Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
Perceptic
AI platform that connects drug-discovery and clinical data, acting as a connective layer across a pharma organization's tools.
Perceptic, founded in 2024 and headquartered in London, United Kingdom, is building an AI platform positioned as connective tissue between discrete AI tools and the internal and external data pharmaceutical companies use across the drug-development lifecycle, harmonizing data and applying AI agents to support decision-making from discovery toward clinical development. The company raised a $12M seed round in 2026 led by Accel. As an early-stage platform, its production maturity should be assessed directly; Atlas found no vendor-held security certifications documented on its site.
- Headquarters
- London, United Kingdom
- Founded
- 2024
- Deployment options
- cloud
- Website
- https://www.perceptic.com
- https://www.linkedin.com/company/perceptic
Categories
Vendor Scorecard
57% — Partial coverage. Coverage score — how completely Atlas documents Perceptic 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
Biotech Startup, Mid-Size Biotech, Large Pharma, Top 20 Pharma, R&D Leadership, Data Management
Solution Scope — Documented
Natural Language Processing
Company Viability — Documented
Seed, $12.0M raised, Founded 2024
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 data sources and AI tools does the platform integrate with today?
- How is data harmonized and governed across internal and external sources?
- How are AI-agent outputs validated before informing decisions?
- What security and data-residency documentation is available for evaluation?