Capabilities
Geospatial, data, and AI systems that hold up in production
Geospatial work comes first. Around it: the data engineering and ops analytics that feed it, and the retrieval, ML, and LLM layers that sit on top. Each area links to the work it comes from.
Geospatial Data & Analysis
Geographic data built from disparate sources and made usable by people who are not GIS specialists: spatial ETL, interpolation across mismatched geographies, Earth observation data, and the analytics and AI on top.
Data Engineering & Ops Analytics
Pipelines, warehouses, and reporting layers that turn operational data from many systems into numbers people can act on, with health checks that say whether the data is current, not just whether the job ran.
RAG & Knowledge Retrieval
Retrieval-augmented generation over real documents: answers that cite their source, retrieval that can run locally, and costs with a hard ceiling.
MLOps & Model Deployment
Getting models and LLM features into production and keeping them honest: evaluation, cost and latency tracking, human review, and deploys that can be rerun safely.
Agentic AI & LLM Automation
LLM agents that take real action within tight limits: constrained configs, least-privilege access, and a human sign-off before anything ships.
Platform Engineering
The backend that data and AI features sit on: APIs, schema as code, deploys you can rerun, and admin tools people actually use.