AI-Powered Review
AI validates every parameter and document so your team can focus on what needs attention.
Pharmaceutical QA teams spend 60–70% of their review cycle on manual validation, cross checking ranges and hunting through siloed data.
Data Manually Reviewed
Exception Reviewed By You
An AI rule engine ingests data from LIMS, ERP, and batch records, validates hundreds of parameters against specifications and historical trends, then surfaces only critical exceptions in a color-coded dashboard for human decision.
Connectors pull parameter values from LIMS, ERP, and scanned documents via AI-OCR, ensuring all batch data is captured automatically without manual effort.
Ingested data is standardized and mapped into structured formats, making it ready for consistent validation across systems and workflows.
Validation library checks ranges, signatures, and calculations in parallel, ensuring every parameter meets predefined pharma-specific rules and standards.
Proprietary algorithms compare current values against historical distributions, identifying deviations and surfacing only meaningful out-of-trend conditions.
Decision and revisions persist as immutable records for a permanent 21 CFR Part 11 trail, ensuring full traceability of every validation decision and action taken.
Reviewers see parameter cards with source snippets and one-click controls, enabling faster decision-making by focusing only on flagged exceptions.
From data ingestion to exception-based review, Mareana automates review, surfaces critical issues, and ensures full traceability, so your team can review faster with confidence.
AI validates every parameter and document so your team can focus on what needs attention.
Only critical issues are surfaced, prioritized, and explained with AI-driven insights.
All critical information in one place for faster understanding and confident decisions.
Every action is logged. Every decision is traceable.
Make faster, audit-ready decisions with full transparency.
Batch Review Copilot provides a configurable rule library with Python hooks for molecule-specific calculations, automated signature verification, range checks, and CAPA status validation, all designed for GxP environments.
Executes range checks, signature completeness, and Potency/Yield math via Python hooks in parallel.
Identifies process drift by comparing current batch values against historical distributions.
Model retrains on reviewer decisions under strict validation logging protocols.
Generic tools weren’t built for pharma batch release. Mareana replaces manual rules and fragmented workflows with AI-powered, review-by-exception, purpose-built for QA teams.
Every validation is logged with source data, rules, reviewer actions, and e-signatures—fully audit-ready for global Pharma-CDMO collaboration.
Asset ID: asset-05-dual-approval-workflow
Trace any release decision back to source data in seconds.
Role-based routing ensures accountable and compliant approvals.
Immutable graph edges prevent retroactive modification.
Log training datasets and model versions to prevent 'black box' drift.
Batch Review Copilot integrates with existing LIMS, ERP, and electronic batch record systems. Automated status updates keep processes aligned across systems and maintain uninterrupted workflow execution.
Plug into your existing LIMS, ERP, and MES — no workflow disruption
Answers to common questions about Mareana, integrations, and compliance.
Mareana Batch Review Copilot flags only what needs attention, so your QA team moves faster without compromising compliance.