Mireye is building the missing link for physical world AI agents

Most LLM agents are incredibly smart about the internet but completely clueless about the actual ground they are trying to reason about. You can ask Claude or GPT-4 to write a Python script in seconds, but if you ask it for the specific sewer main locations in a random North Carolina county or the exact acreage of a specific parcel, it will almost certainly hallucinate a plausible-sounding lie. This is the massive wall developers hit when moving from digital-only workflows to real-world applications like construction, insurance underwriting, or logistics.

Mireye (a YC S26 company) is attempting to solve this by providing a dedicated infrastructure layer specifically for "physical world" data. Instead of just being a static dataset, they are building an API and MCP (Model Context Protocol) server that provides data, enrichment, tools, and signals for any US location.

Moving beyond simple datasets

The founder, Ansh, realized this while building construction agents. He found that frontier models fail when they hit specific local queries. Even more telling, a Fortune 500 insurer mentioned their engineers had abandoned underwriting agents because the models couldn't handle the granular, local specifics required for risk assessment.

The core of Mireye isn't just a database; it's a decision-making engine. A typical workflow might involve:

• Taking a bare address and enriching it with owner info, acreage, and nearby power infrastructure.
• Using deterministic tools for geometry and drive-time calculations (because LLMs are notoriously bad at "eyeballing" distances).
• Triggering signals when something in the real world changes, such as a new rezoning filing.

Solving the "Null" problem in AI workflows

One of the most interesting technical insights from their development is how they handle data gaps. In traditional data science, a "null" value is a headache. In AI, a "null" is a hallucination trigger. If a model sees a missing value for a flood zone, it often tries to "fill the silence" with a number that sounds right.

To combat this, Mireye implements a strict response schema. Every field returns one of three states:

• ok: The data is present and verified.
• absent: The data definitely does not exist or wasn't found.
• failed: There was an error in retrieval.

By forcing the model to recognize absence rather than guessing, they build the "refusal" capability that makes the data actually trustworthy for high-stakes industries like insurance.

Technical implementation and testing

If you want to test this in your own AI workflow, they have made it fairly low-friction. You can grab a free key at their site (they offer 5,000 credits with no card required) and integrate their skills directly.

The fastest way to see if it works is to paste their skills definition into your agent:

https://www.mireye.com/skills.md

They currently maintain a catalog of 366 fields, which they've had to normalize from wildly different sources—ranging from Maryland's "Hidden Property Owner Names" datasets to manual public records requests for unindexed infrastructure. They've even implemented an "on-demand indexing" feature where, if you request a field they don't currently support, a long-running agent will research, collect, and verify that data within about 24 hours to add to the index.