CALIFORNIA NEWSWIRE (SAN JOSE, Calif.) — Realset AI, a real-world data lab that produces training data for LLMs, frontier models and embodied agents, today announced that Realset AI and Flatkey have raised 10 million dollars in Series A funding. The funding will expand Realset’s capture network of real workplaces and studio environments, grow its pool of expert demonstrators and domain experts, and support open benchmarks that measure whether AI policies work outside the lab.
Frontier labs and robotics companies have largely consumed the text available on the internet. The next gains come from data about people doing real tasks in real places: how a worker folds laundry, loads a dishwasher, packs an order or handles a customer return. That data does not exist on the web, and simulation does not reproduce it.
Realset’s answer is that every dataset starts with a real person doing a real task in a real place. The company uses expert demonstrators rather than crowd annotators, real environments rather than simulation, and delivers de-identified data to US-hosted cloud buckets.
Realset Body captures skilled workers performing manipulation tasks in homes, kitchens, warehouses and light assembly lines, using egocentric and third-person rigs as well as bimanual teleoperation with synchronized stereo video, IMU and action logs. Data is delivered with dense action-level annotation for training vision-language-action models.
Realset Field supplies LLM and agent training data: reinforcement learning environments built from real workflows such as e-commerce operations, customer support, logistics dispatch and manufacturing procedures. Domain experts generate trajectories, preference pairs and rubrics inside the environment, with rewards tied to real business outcomes, so agentic models learn the task rather than the benchmark.
Realset Judge provides expert evaluation for agents in production: evaluation design, failure diagnosis and continuous monitoring by people who do the job the agent is replacing, plus targeted training data for the top failure modes. The same services are offered to data integration and AI solution companies that deploy agents for their own clients.
Realset is building open benchmarks so the field can measure whether a policy works outside the lab. The first, the Realset Household Manipulation Bench, evaluates open-source vision-language-action policies on folding, loading, sorting and wiping tasks captured in real kitchens and laundry rooms, with results expected in the fourth quarter of 2026. A Light Assembly Bench and a Commerce Ops Agent Bench are planned.
Realset AI is a real-world data lab and training data provider for LLMs and embodied AI. It captures expert human demonstrations, builds RL environments from real workflows and evaluates AI agents with domain experts, for frontier labs, robotics companies, and data integration and AI solution providers. Realset is headquartered in San Jose, California. Learn more at https://realset.ai
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