General Data Corporation
The General Data Corporation provides a continuously updated street-level spatial memory API that keeps robots and embodied systems synchronized with the physical world in real time. Operating in Paris, the platform captures roughly 500,000 frames daily, covering 90% of the city, and lets machines query current conditions beyond their immediate sensors. This addresses spatio-temporal context decay by giving autonomous systems access to what exists now, not what they remember.
- Artificial Intelligence
- Data & Analytics
- Autonomous Vehicles
- Hardware
- Logistics & Supply Chain
- Mobility & Transportation
- Robotics
- Software Only
- Space Technology
Funding
Founders
Product
Problem
Robots and autonomous systems suffer from spatio-temporal context decay—their internal maps and memories become stale as the physical world changes around them. Without current, street-level data, machines cannot navigate or act reliably in environments that shift daily due to construction, traffic, weather, or pedestrian activity.
Solution
The General Data Corporation operates a continuously refreshed street-level capture network covering 90% of Paris with 500,000 frames collected daily. This network leverages a distributed workforce moving through the city to feed a spatial memory platform that machines can query in real time via a single API. The system gives embodied systems access to what exists now, including areas beyond their immediate field of view, allowing them to navigate, map, and act with up-to-date context. The platform functions like a search engine for the physical world, delivering current spatial data on-demand to any connected autonomous system.
Target Audience
Primary customers are robotics companies and developers of embodied AI systems—including autonomous delivery vehicles, drones, warehouse robots, and smart city applications—that require current spatial context for reliable navigation and operation.
Features
- Continuously refreshed street-level capture network producing 500,000 frames per day with 90% city coverage in Paris
- Single unified API that provides real-time spatial context to robots and embodied systems
- Distributed capture model using pedestrian networks, enabling constant updating without dedicated fleets or infrastructure
- Privacy-by-default architecture designed into the data pipeline
- Spatial memory platform that resolves spatio-temporal context decay, giving machines access to locations beyond their immediate sensing range