Capture
Normalize the run, outcome, version and operating conditions into one record.
Reliability for
robot deployments.
Cut your deployment time in half.
We will join you on your site, inspect and diagnose your deployed robot and give you a a full audit report and root cause analysis, free of charge.
UC Berkeley SkyDeck · Batch 22A promising demo is not a release decision.
Retraining, calibration drift and environment changes move robot behavior in ways an aggregate benchmark never shows. You need to know where the policy works, where it fails, and whether the candidate beats the deployed baseline.
From a robot run to an engineering decision.
Demo data
Score
Measure reliability by condition, with confidence and coverage made explicit.
Explain
Compare cohorts and surface the changes most associated with the regression.
Pick the decision that is blocking your team.
01
Policy Release Sign-off
Decide whether the next learned-policy release is ready to ship.
- Bring
- Baseline and candidate runs, condition matrix, release metadata
- Leave with
- Go / no-go brief, scoped reliability, regressions, coverage gaps
02
Field Failure Autopsy
Turn a costly field failure into a ranked, testable explanation.
- Bring
- Failed and healthy runs, logs, versions, calibration and operator notes
- Leave with
- Failure cohort, change map, likely contributors, next experiments
03
EOL Yield Intelligence
See which end-of-line conditions are driving rejects and rework.
- Bring
- Station outcomes, configurations, interventions and inspection records
- Leave with
- Yield baseline, loss segments, recurring signatures, action queue
Evidence in. Findings out.
Inputs
247labeled hardware runs
- Model, code and calibration versions
- 6 lighting setups · 4 calibration bands
- 18 object poses and run-level outcomes
Outputs
91%reliability · 95% CI [87%, 94%]
- Reliability segmented by condition
- Coverage matrix with confidence bounds
- Low-light called out as untested, not assumed
Engineering findings
Camera position shift aligned with a cohort drop from 91% to 64%.
- Candidate model showed 3× more object drops
- Runtime latency increased 18 ms after a software change
- Resulting success rate moved down 8 points
Anonymized snapshot. Association is reported separately from causation; findings become experiments to confirm.
The OORB loop
Capture every run. Score reliability. Explain what changed.
01
Capture the evidence available
Start with the outcomes and metadata your stack already produces. During intake, we identify gaps and agree on any additional instrumentation before the evaluation starts.
02
Score by operating condition
Report success and intervention rates with confidence intervals, cohort comparisons and an explicit list of conditions that remain untested.
03
Explain the movement
Link version, calibration and environment changes to outcome shifts, then rank the next experiments needed to confirm the likely cause.
Keep robot data inside your boundary.
Raw logs and video can stay in customer-managed infrastructure. Access, retention and deletion are agreed before data moves.
Local-first option
Process raw run data in a customer-controlled environment when required.
Minimum necessary data
Share derived run manifests and results instead of raw media where possible.
Scoped access
Use read-only paths where possible and document retention and deletion up front.
Evaluate one robot behavior before it ships.
We will come to your deployment site and provide a full audit, free of charge.