinical Operations, R&D Leadership, Data Management
Solution Scope — Documented
Real-World Evidence, Trial Protocol Optimization
Company Viability — Partially documented
Series A
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 is the causal ML validated for our therapeutic area and endpoints?
- Which adaptive design types does the platform support, and how are they pre-specified with regulators?
- What security certifications or third-party audits can you share under NDA?
- How does the platform integrate with our EDC and statistical workflows?