Physical AI & Robotics
Training data and AI systems for robots, vehicles, and physical-world intelligence
Solnix builds the data infrastructure, annotation pipelines, and AI systems that power physical AI, from autonomous vehicle perception models to industrial robotics and warehouse automation.
Overview
The data and AI infrastructure that gives machines physical intelligence
Physical AI systems fail at the data layer. Poor LiDAR annotation, missing edge cases, and inadequate sim-to-real validation cause autonomous systems to fail in deployment. Solnix builds the annotation pipelines, sensor fusion architectures, and evaluation frameworks that make physical AI systems reliable.
What's included
Robotics Training Datasets
Task demonstration data, manipulation trajectories, grasping scenarios, and failure case libraries for robot learning, with sim-to-real transfer validation.
Autonomous Vehicle Datasets
Multi-sensor fusion annotation: LiDAR point clouds, camera arrays, radar, HD map data, with edge case coverage for rare but safety-critical scenarios.
Physical AI Training Data
Real-world interaction data collection design, sensor calibration validation, data quality assessment, and training/test split strategy for physical AI systems.
Sensor Fusion Architecture
We design the perception stack that combines LiDAR, camera, radar, and IMU data streams into a unified representation for downstream AI models.
Simulation & Synthetic Data
When real-world collection is dangerous or expensive, we design simulation pipelines in Isaac Sim, CARLA, or custom environments that produce training-grade synthetic data.
Safety & Regulatory Compliance
Safety case documentation, failure mode analysis, edge-case coverage measurement, and compliance support for ISO 26262, IEC 61508, and UL 4600.
Developer experience
Simple API. Powerful results.
Integrate in minutes with our SDK. Full TypeScript support, comprehensive documentation, and live examples for every feature.
How it works
From setup to production
Sensor & Data Audit
We audit your sensor configuration, data collection infrastructure, and existing annotation to identify coverage gaps and quality issues.
Annotation Pipeline Design
We design the annotation taxonomy, edge case weighting, QA workflow, and tooling stack for your specific sensor modalities and AI tasks.
Production Annotation & Validation
Annotation runs at scale with specialized annotators for each sensor modality, multi-pass QA, and continuous quality monitoring.
Sim Integration & Delivery
Datasets are validated against your training pipeline, with sim-to-real gap analysis and integration support for Isaac Sim, CARLA, or your custom environment.
FAQ
Common questions
Get started
Build physical AI systems that work in the real world
Talk to an expert and get a tailored implementation plan within 48 hours.