Your manufacturing data already exists, so why does getting one trustworthy answer still take hours or even days?
Every batch manufactured generates a trail of critical data, scattered across PDFs, spreadsheets, MES, LIMS, EBR, and partner systems. As production scales, that data becomes harder to collect, validate, search, and use for decisions. The information is all there. It's just never all in one place, in one format, at the moment your team needs it.
A process engineer may know a parameter exists. A quality reviewer may know which batch contains it. An investigator may know where it was recorded. But bringing these pieces together into one trusted view can require hours of manual searching, transcription, and reconciliation.
As teams repeat that work across parameters, records and batches, fragmentation affects more than the time it takes to find an answer.
The cost shows up in three layers:
Operational Inefficiency
Teams lose time collecting, transcribing, and reconciling data across disconnected sources.
Compliance risk
Manual entry and varied formats reduce traceability and complicate audit defense.
Process & Decision Delays
PPQ, investigations, CPV, and quality decisions slow while teams await reliable data.
Introducing CPV-Auto®
CPV-Auto combines AI-assisted technology and human validation to digitize manufacturing information, manage trusted data, analyze process performance and automate internal and regulatory reporting. Biopharma organizations can use the full workflow or select capabilities that complement their existing systems—reducing manual effort, improving data reliability and helping teams maintain process control and reporting readiness.
3X
Faster Processing with AI
100%
Workflow-Validated Data Accuracy
50-70%
Lower Total Cost of Ownership
How CPV-Auto® transforms GMP manufacturing information
From manufacturing information to analysis and reporting
1. Create trusted manufacturing data
CPV-Auto CORE uses AI-assisted intelligent document processing (IDP) to turn paper, scanned and digital manufacturing records—including batch records, certificates of analysis, laboratory records, stability reports and protocols—into structured data. CORE handles handwriting, corrections, changing record versions and multiple languages. Human-in-the-loop validation helps ensure the extracted data is ready for use. Teams can review data collaboratively or receive validated datasets through CORE’s Data-as-a-Service offering.
2. Manage and investigate trusted manufacturing data
CPV-Auto CAM (Customer Access Module) gives teams a central place to track batch and document status, search parameters across records and investigate deviations across batches. Authorized users can preview data against source documents before approval. Role-based access, audit trails and downloadable investigation reports support controlled GMP workflows.
3. Analyze performance and automate reporting
CPV-Auto SRM (Statistical Reporting Module) analyzes approved data from CORE or structured data from customers’ existing systems. Teams can monitor trends and limits, assess process capability and investigate variation through statistical process control and other analyses. SRM also automates charts and reports for internal review and quality or regulatory needs, including CPV and APR/PQR, reducing the manual work required to understand and report on process performance.
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Choose the right starting point
CORE can transform records for a one-time project or as an ongoing service. Teams can receive structured data in a format suited to their own systems or manage it within CPV-Auto. SRM can analyze data from CORE alongside structured data from other sources, or serve as a standalone statistical analysis and reporting tool. This lets organizations use CPV-Auto across the full workflow or alongside their existing MES, LIMS and EBR systems.
Human oversight and control throughout
AI-assisted extraction is paired with human review before data is approved for downstream use. Controlled access, audit trails and electronic signatures support traceability and workflows aligned with 21 CFR Part 11. Client data is used for the agreed workflow and is not used to train CPV-Auto’s AI models.
CPV-Auto is available in validated and non-validated configurations to suit different use cases and regulatory requirements.
Manufacturing teams already have the information they need. CPV-Auto helps make it trustworthy and ready for investigation, process analysis and reporting, without starting each question by searching records and reconciling values manually.
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FAQs
Frequently Asked Questions
Migratr.ai is Aventior's AI-powered data engineering and ETL platform. It covers the full migration lifecycle, ingestion, profiling, exploration, cleaning, transformation, source-to-target mapping, validation, and delivery turning fragmented, multi-source data into standardized, governed pipelines.
Databases, data warehouses, cloud storage, APIs, and flat files including CSV, JSON, Excel, and Parquet.
No. Mapping recommendations are constraint-driven, the AI works inside defined schema rules rather than generating freely and every recommendation goes to a human for review, edit, or rejection before anything moves downstream. Approval gates are part of the pipeline and are also an optional setting.
Real-time validation and preview pipelines let you inspect results before execution, and they flag anomalies and data-loss risks as you build. Transformation logic is type-safe and guardrail-enforced, so a mapping that would break downstream is caught at design time rather than after the load.
It's built with regulated work in mind: versioned mapping rules, audit trails on transformations, rollback capability, and mandatory human approval steps. Teams use it for clinical trial data harmonization across sites and vendors, and for vendor data integration where every change needs to be traceable. For specific validation and compliance requirements, talk to us about your environment.
Across enterprise migrations, teams have cut migration timelines dramatically, pushed error rates close to zero, and seen 60–70% less manual mapping effort. Because mapping rules are saved and versioned, a schema change means adjusting a rule rather than rebuilding the logic so the next data drop doesn't reset the clock.
