The incident response system that makes robot fleets deployable at scale.
Robots get stuck. They hit safety stops. They oscillate. They degrade after updates. The data you need isn't a log file — it's multimodal, high-frequency streams.
Operators step in. Engineers debug incident-by-incident.
Failures need multimodal streams, not simple logs.
Post-mortems happen, but the same issues return.
Teleop becomes the default recovery mechanism.
Fleets can run at high uptime if failures are handled fast and systematically, and every failure becomes a learning signal.
Sensor snapshots, perception outputs, planner state, control commands — auto-assembled into a deterministic replay.
Simple nudges, reversing, waypoint overrides. Get robots unstuck in minutes without full teleop.
Seven incidents across the fleet? That's one reliability problem with seven examples. Fix the pattern.
Clusters become regression tests. Release gates block versions that increase failure rates.
6 min
Mean time to recovery
down from 2+ hours
80%
Fewer repeat incidents
with reliability gating
99.3%
Uptime target
achievable for production fleets
Where downtime directly impacts throughput, fleets are large enough that failures are frequent, and operations teams are already forced into manual recovery loops.
Autonomy won't be perfect.
Fleets can still run like a product.