Mareana Batch Genealogy
AI Powered End-to-End Traceability
Mareana Batch Genealogy gives you a complete, real time view of your manufacturing
data connecting raw materials, processes, and finished goods in one place.
pharma teams
See the full story behind every batch.
Mareana Batch Genealogy connects your data, visualizes the relationships and gives you the clarity to act instantly.
Understand Every Batch. Instantly.
Get a complete view of your batch data, so you can trace issues, reduce risk, and act faster.
End-to-End Traceability
Visualize every upstream and downstream relationship across your manufacturing lifecycle.
Interactive Genealogy Graph
Explore batch lineage through intuitive, zoomable, and connected views.
Faster Root Cause Analysis
Identify issues and isolate impact in minutes and not days.
Audit-Ready Reports
Generate regulatory compliant reports instantly with complete traceability.
Secure, Compliant & Scalable
Built for GxP environments with enterprise grade security and scalability.
Universal Data Ingestion
Connect ERP, MES, LIMS, and historian systems into a unified data layer that eliminates silos, normalizes data, and creates a single source of truth for complete batch traceability.
Visualizing Parent Child Relationships in Manufacturing
Quality reviewers, process engineers, and investigators trace any batch, material, or deviation in one place, without flipping through file cabinets, thumbing through paper batch records, or jumping between LIMS, QMS, and ERP to piece the story together.
Open Text Search
Instantly locate a specific lot across millions of data rows.
Expansion
Expand batch nodes into upstream materials and downstream products.
Filtering
Rapidly drill down to pinpoint specific relationships.
LEGACY DATA SUPPORTDigitizing Paper Batch Records and Legacy Data
The system ingests and links data from scanned paper batch records and Certificates of Analysis (COAs). It transforms static paper trails into digital nodes, ensuring complete lineage visibility and auditable traceability even without owning the manufacturing infrastructure.
KNOWLEDGE GRAPH
AUDIT INTELLIGENCE
CFR Part 11 Compliant 
Immutable Audit Logs Accelerated Recall Response and CFR Part 11 Compliance
The module ensures audit readiness by maintaining an immutable log of all process relationships and changes. This enables manufacturers to pinpoint every finished good containing a specific raw material lot instantly, drastically reducing recall scope and providing iron-clad evidence for regulatory bodies.
Immutable Audit Trails
Every change is permanently recorded and locked, ensuring complete data integrity, traceability, and compliance.
Accelerated Recall Traceability
Trace affected batches and materials in seconds to reduce recall impact.
Regulatory Ready Exports
Generate audit ready reports with complete lineage and logs instantly.
WHY KNOWLEDGE GRAPHS WIN
Knowledge Graph Architecture
vs. Traditional Data Lakes
Mareana utilizes an AI driven Knowledge Graph rather than a static database or data lake. This architecture stores genealogy as immutable nodes and edges, automatically linking parent child and peer entities to preserve context and adapt to process changes without manual remodeling.
Standard Data Lake / SQL
Unorganized data ("Data Swamp") with no inherent context.
Rigid models requiring manual SQL rewrites for recipe changes.
Disconnected rows; difficult to see full lineage.
Often struggles with non digital (paper) or siloed OT data.
Complex to validate; often lacks granular change history.
Mareana Knowledge Graph
Immutable nodes and edges preserving causal relationships.
AI-driven extraction automatically links parent-child entities.
End-to-end lineage (Raw Material → Finished Good).
Universal Ingestion meshes digital, OT, and paper records.
Immutable Audit logs with versioned relationships for CFR Part 11.
Frequently Asked Questions
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Ready to Gain Complete Batch Visibility?
Built on a knowledge graph architecture, Mareana links batch data as connected nodes and relationships, preserving context, enabling dynamic traceability, and eliminating the need for manual data modeling.
