Dataset → evaluated model
AI Training OS
PDICON AI Training OS turns a versioned workflow dataset and training specification into an evaluated model candidate with complete lineage across code, configuration, hardware, metrics and artifacts.
StatusInitial infrastructure direction
Reviewed2026-08-20
Direct answer
PDICON AI Training OS turns a versioned workflow dataset and training specification into an evaluated model candidate with complete lineage across code, configuration, hardware, metrics and artifacts.
System blueprint
A connected control plane, not a collection of features.
01Specify
Configuration before compute.
A training job defines dataset, task, base model, method, precision, hardware profile and acceptance criteria before resources are allocated.
02Execute
Capture the complete run.
GPU configuration, environment, code commit, checkpoints, logs and resource use remain attached to the experiment.
03Evaluate
A checkpoint is not a production model.
Technical quality, expert agreement, evidence grounding, safety, latency and cost must pass before human validation and promotion.
Operating matrix
Evidence moves through explicit controls.
| Subject | Input | Intelligence operation | Human / policy control | Output |
|---|---|---|---|---|
| Technical quality | Ground-truth episodes | Task evaluation | Acceptance threshold | Accuracy profile |
| Expert agreement | Blind review set | Qualified comparison | Reviewer protocol | Agreement profile |
| Grounding | Evidence-linked outputs | Claim verification | Source requirement | Hallucination profile |
| Operations | Runtime benchmark | Latency and cost test | Deployment SLO | Serving profile |
Design boundary
What the system will not pretend to be.
Not a cosmetic train button
Training is a governed lifecycle of data, compute, evaluation and approval.
One GPU environment first
Complex orchestration follows proven workload demand.
No automatic model promotion
Candidates require explicit validation before production use.
Questions answered
Precise answers for technical evaluation.
What makes a training run reproducible?
The exact dataset version, source code, configuration, environment, hardware profile, metrics, checkpoints and resulting model artifact are retained.
When does a trained model become production-ready?
Only after evaluation, qualified human validation, approval and a defined monitored runtime.