Ontology-Driven Ecosystem for Biomanufacturing Data

See how we built a GxP-compliant, Ontology-Driven Data Lakehouse for a bio-pharma leader. Discover how we unified data silos and saved 75% time on OPV reporting.

Solution

Enterprise AI

Industry

Bio-pharmaceutical / Manufacturing

Scope

Data Lakehouse / Semantic Modeling / System Architecture / Validation

Timeframe

7 months

75%

saved time for OPV report generation

hours to deploy data platform from scratch

team members

The client

A leading European bio-pharmaceutical manufacturer operating in a highly regulated production environment (GxP).

Business needs

The primary objective was to establish a Unified Data Platform to serve as the "Single Source of Truth." The client needed to break down data silos between SCADA, LIMS, and paper records to replace manual compilation with near real-time analytics for Ongoing Process Verification (OPV).

The challenge

01

Source Heterogeneity — Integrating vastly different data structures, from transactional SQL databases to high-density time-series data from OT sensors.
02

Regulatory Compliance — Ensuring every data operation is fully auditable and validated according to strict GxP requirements.
03

Restrictive Environment — Building high-performance infrastructure in isolated on-premise networks requiring precise offline dependency management.
04

Validation Continuity — Creating an environment that ensures Data Integrity at every stage – from ingestion to final reporting.

Our solution

Semantic Process Model

Using ontologies to map physical production parameters to universal business classes for rapid onboarding.
Streaming Readiness

A hybrid architecture designed for both batch processing and continuous real-time data ingestion.
Modular Transforms

Pre-validated processing components that automatically align source data with the target business model.
Rapid Deployment

Full containerization allowing the complete deployment of platform components onto infrastructure in just 4 hours.

Protégé (Ontology)
Apache Spark
Kedro (Pipelines)
Apache Airflow
Delta Lake
Kubernetes (k3s)

The outcome

The project delivered a validated data engine that transforms raw process information into business value. It enables a unique correlation between R&D parameters and mass production, serving as the foundation for the client's upcoming Digital Twin project.

Efficiency Gains — Reduced multi-week compilation processes for OPV reports to a fraction of the time, saving 75% of the effort.
Eliminated Silos — Successfully merged data from SCADA, LIMS, and ERP systems into a unified view of every production batch.
On-Demand Scalability — New production areas can be onboarded without rebuilding IT foundations thanks to reusable ontological models.
Full Compliance — The system ensures full auditability and data integrity, confirmed by a comprehensive GxP validation package.

What we implemented

Logic Separation — Business rules reside in the Ontology, not the code, allowing experts to manage the model effortlessly.
Automated Lifecycle — The system automatically recognizes relationships between objects (e.g., LIMS Samples vs SCADA Batches).
Advanced Analytics — Moving beyond "raw joins" to deliver data in full business context, enabling future Digital Twin capabilities.
Hybrid Handling — A unified model capable of handling both batch-oriented data and real-time streaming simultaneously.