Physical AI observability

The reliability layer
for physical AI.

Upload a workflow video. Detect failures. Review incidents.
Build institutional memory.

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—No CV expertise required. Works with any warehouse camera.
● anomalyINC-047
2m ago
reversal/object returned to source zone mid-execution
execution trace
  • ✓
    object_in_source
    t=0.00s
  • ✓
    object_picked
    t=2.84s
  • ✓
    object_in_process
    t=6.21s
  • ↩
    object_in_source
    REVERSAL — unexpected return to source zone
    t=9.87s
  • —
    object_in_output
    skipped
confidence
0.87
The problem
47min
avg time to diagnose a single production failure
// scrubbing video manually
0%
incident recall across runs — every failure is a fresh mystery
// no structured memory
3.2%
typical failure rate in production pick-and-place deployments
// industry benchmark

Your current debugging workflow costs you 30–60 minutes per incident.

ML engineers deploying robot arms in production have Sentry for their Python models, Prometheus for their infrastructure, and nothing for the physical execution layer. When a cycle fails, they watch the video themselves.

No structure. No recall. No pattern detection. When a new engineer joins, the institutional knowledge of past failures lives in Slack and people's heads.

Current workflowcost/incident
1
Pull footage off edge device
~5 min
2
Scrub video to find failure moment
~25 min
3
Write Slack message describing what you saw
~8 min
4
Correlate with sensor logs (if captured)
~12 min
5
Discuss in standup. Maybe write a JIRA ticket.
~7 min
No structured memory. No pattern detection.57 min
How it works
1
Upload

Drop a workflow video

Upload any camera recording of your robot arm cycle. MP4, MOV, or AVI. Up to 500MB.

# POST /api/upload-videofile: workflow_run_47.mp4workflow_id: "pick-place-v2"name: "Run #47 — Line 3" → { "status": "uploaded" }
2
Analyze

Structured anomaly detection

Rule-based engine checks 6 anomaly types against your workflow template. Results in under 60s.

# GET /api/executions/{id}{  status: "failed",  anomalies: [{    type: "reversal",    confidence: 0.87,    at: 9.87  }]}
3
Review

Human review queue

Every detected anomaly goes into a review queue. Confirm or flag as false positive. One click.

# POST /api/anomalies/{id}/review{  verdict: "confirmed",  reviewer: "mleng@co.com",  note: "gripper slip"} → { "status": "reviewed" }
4
Remember

Incident memory that compounds

Every confirmed incident is stored, searchable, and feeds pattern detection across future runs.

# GET /api/incidents?type=reversal{  total: 14,  first_seen: "2025-11-03",  pattern: "gripper_pressure",  recurrence: "every 3.2d"}