Spatialbound is an AI simulator and design platform for the physical world, combining parametric architecture design with a 4D digital twin of any city. It enables architects, urban developers, and robotics teams to run physics, solar, and compliance simulations, and generate synthetic training data for physical AI models—all from a browser.
Funding
Funding not disclosed
Founders
Product
Problem
Architects, urban developers, and robotics teams lack a unified platform to design, simulate, and validate projects within real-world contexts. Traditional tools separate design from environmental analysis, compliance checking, and physical-AI training data generation, forcing teams to use multiple disconnected software packages and manual workflows.
Solution
Spatialbound provides a browser-based AI simulator and design platform that integrates parametric architecture design with a photoreal 4D digital twin of any city worldwide. Users can design buildings, urban layouts, and product geometry using a node-based parametric studio, then run physics, solar, wind, and pedestrian simulations directly within the real-world context. The platform includes AI compliance nodes that evaluate designs against planning and zoning rules in real time, while its agentic AI partner, Fred, assists with geometry generation and implementation planning. Robotics and physical-AI teams can use the vector twin as ground truth for simulating vehicles, drones, and humans, and export synthetic data with depth, normal, and segmentation views for training perception models.
Target Audience
Primary users are architects, urban-development teams, robotics labs, and AI companies that need integrated design, simulation, and synthetic data generation within real-world digital twins.
Features
- Parametric Design Studio with 100+ nodes for geometry, NURBS, boolean operations, sweeps, and lofts
- 4D digital twin of any city with real terrain, buildings, streets, and weather data
- AI compliance nodes that evaluate designs against planning, zoning, and permitted-development rules during modeling
- Robotics simulation supporting drivable vehicles, walking humans, drones, paragliders, and quadrupeds
- Physical-AI synthetic data generation with depth, normal, and segmentation render modes
- Fred, an agentic AI design partner that generates implementation plans and maintains version-controlled digital threads
- Map Designer with multi-layer GIS, H3 hex grids, spatial analytics, and node-based map generation
- Site analysis tools for solar radiation, land use density, flood risk, and environmental assessments