Problem

AI agents can reason well about digital information but lack reliable, structured ground truth about physical places. Facts about a location are fragmented across GIS systems, utility portals, census tables, government databases, and county documents, making them difficult to retrieve, reconcile, cite, and use programmatically. The result is slow, error-prone analysis and agents that must guess or return unsourced estimates when a decision depends on terrain, infrastructure, hazards, land use, or property conditions.

The clearest high-value use case is site selection for data centers and other powered real estate. Mireye describes early customers using agents to source off-market land deals 100 times faster, while its site-selection example replaces weeks of consultant work with a cited screening of slope, flood exposure, nearby high-voltage lines, substation capacity, and interconnection queues. This compresses expensive research and diligence into a repeatable software workflow.

Product / Service

Mireye is an infrastructure layer for physical-world AI agents, delivered through an HTTP API and an MCP server. Its system can geocode an address, parcel, coordinate, or place name; resolve it to a canonical location; answer natural-language questions; and fetch structured geospatial fields. The documentation describes 157 fields across terrain, land cover, built environment, utilities, parcels, climate, and hazards, with presets for workflows such as flood risk, wildfire underwriting, solar siting, wind siting, storage, and data-center development.

The product's differentiator is provenance rather than merely map access. Mireye emphasizes federal sources such as USGS, FEMA, NOAA, USDA, EPA, EIA, and Census, and attaches a source, source URL, retrieval timestamp, and confidence level to each returned field. A planner can select the relevant fields, fetch them in parallel, and return a cited answer, while developers can retrieve raw values and partial-failure details or connect agents such as Claude and Cursor through MCP. The benefit is auditable, replayable physical-world context that agents can use without every customer building and maintaining its own geospatial data pipeline.

Market

Mireye is positioned in the emerging infrastructure market for physical-world AI agents, combining geospatial data enrichment, location intelligence, and agent tooling. It competes indirectly with broad mapping and location APIs such as Google, Mapbox, and Esri; raw authoritative sources such as USGS; satellite and imagery providers; and custom internal geospatial pipelines. Mireye's intended advantage is that it packages fragmented data into agent-ready, structured, citable answers rather than providing only maps, imagery, or raw datasets.

As of August 1, 2026, Mireye is an active Y Combinator Summer 2026 company founded in 2026 and appears to be at an early-access/design-partner stage. The public evidence identifies first customers in data-center land sourcing and confirms the company is seeking to help other industries build similar agents, but it does not disclose Mireye's own revenue, customer count, or a large external funding round. It is therefore best characterized as an early commercial infrastructure startup with initial customer traction, not as a company with publicly established scale or reported ARR.