Every humanoid robot that ships in the next decade will be trained on data. Captured Intelligence is building the platform that produces that data, at the scale, quality, and cost structure the industry requires.
Request Investor DeckThe humanoid robot market is projected to reach $38 billion by 2035, with Goldman Sachs forecasting 250 million units deployed globally by 2035. Every single one of those robots requires hundreds of thousands of hours of human demonstration data to train the AI models that control them.
The precedent is clear: Scale AI became a $29 billion company by solving the data problem for language AI. Captured Intelligence is positioned to become the Scale AI for physical AI, a category that is larger, harder, and earlier in its development curve.
Unlike language data, physical motion data cannot be scraped from the internet. It must be deliberately captured, structured, and labeled. This creates a durable, high-margin business with structural barriers to entry that compound over time.
Every worker enrolled makes our dataset more diverse. Every dataset delivered makes our pipeline smarter. The more clients we serve, the better our quality models become. Classic two-sided marketplace dynamics with compounding defensibility.
Our proprietary $300 sensor kit delivers teleoperation-grade data quality at 1/130th the cost of competing approaches. This cost structure enables scale that is structurally impossible for hardware-first competitors.
Our Σ Pipeline improves with every session processed. Quality models trained on our own data enable higher acceptance rates, lower rejection costs, and better margins, a self-reinforcing loop that widens our lead over time.
Enterprise data supply agreements with robotics labs create multi-year, recurring revenue streams. Once a lab's training pipeline is built on our data schema, switching costs are substantial.
We charge enterprise clients $100 to $150 or more per hour of verified, structured data. We pay workers $40 to $50 per hour. The spread is our core margin, and it widens as our quality pipeline improves.
Pre-built, curated datasets for specific task categories (household manipulation, warehouse operations, healthcare assistance) licensed on an annual basis. High-margin, low-cost-to-serve.
Enterprise clients who want to run their own data collection programs can license our hardware kit and pipeline software. Recurring SaaS revenue with high switching costs.
We are raising a $5M seed round to scale our worker network to 50,000 enrolled contributors, launch our proprietary hardware kit, and deliver our first 10 enterprise client datasets. The goal is to reach Series A metrics within 18 months.
Request Full DeckFormer Head of Data at Scale AI. Led the annotation pipeline that processed 2B+ data points for GPT-4 training. Stanford CS, ex-Google Brain.
PhD Robotics, CMU. Former Research Scientist at Physical Intelligence. Designed the motion capture pipeline for the π0 model. 12 patents in embodied AI.
Former VP Operations at DoorDash. Built the gig worker onboarding system that scaled to 7M+ Dashers. Expert in marketplace operations and worker economics.
Former Senior Researcher at DeepMind Robotics. Specializes in VLA model training data requirements. Co-author of the OpenVLA paper with 1,200+ citations.
We are in active conversations with a small group of strategic investors. If you are a fund or individual with conviction in the physical AI thesis, we would like to hear from you.