Mireye provides a single API that lets AI agents access real‑time physical‑world data, enrichment, tools, and signals from authoritative sources such as USGS, NOAA, and EPA. By returning cited, timestamped results with confidence scores, it enables agents like Claude or ChatGPT to answer queries—e.g., precise elevation lookups—without manual data integration.
Funding
Funding not disclosed
Founders
Product
Problem
AI language models and autonomous agents often cannot access up-to-date, authoritative physical‑world information such as elevation, land use, weather, or environmental regulations, leading to vague or unverifiable responses. This limits their usefulness in applications that require precise geographic or environmental context.
Solution
Mireye offers a single API that connects AI agents—including Claude, ChatGPT, Gemini, and custom models—to real‑time physical‑world data sourced from more than 30 government and scientific agencies. When an agent queries a datum, Mireye returns the value together with a citation, fetch timestamp, and confidence level, enabling the agent to provide verifiable answers. The platform also supplies enrichment fields, proprietary signals, and tool integrations that extend raw data with derived insights. If a requested field is not yet cataloged, Mireye automatically queues it for future inclusion, ensuring continuous expansion of the data catalog. By standardizing access to diverse geospatial and environmental datasets, Mireye allows agents to make accurate, data‑driven decisions about geography, elevation, climate, and related attributes.
Target Audience
Primary customers are developers and enterprises building AI assistants, autonomous agents, or decision‑support systems that require reliable geospatial and environmental data, as well as GIS platforms seeking to augment AI capabilities with real‑time physical‑world context.
Features
- Unified REST API that supports queries from any AI model or custom agent
- Direct integration of authoritative sources such as USGS, NOAA, EPA, Sentinel‑2, and dozens of other federal datasets
- Returned results include source citation, fetch timestamp, and confidence score for traceability
- Automatic enrichment of raw data with derived fields, proprietary signals, and tool outputs
- Dynamic catalog growth: uncatalogued field requests are queued for automated data acquisition and future availability
- Support for multiple data types including elevation, land cover, weather, demographic, and infrastructure attributes