Problem

Humanoid and other embodied-AI robots face a fundamental data bottleneck: they need to learn how people move through messy, varied physical environments, but conventional factory datasets capture repetitive tasks in standardized settings. Asimov targets the resulting gap by collecting everyday human activity in homes, kitchens, offices, hotels, restaurants, warehouses, and other workplaces. The core use case is supplying the diverse movement and interaction data needed to train robots that must operate beyond controlled factories.

The pain is both technical and economic. Robotics companies need large volumes of high-quality, annotated demonstrations, while collecting them manually or building dedicated environments is expensive and lacks environmental diversity. Asimov also addresses the supply-side economics by turning ordinary work and household activity into paid data collection; participating businesses can have worker salaries covered while their operations continue normally.

Product / Service

Asimov operates a managed, vertically integrated data-collection service for robotics teams. Individuals apply with their location, phone model, availability, and access to relevant tasks, then use a lightweight phone head mount and an app to record assigned activities. Businesses can have employees wear recording equipment while doing their normal jobs. Asimov handles hardware distribution, collector training, quality assurance, annotation, and post-processing, delivering clean datasets ready for model training.

The model creates a two-sided benefit: collectors are paid for approved recording time, while robotics customers receive real-world, multimodal data from many people and environments without having to build their own collection network. For larger business partnerships, Asimov offers salary coverage and revenue sharing from the resulting datasets, while its privacy guidance and non-disruptive workflow are designed to reduce operational and data-governance friction.

Market

Asimov competes in the emerging embodied-AI and robotics-training-data market, with a particular focus on humanoid robots. Its differentiation is a broad, real-world supply chain rather than a factory-only or lab-only dataset: the company combines households and businesses, proprietary collection hardware, and annotation tooling into an end-to-end pipeline. A close publicly identified competitor is YC-backed Sensei, which likewise provides outsourced robotics training-data collection using hardware for human demonstrations; larger data-services companies such as Scale AI are adjacent alternatives, although the available evidence does not establish an exhaustive competitor set.

The company is early but shows meaningful commercial traction. It is an active Winter 2026 Y Combinator company, says it is collecting across three continents and is trusted by frontier robotics teams, and reports a network of thousands of households and businesses, tens of thousands of hours already shipped to frontier robotics teams, and customers among the largest robotics labs. Public materials reviewed do not disclose revenue, so Asimov should not be definitively labeled pre-revenue; the best-supported description is an early-stage company with active customer delivery and expanding data-collection operations.