Sentry for autonomous
robots in the real world.
Vesper is the failure and feedback loop for Physical AI – turning real-world incidents into timelines, scenarios, and train/test data.
Physical AI is flying blind on failures.
Invisible field failures
Robots fail in the wild without adequate logging. Developers are left guessing what sensory input caused the misalignment.
Fragmented failure data
Logs, LiDAR, and video streams live in silos. Correlating them for a post-mortem is a manual, multi-day engineering effort.
Wasted learning signal
Valuable edge cases are discarded because there's no way to extract them from raw telemetry at scale.
Edge-case explosion
As fleet size grows, the number of unique failures grows exponentially. Traditional QA cannot keep up.
Failures captured, noise filtered
Real-time lightweight sentry identifies deviations from nominal paths.
Incidents reconstructed into episodes
Multimodal data is fused into a 4D interactive timeline.
Hard cases become train/test data
Export directly into simulation environments or VLA training sets.
Developer-First Integration
Insert Vesper into your ROS2 or Python-based robotics stack with 3 lines of code. Our SDK handles high-bandwidth buffering of LiDAR, IMU, and joint state logs.
- check_circleMultimodal timeline (LiDAR + Sensors)
- check_circleAutomatic goal annotation
- check_circleDistributed trace logging
The Training Loop Re-engineered
From real-world mess to simulation-ready digital twin in one pipeline.
Capture a real failure
Automatic event triggering captures the 30-second window leading to an incident.
Reconstruct a scene
Our NeRF-based engine builds a 3D digital twin of the environment directly from robot sensors.
Feed the training loop
Simulate 1,000 variations of that specific edge case to harden your policy.
Operational Intelligence
Robotics Engineers
Debug field incidents in minutes, not days. Get full trace visibility.
Fleet Operators
Monitor fleet health and intervene before catastrophic failures occur.
Sim & ML Teams
Automated dataset curation for VLA and end-to-end policy training.
End Customers
Transparent reporting on uptime and autonomous success metrics.
Scaling Physical AI
Warehouse AMR Fleets
Deployment and debugging for high-density logistics environments.
Tier-1 Expansion
Humanoids, cobots, and specialized inspection robots in heavy industry.
The Universal Episode Layer
Vesper sits between raw hardware telemetry and the high-level AI stack, providing the structure required for the next generation of physical intelligence.
Layer 03: Intelligence Stack
VLA Models, LLMs, 3D Reconstruction Engines, Simulation Loops
Layer 02: Vesper Structured Episodes
Unified, time-synced 4D data packets containing sensors, labels, and environmental context.
Layer 01: Robots & Sensors
ROS2 Nodes, LiDAR, Cameras, IMU, Motor Encoders, Edge Logs
System Comparison
| Bucket | What they do | Where they stop | Vesper's Difference |
|---|---|---|---|
| Observability | Dashboards and metrics | Cannot visualize physical failures | 4D Incident Reconstruction |
| Simulation | Synthetic data generation | Disconnected from reality | Real-to-Sim Pipeline |
| Logging | Raw packet capture (MCAP) | Bloated, manual curation required | Smart Episode Extraction |
The Team
3D AI experts who've spent a decade building at the intersection of geometry and machine intelligence.
Turn your next failure into a better robot.
Join the early pilot program.