Observability for Physical AI

The telemetry and real-time analytics platform for robot fleets. Ingest logs, metrics, traces and sensor streams from every machine, then trace any failure back to the exact recording.

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Open by design

ROS 2
MCAP
OTLP
Apache Iceberg
Parquet

site-07/fleet overview Live

uav-0031cam_front

t=1421.38s30 fps

Machines online

142/146

Ingest rate

1.84 GB/s

p95 query latency

212 ms

tracetask.pickarm-0207

task.pick

perception.infer

grasp.exec

recording

Illustrative data

Our mission

Physical AI is moving into production. We're building the observability layer it runs on.

How it works

Edge collectors stream telemetry from every machine, with no code changes.

The platform

[One platform. Every machine.]

Real-time observability, built on an open telemetry standard.

The Brewric platform

Each layer builds on the one below it.

Fleet applications

Fleet monitoring
Incident correlation
Regression detection
Alerting

Real-time analytics

Hosted ingestion
Fleet-wide live queries
Retention
Enterprise controls

Open telemetry standard

Shared conventions
Edge collector
C++ and Python SDKs
Open formats

Your fleet

Sensors
Software
AI models
Hardware

Capabilities

Built for fleets in production

Ingest

Unified telemetry

Logs, metrics, traces and sensor streams on a single timeline, correlated by robot, task and model version.

Collect

Zero-code collection

An edge collector reads what robots already publish. Add SDKs for richer data.

Query

Fleet-wide live queries

Query every machine in near real time, across sites and robot types.

Correlate

Incident correlation

Tie each failure and intervention to the exact synced sensor recording.

Detect

Regression detection

Catch AI model regressions as new versions roll out across the fleet.

Govern

Enterprise controls

Access controls, retention policies and audit trails for production fleets, with your data kept portable.

Open standard

One schema for machines in the real world

Vendor-neutral conventions for robot identity, tasks, failures, interventions and model versions, with links to synced sensor recordings. Capture once and send it to any tool.

failed_pick.yamlIllustrative

resource:  robot.id: arm-0207  robot.fleet: site-07  model.version: grasp-policy@12.4 span:  name: task.pick  status: failed  failure.class: grasp.slip  intervention: remote.operator  recording:    uri: s3://fleet/site-07/run-8841.mcap    t: 1421.38