Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
Triomics
Oncology-focused AI platform that reads longitudinal patient records to automate trial matching and chart abstraction at cancer centers.
Triomics, founded in 2021 and headquartered in New York, builds oncology-specific AI that reads the full longitudinal patient record — including unstructured notes — to support clinical trial matching, eligibility pre-screening, and chart abstraction. The company works with major academic cancer centers and raised a $22M Series B in May 2026. Its privacy policy documents how it handles HIPAA-regulated health data; Atlas found no vendor-held security certifications documented on its site.
- Headquarters
- New York, United States
- Founded
- 2021
- Deployment options
- cloud
- Website
- https://www.triomics.com
- https://www.linkedin.com/company/triomics
Categories
AI Use Cases
- Clinical Data Management
- Patient Recruitment & Enrollment
Vendor Scorecard
50% — Partial coverage. Coverage score — how completely Atlas documents Triomics 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, Academic / Research, Clinical Operations, Data Management, R&D Leadership
Solution Scope — Documented
Natural Language Processing, Patient Recruitment
Company Viability — Partially documented
Series B, Founded 2021
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 extraction and matching accuracy measured on our patient population and document types?
- Which EHR systems can the platform ingest, and what does integration require?
- What security certifications or third-party audits can you share under NDA?
- Which cancer center deployments can serve as references for our use case?