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AI-Enabled Data Integration and Quality Management for Agile Pharmaceutical Manufacturing

At Continua, we’ve built a real working platform using AI, Unified Namespace (UNS), Knowledge Graphs, and Lean QMS. The result? A structured, real-time data foundation that speeds up tech transfers, reduces QA workload, and aligns with FDA initiatives like KASA and PQ/CMC.

No more siloed data.
No more manual traceability.
No more guesswork during regulatory filings.

We’re helping compress the timeline from plant design to FDA approval while enabling distributed, onshore manufacturing. Learn how by reading our white paper below.

READ THE WHITE PAPER

Authors


Paul Brodbeck
Chief Technologist


Anthony DeBiase
Director, Digital PD


Brian Sauerborn
Engineering Manager, DX

MORE INSIGHTS & STORIES

AI-Enabled Data Integration and Quality Management for Agile Pharmaceutical Manufacturing

Learn how we’ve built a structured, real-time data foundation that speeds up tech transfers, reduces QA workload, and aligns with FDA initiatives.

Implementing Manufacturing Workflow Automation

Embracing digital transformation in life sciences manufacturing organizations can enhance overall operational efficiency.

The Next Step in Your Digital Strategy

Learn how to assess and build your DX strategy with a Digital Transformation Maturity Assessment.

The Ideal Architecture for Digital Transformation

Know the ‘current state’ and ‘future state’ of your business in real time with contextualized data from an enterprise-wide data hub.

The Business Case for Digital Transformation

Digital Transformation can unlock significant value across the entire supply chain and throughout the factory network.

Augmenting an MES

Bringing large amounts of data into your business systems requires the flexibility, security, and growth potential of a Unified Namespace ecosystem.

Integrating Lab Automation Systems

Learn how we integrated Nova FLEX cell culture analysis data with this pharma company’s plant control system to automate required actions.

Monitoring High-Value R&D Materials

This biotech company now monitors and tracks its R&D materials throughout the earliest stages of drug development using a UNS platform.

MES Data Informs Analytics

Explore several ways to extract MES data to support analytics: from hiring an MES engineer to querying the MES recipe, to creating a Unified Namespace.

Ideal Unified Namespace Pilots

Examples: Continuous Process Verification with batch-to-batch comparisons at different locations and Machine Learning with data from multiple systems.