We publish open research at the intersection of AI and spatial reasoning — contributing tools, frameworks, and ideas that help anyone build smarter infrastructure for the real world.
Teaching AI to reason
about the physical world
Most AI research focuses on language and vision. We focus on the third modality: spatial reasoning over the physical world. Our work sits at the intersection of large language models, mathematical optimization, and geospatial systems.
We believe the most impactful AI research will be work that helps everyone — not just us — build systems that understand physical constraints, plan infrastructure autonomously, and make critical spatial decisions with precision. We open-source our findings so the entire community can build on them.
Spatial Reasoning Frameworks
Encoding geographic context — distances, topologies, physical constraints — as structured LLM input. Enabling any AI system to reason about the real world, not just text.
Domain-Agnostic Agent Architecture
A single agent design that adapts to any infrastructure domain through configuration. Our approach shows that domain expertise doesn't require domain-specific code.
Hybrid Optimization Research
Combining multiple mathematical optimization families into a unified spatial solver — published so others can apply these techniques to their own infrastructure challenges.
Recent papers
Open research on spatial AI, autonomous design, and optimization — published to advance the field and invite collaboration.
More publications coming soon
Open collaboration
Build spatial intelligence with us
We're looking for researchers and engineers who want to teach AI to understand the physical world. Remote-first. Research-driven.