Shotwell AI provides a data validation platform for robotics, automating the collection, labeling, and quality checking of sensor streams such as video, lidar, and proprioception. The system detects anomalies, labeling errors, and distribution shifts, giving robotics teams clear metrics and audit trails to improve model accuracy and reduce development cycles.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics teams often struggle with inconsistent or low‑quality training data, which hampers the performance and reliability of AI models used in physical systems.
Solution
Shotwell AI offers a data validation platform specifically designed for robotics applications. The platform provides an integrated engine that streamlines the collection, labeling, and verification of sensor and perception data from robots. By automating quality checks and ensuring that training datasets meet defined standards, Shotwell AI helps teams accelerate development cycles and improve model accuracy for real‑world deployment.
Target Audience
Primary customers are robotics development teams and AI engineers building perception and control models for autonomous machines, such as drones, warehouse robots, and manufacturing automation systems.
Features
- End‑to‑end pipeline for gathering robot sensor data, including video, lidar, and proprioceptive streams
- Automated labeling tools that align annotations with robotic contexts and tasks
- Validation engine that detects anomalies, labeling errors, and distribution shifts in training data
- Dashboard for tracking data quality metrics and audit trails across multiple robot fleets
- API integration that connects with existing robotics simulation and training frameworks