Paper Batch Record Digitization transforms unstructured manufacturing documents (batch records, CoAs, logbooks) into GxP-compliant, queryable data. It uses pharma-tuned AI to extract, contextualize, and link data to a Knowledge Graph for immediate analytics and trending.
The Problem
Paper batch records lock critical manufacturing data away from analytics, CPV trending, and root-cause investigation. Each handwritten entry, checked box, and CoA sitting in a filing cabinet represents data your yield models and deviation workflows cannot access.
Manual transcription introduces errors. Generic OCR fails on pharmaceutical complexity-units like μg vs. mg, scientific notation, cursive handwriting, and multi-column tables produce unacceptable error rates. The result: QA teams spend hours correcting extraction mistakes or abandon digitization entirely.
lost weekly to manual data re-entry
error rates from generic OCR
of historical data locked in archives
A simple, intelligent workflow to digitize, validate, and deliver audit-ready data in hours—not weeks.
Upload batch record PDFs individually or in bulk
Extract structured data from handwriting and complex layouts.
Get verified, traceable data ready for compliance and systems.
Digitization is just the first step. Mareana enables downstream workflows, analytics, and compliance across your batch data ecosystem.
Standardize vendor formats into a unified schema—no manual re-entry.
Unlock years of batch data for trend analysis and process optimization.
Trace every data point back to its source with complete audit trails.
Automatically flag anomalies, deviations, and out-of-spec values.
Pull data from LIMS, MES, and analytics systems seamlessly.
Identify bottlenecks and improve yield with data-driven insights.
Reduction In Manual Effort
ALCOA+ Compliant Transcription
End-To-End Audit Traceability
Data Availability For Review
Scanned documents are processed through pharma-tuned AI-OCR, mapped via configurable templates, scored for confidence, routed for human verification, normalized to structured data, and automatically linked to batch genealogy.
Mareana Processing data...
Generic OCR tools lack pharmaceutical domain training. Pharma-specific vision models reduce errors by up to 80% through domain-tuned transformers.
OCR gives you output. Mareana gives you confidence.
Our platform is designed to process pharmaceutical batch records, helping extract checkboxes, handwritten entries, and complex tables into structured, review-ready outputs.
Identify and verify critical checklist fields like equipment calibration,
line clearance, and material verification.
Accurately extract handwritten entries and correct OCR errors using pharma context.
Extract and organize multi-layered tables while preserving relationships between rows and columns.
Seamlessly connect Mareana with your existing systems—no disruption, no rebuild.
Connect with SAP, Oracle, and MES systems seamlessly.
Sync data with LIMS and quality workflows in real time.
Send structured data to dashboards and BI tools.
Flexible APIs for any system integration.
From QA reviews to data analytics, Mareana helps every team eliminate manual work and move faster with structured, audit-ready data.
Ensure every batch is audit-ready with human in the loop validation. Automated extraction and confidence scoring help reduce risk and accelerate approvals.
Eliminate delays caused by manual transcription and review cycles. Digitize batch records quickly and accelerate batch release timelines across plants.
Turn unstructured batch data into analytics-ready datasets. Feed structured data directly into MES, LIMS, and analytics systems to unlock CPV and trend insights.
Answers to common questions about Mareana, integrations, and compliance.
Connect with our experts to explore how Mareana can support your data, compliance, and integration needs.
Stop leaving critical manufacturing data locked in filing cabinets. Transform historical records into actionable insights for CPV trending.
Explore how AI is transforming batch record digitization, compliance workflows, and data-driven manufacturing in pharma.
