The learning control plane
Industrial Intelligence Platform
The PDICON Intelligence platform preserves lineage from expert work to workflow dataset, trained model, evidence-backed recommendation, human approval and eventual project outcome.
StatusPlatform architecture direction
Reviewed2026-08-20
Direct answer
The PDICON Intelligence platform preserves lineage from expert work to workflow dataset, trained model, evidence-backed recommendation, human approval and eventual project outcome.
System blueprint
A connected control plane, not a collection of features.
01Capture
Represent what experts actually do.
Browser and project interfaces emit controlled events for artifacts, actions, context, evidence, decisions, corrections and outcomes.
02Train
Build intelligence with reproducible lineage.
Dataset versions pass quality gates into training specifications, GPU scheduling, evaluation, model registration and approval.
03Deploy
Execute across models without losing governance.
A task contract chooses an appropriate runtime while preserving permissions, evidence, benchmark profiles and human control.
Operating matrix
Evidence moves through explicit controls.
| Subject | Input | Intelligence operation | Human / policy control | Output |
|---|---|---|---|---|
| Workflow service | Human and system activity | Event normalization | Sensitive-field policy | Structured evidence |
| Data platform | Artifacts and episodes | Versioning and retrieval | Tenant permissions | Training-ready context |
| AI platform | Dataset and task specification | Train, evaluate and route | Approval gates | Deployed model |
Design boundary
What the system will not pretend to be.
No mystery model
Every production candidate retains data, code, configuration, hardware and evaluation lineage.
No provider lock-in by design
Applications request a task rather than a provider-specific endpoint.
No automatic engineering authority
Recommendations remain subordinate to qualified approval.