Spatial search
Query a scene in natural language — wood, metal, glass — and get a relevance heatmap of matching objects and materials.
AI Agents
Orchestrating agents that can actually get work done.
Our research focuses on making multi-agent systems dependable in production. That means disciplined tool use, recoverable failure modes, and evaluation methodology that scales beyond one-shot benchmarks. We contribute back to open-source and publish where we can.
- Instruction-based evaluation frameworks
- Tool routing and fallback strategies
- Cost-aware agent planners
- Human-in-the-loop handoff patterns
What is an AI agent?
AI agents are software systems that take on tasks the way a human specialist would: they act autonomously, make decisions, and use tools to get there. Today they already handle work in marketing, sales, software development, data acquisition, and analysis.
Multi-agent systems
Instead of building one super-brain agent, we compose teams of specialized agents. A collection of focused experts solves tasks more efficiently — and more reliably — than a single monolithic model.
- Each agent uses the model and tools best suited to its job
- Cost-effective while maintaining high performance
- Modular and flexible to adapt to your business operations
Architecture
Every agent runs on a foundation model with clear role instructions, a persona, and context. Agents access background tools and wait for instructions from an AI orchestration layer — while humans stay in control and initiate commands from the outside.
Human operator
AI orchestrator
LLM
AI agents
S-Agent
Tools
Databases
Browsing
Knowledge graph
S-Agent
Our S-Agent is a spatial-intelligence agent in development that brings real-world spatial data into agentic systems. It already reconstructs rooms in 3D, finds objects and materials, and separates scenes with segmentation masks. Next up: deeper spatial understanding and intuitive physics, fully compatible with multi-agent setups.
Tooling
Three core capabilities power the S-Agent today.
3D reconstruction
Photorealistic 3D reconstruction of real environments as the foundation for search, measurement, and simulation.
Grouping & separation
Segmentation masks split a scene into objects and regions, so agents can reason about the parts — not just the pixels.
Spatial memory
Spatial memory means remembering and using information about the physical environment — Spatial Information Retrieval (SIR) that enhances your LLM applications.
- Spaces can be used as context for LLMs
- Better navigation within 3D environments
- Creation of immersive user experiences
- Text-based regulations can be verified
- More personal assistance with spatial understanding
Inference
QuestionResponse
S-Agent
Retrieve contextText | Image | Video
LLM | VLM
Data acquisition & retrieval
Spatial database
Spatial intelligence
Images & video
- Monitor folder
- Upload
- Streaming
Use AI technology purposefully as a tool for your team.
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