Make AI-Powered Robotics Reliable in the Real World
The end-to-end platform for training, testing, and deploying foundation models for autonomous systems.
Robotics doesn't fail in the lab - it fails in the 'last mile'
Production conditions like lighting, motion, clutter, surfaces, sensor noise, expose failures you didn't measure.
Edge cases in pick-and-place routines, navigation, or inspection trigger incidents, escalations, and rollout stalls.
Teams can't quickly prove what broke, why, and what to fix first-so scaling drags.
From pilot purgatory to scaled production
Surface failure modes in perception, manipulation, or navigation before rollout expands.
Reduce surprises and regressions once models hit the field.
Faster rollout decisions: proceed / fix / pause- based on evidence, not instinct.
Operationalize reliability from lab to field- with Tensorleap
Which sensors and visual concepts drive decisions during real-world execution
Spot differences between lab data and production data as conditions change
Go from failure insight to fixes across data, validation, and redeployment
Built for the teams who ship physical AI
Leaders
Reduce deployment risk, protect uptime, and make go/no-go decisions with clear evidence.
Find failure modes faster, reduce rework, and iterate without regressions in real-world runs.