Bring the work. Keep the proof. Own the model.
The data only people can give, and an AI app for your field. AuraOne turns reviewed real work into signed, rights-cleared evidence and weights you keep — so your data survives an audit and your model stays yours.
One gets you the people who make your data right and signs every datapoint they produce. The other turns that work into an AI app for your field. You keep the weights.
Engineers, doctors, lawyers, bankers, and scientists author the work. Every datapoint is signed.
Built on that signed work. You keep the weights.
Why now: the largest data vendor was absorbed by one of the labs it served. A competitor lost four terabytes — including who its workers were. The EU AI Act's training-data rules enforce in August 2026, and 78% of teams can't validate their data before training. You need a neutral source you can defend under audit.
Every datapoint carries the verified person who made it and the person who reviewed it. Identity, credentials, calibration.
SignedSigned consent and usage rights travel with the data. A chain that survives an EU AI Act audit and a worker-classification challenge.
SignedYour data is not pooled in one place with everyone else's. You run the layer. The weights are yours to keep.
SignedTest the run. Review the hard cases. Recruit the right specialist. Remember the misses so the same one is caught again. Approve what holds up.
Test the run.
Review the hard cases.
Recruit the right specialist.
Remember the misses.
Approve what's right.
Evidence accumulates as the record moves.
One ownership loop. Models and Robotics first. The same loop extends across the domain labs. Each app starts from a selected model baseline, runs inside your work, and learns from your signed data. The weights are yours to keep.
See every applicationScreen risky sequences before they reach synthesis or the bench.
Open appCompare routes and choose the path the bench can actually defend and run.
Open appReview simulations against reality before a result moves into design, science, or reporting.
Open appScreen material candidates and move only the right ones into qualification.
Open appSeparate real signal from noise before scarce follow-up time gets spent.
Open appTurn climate scenarios and monitoring work into a reporting record people can trust.
Open appReview site conditions and document which response was actually approved.
Open appTriage cases and keep every hard clinical decision attached to the record behind it.
Open appReview changes against requirements before they become release, safety, or cost problems.
Open appKeep scene, alignment, and delivery decisions traceable from capture through handoff.
Open appReview inspections and decide what can ship with the evidence attached.
Open appReview risk decisions and explain the edge cases before an examiner or committee asks.
Open appTurn real-environment capture, teleop interventions, and robot failures into rights-cleared, eval-ready datasets your team can defend.
Open app“Release review moves from a late scramble to a repeatable gate. When an edge case slips, the team captures it, reviews it, and turns it into release evidence.”
We scope the dataset, the rubric, and the reviewers, and hand back a signed evidence packet. A robotics pilot runs 100 to 500 reviewed episodes.