Compliance & Validation — Documented
ISO 27001
N-SIDE
Optimization and AI platform for clinical trial supply forecasting and production planning, reducing drug waste.
N-SIDE, founded in 2000 and headquartered in Louvain-la-Neuve, Belgium, applies mathematical optimization and machine learning to clinical trial supply: forecasting demand, right-sizing drug production, and reducing waste across trial portfolios, with offices in Cambridge, Massachusetts and Tokyo. The company reports ISO 27001 certification and raised a reported $11.8 million round in 2021. Its life sciences business combines software with consulting, so clarify the delivery model; the company also serves the energy sector, though life sciences is a dedicated division.
- Headquarters
- Louvain-la-Neuve, Belgium
- Founded
- 2000
- Compliance
- ISO 27001
- Deployment options
- cloud
- Website
- https://n-side.com
- https://www.linkedin.com/company/n-side
Categories
AI Use Cases
- Supply Chain & Demand Forecasting
Vendor Scorecard
71% — Complete coverage. Coverage score — how completely Atlas documents N-SIDE 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, Clinical Operations
Solution Scope — Documented
Supply Chain Optimization
Company Viability — Documented
Series B, $11.8M raised, Founded 2000
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 much drug waste reduction is realistic for our trial mix, and how is it measured?
- What share of an engagement is software versus optimization consulting?
- How does the platform integrate with our RTSM and depot network?
- How are forecasts kept current as enrollment assumptions change mid-study?