Mireye

Mireye is an AI infrastructure tool for giving agents source-backed context about physical places. Its official site describes a physical-world context layer with one API for data, enrichment, tools, and signals. It also exposes an MCP tool layer so agent clients can ask questions about real locations and receive cited, timestamped, confidence-scored answers instead of rough estimates. The core problem is that general chat models often answer location questions from memory or broad assumptions. Mireye’s site contrasts that with a tool-backed answer for coordinates in Manhattan: the response includes an elevation value, datum, source, fetched date, and confidence level. That pattern is the important product idea. The agent does not just return a plausible paragraph. It returns a specific field with provenance that a user can inspect. Mireye is aimed at workflows where physical-world data changes the decision. The site lists use cases for data center siting, renewable energy screening, insurance underwriting, mortgage and title workflows, residential land diligence, and commercial lending. Example questions ask for slope, FEMA flood zone, nearest transmission line, substation voltage, interconnection queue, annual irradiance, soil drainage class, coastal exposure, wetland intersection, road access, utility service territory, and parcel acreage. The product’s source coverage is also central. The official page says Mireye uses 85 authoritative sources, primarily federal datasets, plus county records and open datasets. Source names listed on the page include USGS, FEMA, NOAA, USDA, EPA, EIA, FCC, Census, NREL, USFWS, USFS, FHWA, FAA, BTS, USACE, BLM, HUD, FHFA, BLS, NSIDC, Overture, Regrid, JRC, and Sentinel-2. Each field is described as returning a source name, source URL, fetched timestamp, and confidence level. For developers, Mireye is best evaluated as an API and MCP-backed data layer for agent workflows rather than a broad analytics dashboard. The field-request flow on the site says Mireye checks whether a requested field already exists, suggests near matches when available, and queues a new build when the field is genuinely missing. That makes it interesting for teams building agents that must reason about places but cannot maintain every dataset themselves. Pricing is listed as custom because the reviewed public page did not provide a full self-serve pricing table. Teams should contact Mireye for current API terms, dataset coverage, rate limits, and MCP deployment details. A safe pilot would start with a fixed set of locations and fields, compare answers against known source data, and only then connect the API to high-stakes underwriting, siting, lending, or operational decisions.