When artificial intelligence gets asked about the physical world, it tends to make things up. Ask a language model about the soil drainage on a specific parcel in Oregon or the flood risk at an address in Florida, and you're likely to get confident nonsense.
Mireye, a participant in Y Combinator's Summer 2026 accelerator program, thinks it has an answer. The startup launched an API designed to let AI systems retrieve verifiable facts about any US location across hundreds of data fields, from terrain elevation to climate patterns to utility infrastructure. Every response comes with source citations, timestamps, and a confidence rating.
The approach reflects a broader shift in how companies are trying to tame the hallucination problem that has plagued AI applications dealing with real-world geography. While language models excel at explaining concepts, they struggle with the kind of granular, location-specific information that developers increasingly need for applications in real estate, logistics, and environmental analysis.
"Absence is an answer," the company notes in its technical documentation—a philosophy that runs counter to how most APIs handle missing data. Rather than filling gaps with estimates or defaulting to silence, Mireye's system explicitly flags when information isn't available, returning one of three states for each query: present, absent, or failed.
The API pulls from an amalgam of public and licensed datasets: USGS elevation records, NOAA climate data, EPA facility registries, OpenStreetMap infrastructure. Fields span terrain characteristics, land cover, zoning regulations, utility networks, property parcels, climate normals, and natural hazards. Coverage extends across the United States, with partial support for Canadian locations in drive-time calculations.
Four endpoints handle different query types. One accepts plain-English questions and plans data retrieval with inline citations. Another fetches facts by coordinate or street address. A proximity endpoint calculates travel matrices for up to 500 locations simultaneously, while a field-request system lets developers commission coverage for data layers not yet indexed.
The transparency extends to the metadata. Each data point carries the source agency or dataset, a URL, and a confidence bucket. No proprietary scoring, no black boxes. Sources range from federal agencies to community-maintained resources like OpenStreetMap and datasets from the Overture Maps Foundation.
Founder Ansh Chokshi, writing in an online forum around September 2026, emphasized that the system is designed with AI agents in mind. It returns structured instructions on handling null values and partial failures—details that matter when an algorithm needs to make decisions based on incomplete information.
The company released a Python package that bootstraps federal and open-source geospatial assets locally, including support for the Model Context Protocol, a standard that's gained traction for connecting AI systems to external tools. OpenAI added MCP server support to parts of its platform last year, and several geospatial vendors have followed with their own integrations.
Use-case templates on Mireye's site show the range of applications. One retrieves elevation, slope, soil drainage, and land-cover data with full source attribution. Another analyzes retail locations by identifying shared physical characteristics across store sites, combining Mireye's datasets with points of interest from OpenStreetMap.
The space is getting crowded, though perhaps that validates the opportunity. Mapbox announced its own AI-focused location infrastructure in September 2026, including an agentic mapping engine and a places database with a quarter-billion entries. CARTO rolled out tools for connecting enterprise GIS workflows to AI agents earlier in 2026. Esri, the industry stalwart, has been emphasizing geographic context as essential guardrails against AI hallucinations.
Specialized providers are staking out niches. Regrid focuses on parcel data through both API and MCP channels. LightBox maintains property records for more than 150 million US parcels. Foursquare, long a source for location visitation data, is being acquired by Infillion in a deal announced last fall.
Analyst projections suggest the market opportunity is substantial. Gartner pegged spatial AI as a multi-billion-dollar segment over the next decade, while the broader geospatial analytics market has been valued above $100 billion.
Mireye received Y Combinator's standard $500,000 investment in 2026 as part of its cohort. No subsequent funding rounds have been announced. Chokshi has indicated the company plans to expand its catalog through on-demand requests, deploying what he described as long-running agents to crawl and index new data sources as customers need them.
The pitch is straightforward, if ambitious: index the physical world and make it as searchable as the web. Whether AI systems actually need that level of geographic specificity, or whether simpler approximations suffice for most applications, remains an open question. But for developers building tools that operate at the intersection of artificial intelligence and physical reality, having facts that come with receipts might be worth the added complexity.
