Hi HN, I'm Ansh, founder of Mireye. I'm building the infrastructure AI agents use to make decisions about physical places: data, enrichment, tools, and signals for any US location, behind one API and MCP server.

Before Mireye I was building construction agents and hit this wall myself: my agent could reason about anything online but knew nothing about the ground under it. Then a Fortune 500 insurer told me their engineers had given up on underwriting agents for the same reason. Frontier models keep hallucinating when asked a specific question about a specific place.

Mireye is not a dataset with an API on top, because facts alone are not a decision. An agent runs the whole job through it: cited facts, a bare address enriched into owner, acreage, structures, and nearby power, tools for the operations models get wrong, and signals when something changes, like a rezoning filing. The tools came from watching agents fail. Each failure became something an agent can call: deterministic geometry and drive-time tools, parcel resolution, a quote endpoint that prices a job before it runs, and skills that package whole workflows.

The hard part surprised me. Every source has to be gathered (sometimes county by county), normalized into one schema, contracted, and kept fresh. We run that loop for 366 fields today. The deeper problem is meaning. Two counties publish a field with the same name and it means different things. So we type absence. Every field returns ok, absent, or failed. Customers have told me the refusals are why they trust it.

People have built things I didn't plan for: insurance teams screening portfolios, a proptech cleaning addresses, a robotics company sourcing warehouses, data center site selection, drone deployment planning, school bus routes, and signals for human trafficking investigations.