An AI app for your field. You keep the weights.
Tracing tells you what the model did. AuraOne records who approved it — eval miss, reviewer, regression gate, signed release. Start open. Train on your reviewed work. Keep the tuned model.
Dashboards tell you what changed.
A signed gate, on the evidence, with the reviewer named.
Train on reviewed work. Own the model.
Observability stops at the score. The value is the signed release.
Dashboards show what changed. They do not record who approved it. Here, an eval miss carries a reviewer, the regression gate it had to pass, and the signed evidence the release went out on. Then you train on that reviewed work and keep the tuned weights. Independent of any single data or model vendor — the release record is yours and free of conflict.
- ·Evaluation Studio
- ·AuraQC
- ·Regression Bank
- ·Compliance Monitoring
- ·Control Center
- ·Autopilot
- ·Training
- ·RL Environments
- ·Federated Learning
- ·One record per release
- ·Reviewer attached
- ·Regression gate
- ·Export pack you own
Test the work. Review it. Catch the same mistake once.
Every escaped failure becomes a gate the next release cannot cross. The proof rides along — access, audit, retention, and exports live inside the workflow.
Test it before it ships.
For the teams who stopped trusting the eval script.
Quality that doesn't end at ship day.
Every issue. Every reviewer. One screen.
Every mistake. Only once.
Every escaped failure becomes a gate the next release cannot cross.
Compliance that writes itself.
The record builds as the work is done. Access, audit, retention, and exports live here too.
EP·4187
The last check before it ships.
Tests, reviews, regressions, and compliance converge here.
The stack, on one page.
Start here. Every module, every group, one scroll.
Start open. Train on your reviewed work. Keep the tuned weights.
You own the model, not a rented endpoint. Run the agents, learn together, and keep the updates without sharing the data.
Describe it. Watch it run.
Workflows that build themselves — and wait for the approval you set.
EP·4187
Your model. Yours to keep.
Train on reviewed work. Keep the tuned weights. Export the record.
Environments built by the people who use them.
Reviewed once. Approved once. Available to every team that needs them.
Learn together. Keep it separate.
Share the updates. Not the data.
A signal becomes a decision without losing context.
An eval miss becomes a reviewer assignment, a regression case, a compliance finding, and a release hold — then a signed packet and a tuned model. One record, not five separate stories.
The gate it must pass
The evidence it shipped on
EP·4187
The proof leaves with you.
A signed release packet — scorecard, reviewer rationale, replay suite, and decision timeline — ready when an examiner or committee asks. EU AI Act high-risk training-data provenance enforces August 2026; 78% of orgs can't validate their data before training, and 77% can't trace where it came from.
Scorecards
One read on what passed, what failed, and what needs another look.
Review records
The case, the reviewer notes, and the final call stay together.
Replay suites
Escaped issues become repeatable checks the next release must pass.
Decision timelines
What changed. Who approved it. Whether the work moved forward.
Evidence packets
Checks, history, and proof — ready when someone asks.
Bring the work. Keep the proof. Own the model.
Bring the release, the review queue, or the escaped issue that matters most. We will show how it runs. Keep your registry, your CI, your providers — AuraOne signs the hard decision and keeps the reviewer evidence attached.