Proof, not promises.
See the measured outcomes robotics teams get from certifying policies on Certisto — fewer field failures, faster sign-off, and evidence everyone trusts.
How Atlas Robotics prevented two recalls before launch
A humanoid team used Certisto to surface long-tail manipulation failures in the twin — failures that would have shipped to the field.
Outcome snapshot
The robots behind these numbers
Deployments, measured.
Preventing recalls with twin-caught failure modes
2 recalls prevented
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Cutting assembly sign-off from weeks to days
8× faster
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Certifying warehouse navigation at fleet scale
62% fewer incidents
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A three-week pilot to first signed certificate
3-week pilot
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50k trials before a single public demo
0 violations
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Making certificates a deployment gate
Fleet standard
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From confident demos to certified deployment.
Atlas Robotics builds bipedal humanoids for warehouse work. Their manipulation policies looked flawless in internal demos — but leadership had no way to prove readiness before a fleet rollout that would put robots next to people.
The challenge
Demos only exercise the happy path. The team needed to know how policies behaved in the rare, dangerous conditions that never showed up in a scripted demo.
The approach
- Twin Studio built a calibrated twin of the target warehouse
- Data Foundry mined occlusion, low-light, and payload-shift scenarios
- Cert Engine ran 50k parallel trials and scored readiness
The result
Seven failure modes surfaced in the twin — two severe enough to cause recalls in the field. Atlas fixed them pre-launch and shipped with a certified operating envelope and a 96% field-correlated certificate.
Every result is validated against the field.
We don't publish sim numbers and call them outcomes. Each case study includes a correlation review comparing the certificate's prediction to what actually happened after deployment.
- Predicted readiness vs. measured field performance
- Failure modes confirmed or ruled out in the field
- Independent, reproducible from Evidence Vault records
Correlation review
What every successful engagement has in common.
Start with one high-stakes policy
Pick the deployment where being wrong is most expensive.
Calibrate the twin to reality
Match physics and sensors to real hardware before trusting a score.
Mine the long tail
Let the scenario miner find the failures manual testing misses.
Gate the release
Set a minimum readiness score and make it part of the launch review.
Outcomes by robot type.
| Robot type | Failures caught pre-field | Cycle-time change | Field correlation |
|---|---|---|---|
| Humanoid | 7 avg / policy | −78% vs. manual | 96% |
| Industrial arm | 4 avg / policy | 8× faster | 93% |
| Mobile robot | 5 avg / policy | −62% incidents | 94% |
| Autonomous machine | 6 avg / policy | 3-week pilot | 91% |
"The ROI was obvious after the first certificate. We caught a failure that would have cost more than a year of the platform."
COO, Forge Dynamics
Write the next success story.
Bring us your hardest deployment. We'll certify a policy against a twin of your environment.