About

A Robotics Startup Building Product Infrastructure Around Robot Deployments

Learn what ROBOFLOW AI does, the problems we solve for robot operations teams, and who is building the platform.

What ROBOFLOW AI Does

Robots generate terabytes of sensor data every day — camera feeds, LiDAR point clouds, IMU readings, depth maps. But most of that data sits unused because cleaning, labeling, and organizing it takes weeks of manual work. RoboFLO AI fixes that. Our AI-powered SaaS platform automatically ingests raw sensor data, auto-labels it using advanced AI models, cleans and deduplicates it, and versions every dataset — so your team can go from raw robot data to trained AI models in hours, not months.

Robotics teams waste 80% of their time preparing data instead of training AI models. Manual labeling is slow, expensive, and error-prone. RoboFLO AI automates the entire data pipeline so teams can focus on building smarter robots.

Who It Is For

Robotics engineers, AI/ML teams, autonomous vehicle developers, drone operators, and warehouse robotics companies who need clean, labeled datasets to train their AI models.

The robotics AI market demands massive volumes of high-quality training data. RoboFLO AI is the SaaS data engine that sits between raw robot sensors and ML training pipelines — fully cloud-based, pay-per-use, and powered by AI.

Current public stage: AI-Powered SaaS

Team

Meet The Founders

RoboFLO AI is built by a founding team with deep experience in robotics, AI infrastructure, and enterprise SaaS. We're on a mission to make AI-powered data processing accessible to every robotics team in the world.

Kaushal Agarwal

CEO

Co-founder and CEO of RoboFLO AI. 10+ years in robotics and enterprise software. Leads company strategy, product vision, and go-to-market. Deep domain expertise in AI-powered data pipelines, industrial automation, and scaling robotics AI from prototype to production.

LinkedIn Profile
John Dsouza

CTO

Co-founder and CTO of RoboFLO AI. 10+ years building distributed systems, cloud-native platforms, and AI infrastructure. Architects the AI data engine that powers automated labeling, sensor data processing, and ML pipeline integration at scale.