Digital CMC.
Powered by AI.
Genedata Vico™ is an AI-native solution purpose-built to strengthen and accelerate CMC. Scientists can easily create and manage domain-specific data products, apply specialized agentic AI, and use leading AI models to guide confident, dynamic decisions in development and manufacturing. A closed learning loop continuously improves the accuracy and relevance of analytical results as new data and outcomes arrive, while maintaining enterprise-grade security, compliance, and end-to-end traceability.
With Genedata Vico, we shift from a document-centric to a decision-centric approach. Now we can quickly access and analyze our data, run AI/ML models, and move faster toward IND.
Ismael Velasco
Director Global Data & Insights TRD
CMC Data and AI.
At Scale.
- Manage CMC digital assets at enterprise scale, connecting data and models across small- and large-molecule drug development and manufacturing
- Turn experimental, process analytical technology (PAT), manufacturing, and quality data into governed, CMC‑specific data products that preserve scientific context and lineage
- Achieve scalable AI in GxP by connecting experiments, data, models, and decisions into governed, unified workflows
- Enable proactive process decisions with complete, consistent, traceable data
Ask. Analyze.
Decide.
- Use CMC-specific agentic AI to search, analyze, interrogate, and visualize data
- Support human-in-the-loop decision making, ensuring scientific oversight and confidence
- Identify root causes and key drivers of quality attributes at scale
- Combine AI insights with clear data lineage and human oversight, so outcomes stay explainable and defensible
Closed-Loop Learning.
Better Decisions.
- Capture and apply CMC knowledge through a closed, learning AI loop
- Feed results from experiments and manufacturing runs back into models to improve future decisions
- Perform AI-guided design of experiments (DoE) based on historical data — not trial and error
- Detect and anticipate process deviations earlier, using AI-augmented PAT
Many CMC Use Cases.
One Platform.
- De‑risk tech transfer and scale up by reusing traceable process knowledge, models, and decision logic across programs and manufacturing sites
- Prioritize chemical synthesis routes using historical process data and closed‑loop AI to improve cost, scalability, and speed
- Optimize formulation stability and performance by learning from development, scale‑up, and manufacturing outcomes across sites
- Improve inventory and shipment planning by integrating manufacturing, quality, and supply data for proactive, data‑driven decisions
Unified Data and AI
Connecting data, context, and AI across the drug lifecycle
Decision-Ready Data
Context-rich, traceable data products for review‑ready decisions
Interoperability
Across development, tech transfer, and manufacturing — without breaking traceability
GxP Compliance
Governed, auditable AI for enterprise science
Speed to Insight
Understanding process behavior and identifying risk — faster and at scale
