Introduction

Physical AI Needs Data That Doesn't Exist Yet

Training a language model is hard, but at least the internet hands you a corpus. Physical AI is a fundamentally different problem: the training data doesn't exist until someone drives every street, processes every frame, and delivers it as structured, geo-referenced ground truth. There is no "internet of the physical world" to scrape — and the real world changes constantly, so a one-time capture pass is never enough.

This is the bottleneck holding back autonomous vehicles, robotics, world models, and every other system that needs to understand and navigate physical space. The data gap is massive, and it's not closing on its own.

Most companies attempting to close it either build expensive first-party fleets that can't scale, or rely on commodity cameras that can't produce the quality. We took a different approach: purpose-built hardware deployed at massive distributed scale, with a software stack designed from day one to close the loop from capture to API.

How Bee Maps Works: The Data Layer for Physical AI

Bee Maps is built on the Hivemapper Network — a decentralized infrastructure of purpose built dashcams collecting fresh, street-level data across 37% of the world's roads. Instead of relying on dedicated mapping fleets, we help turn everyday driving into continuous, high-quality capture of the physical world.

At the core is Map AI — an engine that transforms billions of video frames of road imagery into structured, geo-referenced data: the raw material that AI world models need to understand how the physical world actually looks and behaves.

The Problems We're Solving

• Physical AI is data-starved. Autonomous vehicles, robots, and world-model researchers need massive volumes of fresh, diverse, geo-referenced visual data. It barely exists today.

• The real world changes constantly. A one-time mapping pass produces a digital museum. World models require continuous capture — days and weeks fresh, not months and years stale.

• First-party fleets don't scale. Dedicated collection vehicles are expensive and slow. Covering the world's roads requires a fundamentally different architecture.

• Maps alone aren't enough. Autonomous and ADAS systems don't just need road geometry — they need to understand context, detect changes, and reason about what's happening on the ground in near real-time.

• Today's data doesn't understand the "why." When traffic drops from 40 mph to 10, is it construction, a fender-bender, or a multi-hour closure? Continuous visual capture can answer that. Static maps never will.

Getting Started

This documentation covers everything you need to build on Bee Maps — from imagery and map feature APIs to AI event data and world model training datasets. Use the navigation to explore specific topics, or search for what you need.

Three ways to start:

• Build with our data. Access fresh imagery, map features, and AI events through the Data APIs.

• Use AI agents to work with the data. Query, analyze, and act on world model data through Bee Maps Agents.

• Grow the map. Deploy a Bee dashcam and start contributing fresh capture to the network.

– The Bee Maps Team