Embedded, on-device analytics and observability layer for robots and physical AI systems. Purpose-built for real deployments where connectivity is unreliable.
Designed for real deployments where connectivity is unreliable. Runs disconnected on device and synchronizes to the cloud when available.
Unlike general-purpose observability tools, Physion handles the episodic, context-heavy nature of robotics: tasks have start/end, failures have spatial context.
Integrates with Raindrop to run persistent cloud agents against the Physion cloud twin, enabling always-on inference-heavy operations workflows.
Physical AI and robotics teams face unique observability challenges that traditional tools weren't built to handle
Find when the system starts behaving differently over days or weeks, correlate changes to environment, model version, firmware, or config.
Quickly answer "what changed" and "which robots are affected" without pulling raw logs and hand-scripting analysis.
Produce auditable traces and summaries of what the robot perceived, decided, and did during key windows.
Compare performance and failure modes across software and model rollouts using consistent queries and metrics, enabling continuous learning loops instead of episodic retraining.
Physical AI generates terabyte-scale data. Bandwidth-aware on-device filtering ensures you upload high signal, not raw firehose, making cloud-side analysis economical even at remote sites with limited connectivity.
Handle intermittent connectivity, partial failures, and long tail edge conditions without losing the story of what occurred.
Physical AI generates episodic, spatially-aware data that traditional observability systems can't handle effectively.
Join leading robotics teams building the future of physical AI
Purpose-built capabilities for robotics and physical AI observability
Pipeline for robotics signals, model outputs, and system state with on-device time series & event database.
Columnar analytics store for longer-window queries and summaries with native query language for telemetry and sensor signals.
Episodes as first-class abstractions: semantically search by what happened (hesitation, collision), not just timestamps. Structured, queryable scenarios instead of opaque blobs.
RPC, pubsub, and HTTP primitives so components can publish and consume analytics without brittle integration work.
Feed handlers subscribe to ROS2 pubsub topics to capture sensor signals, model outputs, and actuator commands as structured episodes.
Fully embedded, hybrid, or cloud-first deployment options with ability to run Physion instances off-device as well.
Fleet aggregation, cross-bot comparisons, and scale offline analysis through cloud twin synchronization.
Intentionally CPU-first on device so it doesn't compete with real-time perception and control workloads.
Raindrop + Physion
Physion makes robots and fleets observable and queryable. Raindrop runs always-on virtual agents against that reality, and safely feeds decisions back into operations. Together they form a closed-loop supervisor layer: sense (Physion) → reason (cloud inference) → act (workflows and controls) → verify (back to Physion).
Physion enables GPU-heavy cloud inference at scale while keeping edge devices CPU-first
Classify incidents, explain failures, and produce operator-ready summaries over curated episodes.
Find repeats of a failure mode or goal miss across robots, sites, and time using behavioral patterns.
Detect emerging patterns early and quantify impact across environments with drift modeling.
Judge quality, compliance, and done-ness against goals and policies when success is not a single scalar KPI.
Build high-value datasets from real operations without manual log pulls for retraining data curation.
Close the loop by verifying outcomes over time, generating new training and evaluation signals from fleet operations.
Always-on virtual agents that turn monitoring into supervision
Defines goals and constraints, monitors semantic goal attainment rates across fleets and environments.
Detects near-misses, policy violations, and behavioral drift from Physion episodes using cloud inference.
Selects high-value episodes and runs VLM and embedding pipelines to label, index, and deduplicate.
How Physion and Raindrop work together
Physion runs locally on robots to capture signals, derive summaries, and package high-signal episodes. It syncs selected episodes and aggregates to a cloud twin for fleet-wide analysis. Raindrop runs supervisor agents against the cloud twin, orchestrating workflows and calling cloud-side inference endpoints. Cloud GPUs run inference pipelines for understanding, clustering, outcome scoring, and recommendations. Agents emit actions back into operations: alerts, tickets, dashboards, and rollout recommendations.
Robot runs Physion locally to capture signals and derive summaries
Physion syncs episodes to cloud twin for fleet-wide analysis
Raindrop runs supervisor agents against the Physion cloud twin
Cloud GPUs run VLM, LLM, and anomaly detection workloads
Agents emit actions: alerts, tickets, rollout recommendations
Results flow back as curated datasets and verified outcomes
Autonomous delivery fleet uses Physion to detect when perception models degrade in rain, triggering cloud analysis across 1,000 robots to identify root cause and validate fix.