
Reliability infrastructure built by roboticists and engineers.
We are building the layer that tells a robotics team whether a learned behavior is ready to ship, where it is fragile, and what changed when it regresses.
The deployment problem is an evidence problem.
Learned policies make robot behavior probabilistic. A checkpoint can improve an average score while silently breaking one lighting condition, object pose or calibration band. That makes a demo insufficient for a release decision.
OORB turns run-level evidence into condition-scoped reliability, coverage gaps and regression findings. The goal is practical: give the team shipping the robot a baseline it can defend and a faster path to the next experiment.
The founding team
Four engineers across the full robot deployment stack.
The team combines autonomy research, embedded and electromechanical systems, full-stack AI infrastructure, MLOps, QA and pilot execution.




Azer Ben Abdallah
Electromechanical engineer leading hardware product design and deployment workflows. Previously at OceanQuest, building robotics and industrial hardware systems from first prototype through fabrication.
Technical range
Built across the boundary between software and the physical world.
Robot reliability is not only a model metric. It sits across the policy, runtime, hardware, calibration, environment and operating workflow.
Robot systems
Embedded engineering, sensing, autonomy research, electromechanical design and real-hardware iteration.
AI infrastructure
Agentic systems, retrieval, backend services, cloud operations, MLOps and quality engineering.
Deployment execution
Pilot operations, institutional integrations, calibration-aware workflows and industrial product delivery.
Delaware C Corporation based in San Francisco · UC Berkeley SkyDeck Batch 22.
Work with us
Bring one learned behavior.
Leave with a reliability baseline.
We work with a focused number of pilot teams on release sign-off, field failure analysis and end-of-line yield.
Get a free audit todayor contact@oorb.io