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AI is a superior way to digitise paper batch records because it understands both the context and structure of the data coming from disparate sources. To ensure accuracy and maintain a human-in-the-loop, Mareana uses a confidence scoring mechanism that unlocks the doors for exception-based review.
After the data is digitised, Mareana checks every value against predefined ranges. When a value is within range and the system is confident in its extraction, that field can be accepted automatically..
Since the accuracy is high and the limits are met, QA reviewers can safely bypass these entries. A flag is raised if a value falls outside the allowed range. As the extraction confidence is high, these deviations are sent to QA for immediate corrective actio
The system also triggers a flag if it has low confidence in its own reading. This ensures that every uncertain extraction receives human verification. Specialised AI agents also look for unstructured exceptions like margin notes or strikeouts.
The platform compares these notes against master batch records and SOPs to determine if they require further justification. For example, it can verify if a documented time extension stays within a validated threshold.
This workflow directs the reviewer straight to these identified flags. The reviewer verifies the exceptions the system found based on the provided evidence. This allows the team to focus their expertise on the data that requires action rather than searching through every page of the record.
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Frequently Asked Questions
Mareana has helped numerous firms in the Pharmaceutical, Chemical, Medical Device, and Industrial Manufacturing industries.
Exception-based review is a quality assurance approach where reviewers focus only on data that requires attention instead of manually checking every field. AI automatically validates high-confidence, in-range values while flagging exceptions, helping QA teams reduce review time without compromising compliance.
AI digitises paper batch records, understands the context of structured and unstructured data, and evaluates extraction confidence. It automatically accepts accurate, in-range values while sending low-confidence or out-of-range data to QA reviewers, making the review process faster and more efficient.
Confidence scoring allows AI to measure how certain it is about each extracted value. High-confidence data that meets predefined limits can be accepted automatically, while low-confidence extractions are flagged for human verification to ensure data accuracy and regulatory compliance.
AI checks extracted values against predefined ranges and uses specialised AI agents to detect unstructured exceptions such as handwritten notes, strikeouts, and annotations. It also compares these findings with master batch records and SOPs to determine whether further review or justification is required.
No. AI supports a human-in-the-loop workflow by automating routine validation while directing QA reviewers to exceptions that need expert judgement. This enables reviewers to focus on deviations, low-confidence extractions, and compliance-critical decisions instead of reviewing every page manually.
AI-powered exception-based review improves efficiency by reducing manual review effort, accelerating batch record verification, increasing data accuracy through confidence scoring, identifying structured and unstructured exceptions, and helping QA teams focus on high-risk issues while maintaining regulatory compliance.
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