STEP 01
WHAT WE TRAIN
Train on reviewed work
Reviewed signals, accepted overrides, and signed cases become the corpus. The rubric is the bar — the run learns from work the team already trusts.
→AI LABS · TRAINING · YOUR MODEL. YOURS TO KEEP.
Your model. Yours to keep.
Train on reviewed work. Keep the tuned weights. Export the record.
WEIGHTS
Tuned and yours
Trained on reviewed work. Exportable to your environment.
RECORD
Every run, intact
Inputs, reviewers, policy, and decisions stay attached.
PROOF
Ships with the model
Lineage and evidence leave with the weights.
HOW IT WORKS
Three steps. One run.
Train on what was reviewed. Validate against the rubric. Keep the weights and the record.
STEP 02
WHAT WE CHECK
Validate against the rubric
The tuned model runs the same rubric every release passes. Drift, regression, and bias surface before the gate, not after launch.
→STEP 03
WHAT WE EXPORT
Keep the weights
Tuned weights, training records, and validation runs leave as one signed packet. Yours to keep in your environment, on your schedule.
WHAT COMES OUT
What your team leaves with.
Every run leaves something the team can keep — and something the next release can build on.
01
Tuned weights
The model the team can keep — trained on reviewed work, exportable to the environment you run.
↳ ARTIFACT
02
Training records
Objective, corpus, configuration, and constraints — logged so the run can be reproduced.
↳ ARTIFACT
03
Validation runs
The same rubric every release clears. Drift, regression, and bias shown before the gate.
↳ ARTIFACT
04
Export packets
Weights, dataset slice, reviewer coverage, and checksums leave as one signed record.
↳ ARTIFACT
05
Lineage chain
Point from the exported model back to its reviewers, inputs, and policies — without rebuilding context.
↳ ARTIFACT
EXPORT PACKET
SIGNED PROOF● SEALED
Rubric142 cases
Reviewer19 overrides
Checksumsha256:7fa9…
EvidenceEP-4187
EVALREVIEWPOLICYRELEASE
SIGNED
EP·4187
EP·4187
Signed at release gate · EP-4187
WHERE IT FITS
In the loop, this is where you remember.
Test the run. Review the hard cases. Recruit the right specialist. Remember what was reviewed — and train on it. Approve what's right.
01
Test
02
Review
03
Recruit
04
Remember
● YOU ARE HERE
05
Approve
RELATED MODULES
Next to this in the Model OS.
AUTOPILOT
The work, run on its own.
Routine cases run through automatically. Reviewers keep the hard ones.
See the page →RL ENVIRONMENTS
Reproducible by construction.
Deterministic environments for evaluation and training.
See the page →FEDERATED LEARNING
Train where the data lives.
Tuned models without moving the work off your network.
See the page →TRAINING
Your model. Yours to keep.
Train on the work your team already reviewed. Keep the weights, the record, and the proof.