STEP 01
TRAIN
Train on reviewed work
Reviewed signals, accepted overrides, and signed-off cases become the corpus. The rubric is the bar — the model learns from work the team already trusts.
→APP DATA · TRAINING · REVIEWED ARTIFACTS
Your run. Yours to prove.
Start from reviewed work, attach lineage to the run, and preserve the approval record. Model export or ownership terms are scoped in the engagement; the public product claim is the evidence trail.
ARTIFACT
Scoped handoff
Reviewed work, training records, validation runs, and any contracted model artifact leave with explicit handoff terms.
RECORD
Every run, intact
Inputs, reviewers, policy, and decisions stay attached to the run.
PROOF
Ships with the run
The lineage chain and validation runs leave with the packet.
HOW IT WORKS
Three steps. One run.
Train on what was reviewed. Validate against the rubric. Keep the record, packet, and scoped handoff terms.
STEP 02
VALIDATE
Validate against the rubric
The candidate run clears the same rubric every release passes. Drift, regression, and bias surface before the gate, not after launch.
→STEP 03
KEEP
Keep the record
Training records, validation runs, and any contracted model artifact leave as one reviewed bundle with explicit handoff terms.
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
Scoped artifact
Any model artifact included in the engagement is tied to reviewed work, validation evidence, and handoff terms.
↳ 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
Dataset slice, reviewer coverage, checksums, and any scoped artifact leave as one record — signed against an identity-verified chain of consent.
↳ ARTIFACT
05
Lineage chain
Trace the exported model back to its reviewers, inputs, and policies — without rebuilding context.
↳ ARTIFACT
EXPORT PACKET
REVIEW RECORD● READY
Rubric142 cases
Reviewer19 overrides
Checksumsha256:7fa9…
RecordRR-4187
EVALREVIEWPOLICYRELEASE
READY
RR·4187
RR·4187
Recorded at launch check · RR-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
● TRAINING SITS HERE
05
Approve
RELATED MODULES
More in App Data.
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 run. Yours to prove.
Start open. Train on the work your team already reviewed. Keep the record, packet, and proof.