Production-distribution coverage
Capture interactions under real shift conditions so training data reflects actual deployment variance.
Telepath collects robot training data inside active facilities where object states, timing pressure, and workflow interruptions are part of normal operation. Teams get signal that transfers better to deployment than controlled lab-only traces.
Capture interactions under real shift conditions so training data reflects actual deployment variance.
Record difficult scenarios and recovery behavior that usually drive failure rates after rollout.
Collection volume and scenario mix are tied to concrete capability targets instead of generic dataset growth.
Programs start from target behaviors and bottlenecks, then map collection to workflows where those behaviors appear naturally.
Telepath applies agreed checks for schema consistency, usable coverage, and scenario fidelity before handoff to model teams.
Delivery cadence can be synchronized with training sprints so new traces quickly feed evaluation and retraining cycles.
It captures real operational variability and exceptions that are hard to reproduce in controlled environments, which improves transfer to production conditions.
Yes. Telepath scopes each program around your target capability metrics, workflow constraints, and delivery requirements.
Yes. Most teams run repeat delivery cycles tied to model iteration so training and evaluation stay aligned with current production behavior.