Integrating data from diverse building systems, sensors, and vendor APIs is complex and time-consuming, hindering efficient access to real-time operational data. The lack of a standardized data layer forces developers to spend excessive time on data discovery, extraction, and normalization instead of focusing on application development.

Mapped offers an AI-powered data infrastructure platform that automates the discovery, extraction, and normalization of data from various sources within commercial and industrial spaces. The platform connects to building systems, sensors, and vendor cloud APIs, extracting data using their native protocols. Mapped then employs machine learning to automatically tag and map the raw data into industry-standard semantic data ontologies like BRICK, Haystack, and REC. This normalized data is used to build a knowledge graph accessible through a GraphQL API, webhooks, and destination connectors, enabling developers and facility operators to efficiently access and utilize real-time data.

Intelligent edge software connects to diverse real-world data sources, including building systems, devices, and vendor cloud APIs.

Automated data discovery and extraction within four hours.

AI-driven tagging and mapping of raw data into industry-standard semantic data ontologies (BRICK, Haystack, REC).

Knowledge graph accessible via GraphQL API, webhooks, and destination connectors.

Support for 70+ cloud sensor integrations, 14 geospatial formats, and 12 on-prem protocols.

Open-source data model (brickschema.org).

Mapped primarily targets property owners, facility operators, and solution providers in retail, warehouses, hospitals, airports, stadiums, campuses, offices, mixed-use developments, and data centers.