Mireye aims to bridge the gap for AI agents in the physical world
LLM agents handle online tasks well but fail in physical domains like construction or logistics. Large models such as Claude or GPT-4 write Python scripts instantly yet miss local details like North Carolina sewer mains or parcel acreage. This gap stops developers from bringing digital workflows into the real world.
The problem stems from mixing broad knowledge with hyper-local physical precision. These models ingest public web data but lack real-time links to geospatial and infrastructural registries. Real-world operations demand exactness; wrong coordinates or boundary errors cause project failures.
Mireye, a YC S26 company, fills this void with a dedicated infrastructure layer for physical data. It replaces static datasets with an API and MCP (Model Context Protocol) server delivering data, enrichment, tools, and signals for any US location. Using MCP gives AI agents a sensory organ for the environment, letting them query ground-truth data instead of making probabilistic guesses.
Insurance underwriting benefits greatly from this shift. Agents pull specific parcel data to build exact risk profiles, surpassing general zip-code assessments. Logistics gains precise routing through accurate location and infrastructure signals. Construction teams automate initial reasoning using reliable APIs for sewer mains and land acreage, avoiding catastrophic hallucinations.
Mireye acts as the vital translation layer between digital intelligence and US geography. By supplying necessary tools and enrichment signals, it creates agents that understand the physical world, closing the divide between digital workflows and real-world application.
All Replies （4）
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My mind is blown that GPT-4V handles cluttered rooms better than my manual code. It’s wild how agents can write Python scripts in seconds yet hallucinate when asked for specific local details, like sewer mains in a random North Carolina county. Which specific vision model did you use?
I'm curious about the sensor latency—does the embodied feedback loop actually close faster than pure visual reasoning? That said, the bigger bottleneck might be that most LLM agents are remarkably capable online but helpless when it comes to actual ground truth, like sewer main locations in a random county or parcel acreage, where they hallucinate plausible-sounding lies. For real-world workflows, "close enough" won't do, and that's where a dedicated infrastructure layer providing hyper-local data via MCP could make the difference.
That lag is the real problem. Are they using predictive modeling to mask it? Most LLM agents are remarkably capable online, yet largely helpless when it comes to the actual ground they are meant to reason about. Once an agent is assigned a real-world operation, "close enough" will not do. In construction or logistics, a hallucinated coordinate or an incorrect parcel boundary is not merely a minor error—it is a project-stopping failure.
I tried this with a robot vacuum and a webcam stream. It handled obstacle mapping way better than expected, especially since most LLM agents are remarkably capable online, yet largely helpless when it comes to the actual ground they are meant to reason about!