AI Agents & LLMs
AI That Understands
Where Things Are.
We build AI agents and LLM-powered systems that work with spatial data natively. Not chatbots with a map layer bolted on. Production AI that reasons about location, queries geospatial databases, and takes action.
What We Build
AI Capabilities
Custom AI Agents
Autonomous agents built on Claude and OpenAI that interact with geospatial data, execute multi-step spatial analyses, generate reports, and trigger workflows. We build the agent, the tools it calls, and the guardrails it operates within.
MCP Server Development
Model Context Protocol servers that give AI models structured, real-time access to spatial databases, geocoding APIs, POI data, and analytics endpoints. Your AI gets live location context, not static training data.
RAG for Geospatial
Retrieval-augmented generation pipelines that pull relevant spatial data before the model responds. Location-aware document search, map-context injection, and spatial filtering in the retrieval step.
LLM-Powered Interfaces
Natural language interfaces for spatial queries. Ask questions in plain English, get maps, charts, and data back. We handle the translation layer between human language and spatial SQL.
Agentic Workflows
Multi-agent systems where specialized agents handle geocoding, spatial analysis, data enrichment, and report generation in coordinated pipelines. Orchestration with human-in-the-loop checkpoints.
Spatial Data for AI
Preparing geospatial data for AI consumption: feature engineering from coordinates, spatial embeddings, location-aware training datasets, and evaluation frameworks for spatial accuracy.
How We Build AI
Our Approach to AI Engineering
We start with the problem, not the model. Many teams bolt an LLM onto an existing workflow and call it AI. We work backwards from the decision your team needs to make, then design the AI system that supports it. Sometimes that is an agent. Sometimes it is a simple classifier. We pick the right tool.
Every AI system we deploy includes monitoring, evaluation, and a clear plan for when the model gets it wrong. We build confidence scores, fallback paths, and human review queues into the architecture from day one. Production AI is not a demo.
We work with Claude (Anthropic), OpenAI, and open-source models depending on the use case. For geospatial tool use and structured reasoning, we build MCP servers that give models direct access to your spatial infrastructure. No prompt-hacking, no fragile integrations.
Tools & Technologies
Our AI Stack
Claude API, OpenAI API, Google Gemini, Anthropic MCP, LangChain, n8n, OpenClaw, Databricks, Python, TypeScript, PostgreSQL/PostGIS, DuckDB, vector databases, AWS Bedrock, GCP Vertex AI, Azure OpenAI
Use Cases
Where This Applies
Automated Spatial Analysis
An AI agent that receives a location, pulls demographic data, mobility patterns, and competitor presence, then generates a structured site assessment report. What used to take an analyst a day takes the agent minutes.
Natural Language Map Queries
Internal tools where your team asks questions like 'show me all stores within 30 minutes of Munich with declining foot traffic' and gets an interactive map back. No SQL, no GIS training required.
Location-Aware Document Intelligence
RAG pipelines that understand geography. Search planning documents, environmental reports, or property records by location proximity, not just keyword matching.
Monitoring and Alerting Agents
Agents that watch spatial data streams (sensor feeds, satellite imagery, market data) and trigger alerts when conditions change in specific areas. From environmental compliance to competitive intelligence.
Your Project
Let's Build
It Together.
Tell us about your project requirements. We'll match you with the right specialists and outline a tailored approach, no commitment required.