Float Runs an AI Energy Company on a 3-Person Team with Tiger Data
Float, an AI energy company, operates with just a three-person team by leveraging Tiger Data's tools for data management and analytics. The small team efficiently handles complex energy data and AI model deployment, demonstrating how streamlined operations and powerful data platforms can enable startups to punch above their weight in capital-intensive industries.
Background
- Tiger Data is a modern data stack (a "data lakehouse") built on Apache Iceberg, designed to make large-scale analytics simple and fast.
- Float is a startup that uses AI to optimize energy consumption for industrial facilities; its core product uses reinforcement learning to control heating/cooling systems in real time.
- The article profiles how Float achieved this with just three staff, using Tiger Data to handle all their data engineering and pipeline infrastructure without hiring a dedicated data team.
- This matters because AI-driven energy optimization is a computationally heavy, data-intensive field. Float's lean approach shows how smaller teams can now build sophisticated AI products thanks to managed data platforms that abstract away infrastructure complexity.