Compliance & Validation — Documented
SOC 2 Type II, ISO 27001, 21 CFR Part 11
TetraScience
Scientific data and AI cloud that turns lab instrument data into harmonized, AI-ready datasets for biopharma R&D.
TetraScience, founded in 2014 and headquartered in Boston, Massachusetts, provides the Tetra Scientific Data and AI Cloud — a platform that collects data from lab instruments and informatics applications, engineers it into a harmonized, AI-ready format, and makes it available for analytics and AI across biopharma R&D. The company maintains ISO 9001 and ISO 27001 certifications and SOC 2 Type II attestation, and offers a dedicated GxP package with a 21 CFR Part 11 audit trail for regulated deployments.
- Headquarters
- Boston, United States
- Founded
- 2014
- Compliance
- SOC 2 Type II, ISO 27001, 21 CFR Part 11, GxP
- Deployment options
- cloud
- Website
- https://www.tetrascience.com
- https://www.linkedin.com/company/tetrascience
Categories
Vendor Scorecard
50% — Partial coverage. Coverage score — how completely Atlas documents TetraScience 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, IT / Digital, Data Management, R&D Leadership
Solution Scope — Not documented by Atlas
Data & AI Infrastructure
Company Viability — Partially documented
Series B, Founded 2014
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 of our lab instruments and informatics systems have supported connectors?
- How does the GxP package support validation for our regulated workflows?
- Where does the data live, and how does it flow into our analytics and AI stack?
- What does onboarding and data engineering look like for a new lab?