Compliance & Validation — Documented
SOC 2 Type II, ISO 27001
Seeq
Industrial analytics and AI platform for time-series process data, used in pharma manufacturing and process optimization.
Seeq, founded in 2013 and headquartered in Seattle, Washington, provides an industrial analytics platform for time-series process data, letting engineers and scientists connect to historians and process data sources, clean and analyze data, and share insights. In life sciences it is used for manufacturing process monitoring, batch analysis, and process optimization. Seeq maintains SOC 2 attestation and ISO/IEC 27001:2022 certification, and publishes validation guidance for customers deploying Seeq for Pharma in GxP environments.
- Headquarters
- Seattle, United States
- Founded
- 2013
- Compliance
- SOC 2 Type II, ISO 27001, GxP
- Deployment options
- cloud
- Website
- https://www.seeq.com
Categories
AI Use Cases
- Manufacturing Process Optimization
- Manufacturing Quality Control
Vendor Scorecard
64% — Partial coverage. Coverage score — how completely Atlas documents Seeq 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
Solution Scope — Documented
Manufacturing Process Optimization, Manufacturing Quality Control
Company Viability — Partially documented
Series D, Founded 2013
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 historians and process data systems have supported connectors?
- What validation documentation is available for GxP manufacturing use?
- How do engineers share and operationalize analyses across sites?
- What does the path from ad hoc analysis to continuous monitoring look like?