Aurevix by Agentic Convergent2 minutes. Not 2 weeks. AI-powered 4D temporal-spatial annotation for multi-sensor robotics data — detecting physics events humans can't see.
Multi-sensor robot data ingest
Temporal-spatial + physics-layer analysis
Export for VLA, imitation & RL models
Robotics data annotation is the #1 bottleneck preventing faster model iteration. The old way is slow, expensive, and fundamentally blind.
Human annotators charge $100–600/hour. A single 10-minute multi-sensor clip costs thousands — and your team processes dozens per week.
Outsourced labeling creates multi-day delays. Your engineers sit idle waiting for annotations while competitors iterate faster.
Humans physically cannot detect micro-slips, torque anomalies, or sub-millimeter friction changes — the exact events that cause robot failures.
“We were spending more on annotation than on compute. Our robotics pipeline was bottlenecked by humans who couldn't even see the data that mattered most.”— ML Lead, Series B Robotics Company
It doesn't have to be this way.
Meet AurevixAn Agentic Convergent product. The 4D temporal-spatial AI annotation engine purpose-built for robotics. Three steps from raw sensor data to training-ready labels.
Drop your MCAP, RLDS, or ROS bag files. Aurevix natively ingests camera, LiDAR, force-torque, joint state, and tactile data simultaneously.
Our 4D temporal-spatial engine fuses all sensor modalities, detecting physics-layer events invisible to humans — micro-slips, torque anomalies, friction transitions.
Export training-ready annotations in seconds. Compatible with RT-2, Octo, OpenVLA, and all major imitation learning and RL frameworks.
Aurevix doesn't just do annotation better — it does annotation that's impossible any other way.
What takes a team of human annotators 20–40 hours per 10-minute clip, Aurevix completes in under 2 minutes. Your model iteration cycle goes from weeks to hours.
Slash annotation costs by 90%. At $75–100 per clip vs. $1K–6K with human labelers, you can annotate 10× more data for the same budget.
10× more data, same budgetAurevix detects physics events invisible to human annotators: micro-slips (sub-mm), torque anomalies, friction coefficient changes, and contact dynamics — the exact events that cause robot failures.
✦ Only Aurevix| Feature | Aurevix | Scale AI | CVAT | Labelbox |
|---|---|---|---|---|
| Multi-sensor fusion (camera+LiDAR+force-torque) | ✓ | ✗ | ✗ | ✗ |
| Physics-layer event detection | ✓ | ✗ | ✗ | ✗ |
| Native MCAP/RLDS/ROS bag support | ✓ | ✗ | ◐ | ✗ |
| 4D temporal-spatial annotation | ✓ | ✗ | ✗ | ✗ |
| Processing time per 10-min clip | <2 min | 2–5 days | 20–40 hrs | 2–5 days |
| Cost per 10-min clip | $75–100 | $3K–6K | $1K–2K | $2K–5K |
| Robot-specific domain models | ✓ | ◐ | ✗ | ◐ |
| VLA model export (RT-2, Octo, OpenVLA) | ✓ | ✗ | ✗ | ✗ |
Aurevix annotates 10 minutes of multi-sensor robot data in under 2 minutes, achieving 97.8% accuracy on micro-slip detection. This is 100× faster than manual annotation at 90% lower cost.
Aurevix specializes in humanoid robots, warehouse robotics, and vision-language-action (VLA) models. We support any robot with camera, LiDAR, force-torque, or IMU sensors.
Yes. Aurevix ingests MCAP, ROS bag, and HDF5 formats. Our API supports direct integration with your existing robotics pipeline.
We export to PyTorch, TensorFlow, JAX, and RLDS formats. Direct integration with Hugging Face datasets and OpenVLA model training pipelines.
Scale AI costs $1K-6K per 10-minute clip with 5-7 day turnaround. Aurevix costs $75-100 per clip with 2-minute instant processing and 97.8% accuracy.
Yes. Aurevix is fully GDPR and CCPA compliant with enterprise-grade data encryption, SOC 2 Type II certified.
Absolutely. All data is processed in isolated containers with zero persistence. Enterprise customers can deploy Aurevix on-premise.
99.95% uptime SLA with guaranteed 24-hour support. Enterprise plans include dedicated support engineers.
Aurevix provides REST API, Python SDK, and CLI interfaces. Start with a 2-minute demo: visit our dashboard and upload a sample ROS bag.
MCAP, ROS bag, HDF5, video (MP4, WebM), point clouds (PCD, PLY), and custom sensor streams via our plugin SDK.
Aurevix uses a proprietary 4D temporal-spatial transformer that fuses RGB camera streams, LiDAR point clouds, and IMU/force-torque data into a single coherent world model for millimeter-accurate event labeling.
Yes. Our AI is trained on physics-layer signatures of micro-slips, torque saturation, and vibration anomalies—events that often precede robot failure but are invisible or too fast for human annotators to catch.
Absolutely. We specialize in labeling language-conditioned manipulation data, perfectly formatted for training foundation models like OpenVLA, Octo, or RT-2 with high-density action-token mapping.
We offer an enterprise on-premise deployment or VPC isolation. Data is processed in zero-persistence Ephemeral Containers, ensuring your proprietary robotics logs never leave your security perimeter.
Manual tools like CVAT or Foxglove require human frame-by-frame labeling, taking 20-40 hours for a 10-minute clip. Aurevix completes the same task in under 2 minutes with higher consistency across temporal boundaries.
Whether you're training humanoids, automating warehouses, or developing next-gen foundation models — Aurevix accelerates your pipeline.
Label grasping, tool use, and dexterous manipulation data for humanoid robot learning — capturing the micro-slip and contact events that define task success or failure.
Annotate high-throughput pick-and-place sequences with force-torque context. Detect near-misses and grip failures automatically.
Generate the training data that vision-language-action models need. Export directly to RT-2, Octo, and OpenVLA-compatible formats.
Automatically flag anomalous physics events in deployment logs. Identify failure precursors before they cause costly incidents.
Join the robotics teams already annotating 100× faster. Limited pilot slots available for qualified teams.