Purpose-built stores, one lineage
Industrial AI Data Platform
PDICON Intelligence separates transactional records, large artifacts, event streams, semantic retrieval and graph relationships while preserving one project and model lineage across them.
Direct answer
PDICON Intelligence separates transactional records, large artifacts, event streams, semantic retrieval and graph relationships while preserving one project and model lineage across them.
System blueprint
A connected control plane, not a collection of features.
Use relational data for governed operations.
Organizations, users, projects, permissions, workflows, model registry, training jobs, billing and audit records require transactional integrity.
Keep heavy engineering evidence in object storage.
Drawings, videos, PDFs, CAD files, datasets, checkpoints and model artifacts belong in versioned S3-compatible storage.
Use vector and graph systems for different questions.
Semantic retrieval finds relevant text and episodes; graph relationships connect project, equipment, vendor, decision and outcome.
Operating matrix
Evidence moves through explicit controls.
| Subject | Input | Intelligence operation | Human / policy control | Output |
|---|---|---|---|---|
| PostgreSQL | Operational records | Transactional query | Row and tenant policy | Trusted system state |
| Object storage | Large binary artifacts | Versioned storage | Signed access | Immutable evidence |
| Vector retrieval | Documents and episodes | Semantic search | Permission filter | Relevant context |
| Graph layer | Cross-project entities | Relationship traversal | Project boundary | Computable precedent |
Design boundary
What the system will not pretend to be.
No one-database shortcut
Each data shape receives a store appropriate to its integrity and retrieval needs.
No permission-free embeddings
Semantic indexes inherit tenant and document controls.
No graph before value
Graph infrastructure follows validated project-memory relationships.