Avala AI

Founded 2020

Convert multimodal robot sensor data into versioned training datasets for physical models

You can use Avala to ingest, process, and annotate sensor data collected from robotics and autonomous fleets. The system fuses raw camera feeds, MCAP logs, and ROS bag streams into 4D representations for machine learning teams. You can run automated quality control checks, manage human and automated labeling, and track dataset versions directly from enterprise cloud storage. Once labeling finishes, you can export structured releases straight to training frameworks like PyTorch or TensorFlow, ensuring full lineage and traceability across all physical model updates.

What Avala AI does

Ingest MCAP and ROS bag sensor log filesReconstruct 4D scenes from camera and LiDAR recordingsRun human and automated quality control reviews on labeled dataPublish traceable dataset releases to model training pipelinesTrack data lineage and inspection logs across model iterationsSend milestone notifications and quality alerts to SlackConverts raw sensor streams and ROS bags into 4D representationsConnects directly with private enterprise cloud storage bucketsCombines automated AI labeling with human verification workflowsTracks complete data lineage and versioning across training releasesExports curated datasets directly into PyTorch and TensorFlow pipelinesDetects sensor errors and labeling defects prior to model training