Mireye Launches Infrastructure for Physical World AI Agents

Addressing AI's Physical World Gap

Mireye has introduced an infrastructure solution for AI agents that need to interact with and make decisions about physical locations. The platform provides a unified API and server that delivers location-specific data, enrichment, tools, and signals for any US location. This development aims to solve a common problem where AI models, despite their reasoning capabilities, lack accurate information about the physical world, leading to inaccuracies or "hallucinations" in their outputs.

Origin and Problem Statement

The founder, Ansh, identified this gap while building AI agents for construction, where agents could process online information but lacked understanding of physical ground conditions. A similar issue was reported by a Fortune 500 insurer struggling with underwriting agents. Existing frontier models often fail when asked specific questions about particular places, highlighting the need for a dedicated physical world data layer for AI.

Beyond Raw Data: Decision-Making Support

Mireye is designed to be more than just a dataset with an API. It integrates cited facts, enriched address data (owner, acreage, structures, nearby power), specialized tools, and signals for changes like rezoning filings. This comprehensive approach supports the entire decision-making process for AI agents, rather than just providing raw information.

Specialized Tools for Agent Accuracy

The platform includes tools developed from observing common agent failures. These tools address issues such as agents miscalculating distances, selecting incorrect parcels for addresses, or inefficiently managing budgets. Mireye offers deterministic geometry and drive-time tools, parcel resolution, a quote endpoint for job pricing, and skills that package workflows like site screening or address underwriting.

Data Aggregation and Maintenance

A significant challenge in building Mireye involved gathering data from various sources, often county by county, and normalizing it into a single schema. The data, which includes 366 fields, must be continuously refreshed and maintained to ensure accuracy and relevance for multi-tenant usage.