The Architecture
Behind the SogodyAI
Data Factory
The SogodyAI Data Factory is built as a modular pipeline designed to continuously ingest, structure, validate, and deliver high-complexity Life Sciences data.
Instead of custom pipelines for every project, the factory operates as a reusable architecture deployed across multiple data environments.
SogodyAI Data Factory
Fragmented Data Sources
1: Ingestion & Harmonisation
Purpose: Continuously ingest fragmented Life Sciences data and normalise it into a unified internal model.
Typical Sources
Typical Sources
Source-aware pipelines synchronise heterogeneous datasets and standardise them into a consistent schema ready for AI structuring and validation.
2: AI-Agent Structuring
Purpose: Convert unstructured pharmaceutical content into structured,
queryable datasets.
Include
Include
AI agents extract entities and relationships from protocols, labels, and publications, transforming free-text biomedical content into structured data objects connected across datasets.
3: Validation & Data Quality
Purpose: Ensure the reliability and consistency of structured datasets
before release.
Technologies
Technologies
Validation agents automatically verify extracted entities against trusted references, ensuring accuracy, traceability, and consistency across domains.
4: Orchestration & Execution
Purpose: Run the factory as a distributed, scalable data system.
Technologies
Technologies
Each pipeline stage runs as an isolated containerised task, enabling parallel processing, fault isolation, and scalable execution across datasets and clients.
5: Output Layer - Data Delivery
Purpose: Deliver analytics-ready datasets into downstream systems.
Delivery Formats
Delivery Formats
Validated datasets are delivered as structured outputs ready for analytics, intelligence, and decision workflows.
1: Ingestion & Harmonisation
Purpose: Continuously ingest fragmented Life Sciences data and normalise it into a unified internal model.
Typical Sources
Typical Sources
Source-aware pipelines synchronise heterogeneous datasets and standardise them into a consistent schema ready for AI structuring and validation.
1: Ingestion & Harmonisation
Purpose: Continuously ingest fragmented Life Sciences data and normalise it into a unified internal model.
Typical Sources
Typical Sources
Source-aware pipelines synchronise heterogeneous datasets and standardise them into a consistent schema ready for AI structuring and validation.
2: AI-Agent Structuring
Purpose: Convert unstructured pharmaceutical content into structured,
queryable datasets.
Include
Include
AI agents extract entities and relationships from protocols, labels, and publications, transforming free-text biomedical content into structured data objects connected across datasets.
3: Validation & Data Quality
Purpose: Ensure the reliability and consistency of structured datasets
before release.
Technologies
Technologies
Validation agents automatically verify extracted entities against trusted references, ensuring accuracy, traceability, and consistency across domains.
4: Orchestration & Execution
Purpose: Run the factory as a distributed, scalable data system.
Technologies
Technologies
Each pipeline stage runs as an isolated containerised task, enabling parallel processing, fault isolation, and scalable execution across datasets and clients.
5: Output Layer - Data Delivery
Purpose: Deliver analytics-ready datasets into downstream systems.
Delivery Formats
Delivery Formats
Validated datasets are delivered as structured outputs ready for analytics, intelligence, and decision workflows.
1: Ingestion & Harmonisation
Purpose: Continuously ingest fragmented Life Sciences data and normalise it into a unified internal model.
Typical Sources
Typical Sources
Source-aware pipelines synchronise heterogeneous datasets and standardise them into a consistent schema ready for AI structuring and validation.