Mireye
API that gives AI agents reliable, sourced facts about any US location — terrain, flood risk, building data — so an agent can answer questions about the physical world instead of guessing.
🔗 Visit Mireye
Description
An AI agent can write code or summarize a document convincingly, but ask it whether a specific address floods, what the terrain looks like nearby, or what's actually built there, and it has no real way to know — that information lives scattered across dozens of separate government datasets, not in anything an LLM was trained on directly. Mireye built a single API that answers exactly those physical-world questions, pulling from official government sources and citing exactly where each fact came from, so an agent gets a real, sourced answer instead of a plausible-sounding guess. Mireye covers roughly 296 geospatial data fields across terrain, land cover, buildings, utilities, parcels, climate and hazard risk, sourced exclusively from federal agencies (USGS, NOAA, USDA, FEMA-adjacent sources, Census and others) with provenance tags and confidence levels attached to each answer. It offers three endpoints — a natural-language Q&A endpoint, a direct-retrieval endpoint, and a field-discovery endpoint — plus native MCP integration for agent tools like Claude Desktop and Cursor, with 15 pre-built use-case presets covering things like flood risk, wildfire risk, and solar/wind site assessment.
💬 Our review
The short version: Mireye is solving a specific, unglamorous but real problem — AI agents have no reliable way to answer "what's actually at this location" questions, since that data lives in scattered federal datasets rather than anything a language model was trained on — and it's building a clean, sourced API specifically for agents to query it.
The provenance-and-confidence design is the part that matters most for anyone using this in a real decision (insurance underwriting, lending risk, site selection): every answer is tagged with exactly which federal source it came from and how confident the system is, rather than an LLM's own unsourced-feeling assertion, which is a meaningfully more trustworthy pattern for high-stakes physical-world questions. Its use-case presets (flood risk, wildfire risk, solar siting) show the team has thought about actual buyer workflows rather than just exposing raw data and leaving integration entirely to the customer. The honest caveats: it's an extremely early company (founded 2026, 2-person team, still in early access with unpublished pricing), currently limited to the continental US plus territories despite an eventual "index every inch of the earth" ambition, and reported query latency (6-20 seconds typical, up to 90 seconds at the tail) is slow enough that it's better suited to an agent doing careful research than one needing a fast, real-time answer. A separate "open source" claim surfaced in research applies to a near-inactive component, not the hosted API itself — the real product is clearly a metered, hosted service still finding its pricing model.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Pros
Every answer tagged with source provenance and a confidence level, not an unsourced LLM guess
296 fields across terrain, buildings, climate and hazard risk, from official federal sources only
Native MCP integration for direct use by agent tools (Claude Desktop, Cursor)
15 pre-built use-case presets (flood risk, wildfire risk, solar/wind siting)
Cons
Extremely early-stage (2026-founded, 2-person team), unpublished pricing
US-only coverage despite a global long-term ambition
Query latency (6-20s typical, up to 90s) is slow for real-time agent use
❓ Frequently asked questions
- What kind of questions can Mireye answer?
- Physical-world facts about any US location — terrain, land cover, building data, utilities, parcel boundaries, climate and hazard risk (like flood or wildfire risk) — sourced from official federal datasets rather than an AI model's guess.
- How do I know an answer is reliable?
- Every response is tagged with the specific federal source it came from and a confidence level, rather than being presented as an unsourced assertion.
- Does it work directly with AI agent tools?
- Yes — it has native MCP (Model Context Protocol) support for tools like Claude Desktop and Cursor, alongside a plain REST API.
- Is it worth the money compared to alternatives?
- Pricing isn't public yet since it's in early access — for a team building agents that need reliably sourced physical-world facts (not just US property records like RealEstateAPI), it's worth evaluating directly, keeping in mind the company's very early stage.
- Which tool should you pick for your case?
- Need sourced, confidence-scored facts about any US location for an AI agent: Mireye. Need specifically real estate/property transaction data for a proptech product: RealEstateAPI.