Results from production floors, not controlled labs.
Every metric on this page comes from a live deployment. Interpretability logs and raw benchmark data are available on request.


Three environments, zero lab conditions.
Deployments span automotive assembly, palletized logistics, and semiconductor inspection — each environment selected because it breaks systems trained only indoors under ideal variance.
Reasoning logs are timestamped and exportable. Your team can audit every decision the robot made during the trial period.
94.7% task completion rate
0.3% unplanned stop rate
99.1% defect classification accuracy
Measured across 12 weeks of continuous operation on a mixed-SKU assembly line with uncontrolled lighting and part-orientation variance.
Recorded over 30-day trial in a high-throughput distribution center. Each stop event is logged with a causal trace accessible to the operations team.
Validated against an independent human-review baseline on 50,000 units. Classification confidence scores are exposed per decision.


Numbers you can reproduce, not compare.
Benchmarks are reported with full protocol documentation — hardware configuration, environment parameters, and evaluation scripts. No normalized scores, no proprietary baselines.
Interpretability log format is open-schema. Your integration team can pipe decision traces into your existing observability stack without vendor tooling.
Audit the record yourself.
Qualified teams receive the full deployment dataset, interpretability log samples, and benchmark protocols. No redacted summaries.
