Deploy robots in real factories. Capture the failures that matter. Extract the data that closes the deployment gap.
Schedule a conversationFoundation models hit 95% in controlled labs. In real factories, that drops to 60%. Commercial deployment demands 99.9%. The data that closes this gap — recovery trajectories from real-world failure — barely exists.
Site acquisition. Safety protocols. Operator hiring. Task resetting. Failure logging. Every lab builds the same five things from scratch. No one has built the shared layer.
50+ funded labs are entering active deployment. The infrastructure they need doesn't exist yet.
Ashmyr is the shared deployment layer between AI labs and the real world. A pre-contracted network of factories where any lab can deploy, fail, capture recovery data, and ship a better model.
No site acquisition. No safety management. No ops team.
Model weights, task definitions, evaluation criteria
Robot ships to sandbox site (or Ashmyr-owned fleet at scale)
Full trajectories, video, joint position streams
Isolated recovery trajectories at point of failure
Every failure is a data event. Our engineers capture the recovery at the exact point of failure. That trajectory feeds the next policy. The robot gets better at exactly what it got wrong.
Every deployment session produces video, joint trajectories, and isolated recovery corrections — formatted for retraining, not post-processing.
RGB capture, every rollout, every failure.
7-DOF joint states at 50Hz, full session duration.
The correction at the exact failure point — segmented, RLDS-formatted, ready for fine-tuning.
A pre-contracted network of active Ethiopian factories where partners get paid to host robots. One provider, zero operational overhead for labs.
Factories get paid to host robots and gain early exposure to automation. Labs get real-world failure data. Real tasks, real workflows, not subsidized test sites.
Active facilities across assembly, packaging, logistics, textile, and food processing — all pre-profiled and ready to receive robots.
A factory network built over years through prior ventures in mining services, commercial real estate, and supply chain operations across Ethiopia.
20+ peer-reviewed papers converge on one finding: environment diversity dominates data volume. The robots that work in the real world are trained on diverse real-world failure — not more simulation.