Compliance & Validation — Documented
SOC 2 Type II, ISO 27001, 21 CFR Part 11
AlphaLife Sciences
AI-first AuroraPrime platform for authoring clinical, regulatory, and safety documents with human-in-the-loop workflows.
AlphaLife Sciences, founded in 2020 with its U.S. headquarters in Iselin, New Jersey and offices in Singapore, provides AuroraPrime, an AI platform for authoring and quality-checking clinical, regulatory, and safety documents — including aggregate safety reports — with a human-in-the-loop model, and has announced a Veeva RIM integration and product partnership. The company states that it operates within 21 CFR Part 11, SOC 2 Type II, and ISO 27001 frameworks; confirm which claims are backed by current third-party audit reports versus self-assessed during evaluation.
- Headquarters
- Iselin, United States
- Founded
- 2020
- Compliance
- SOC 2 Type II, ISO 27001, 21 CFR Part 11
- Deployment options
- cloud
- Website
- https://alphalifesci.com
- https://www.linkedin.com/company/alphalife-sciences
Categories
AI Use Cases
- Regulatory Submission Authoring
Vendor Scorecard
64% — Partial coverage. Coverage score — how completely Atlas documents AlphaLife Sciences 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, Pharmacovigilance, Regulatory Affairs, Medical Affairs
Solution Scope — Documented
Natural Language Processing, Regulatory Submission Authoring
Company Viability — Partially documented
Private, Founded 2020
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 compliance claims are third-party audited versus self-assessed?
- Which document types (aggregate safety, CSR, submissions) are most mature?
- How does the human-in-the-loop review workflow operate in practice?
- How is content grounded to prevent fabricated statements in documents?