> For the complete documentation index, see [llms.txt](https://docs.akenza.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akenza.io/data-fusion-layer/data-fusion-layer.md). # Overview The Data Fusion Layer structures and unifies multi-device IoT telemetry into a standardized digital twin model using an optimized Brick Schema ontology. The **Data Fusion Layer** serves as the foundational data structure for smart facility management. By normalizing heterogeneous payloads from disparate IoT hardware, it creates an accurate digital twin that mirrors physical building hierarchies, assets, and real-time environmental conditions. ### Core Capabilities * **Brick Schema Ontology:** Leverages a tailored subset of [Brick Schema](https://brickschema.org/) to standardize relationships across sites, floors, zones, BMS systems, and IoT sensors. * **Multi-Device Aggregation:** Merges data streams from multi-vendor sensors into a unified, spatial context rather than isolated device silos. * **Genio AI Integration:** Supplies clean, contextualized data to Genio for advanced analytics, predictive maintenance, and operational insights. * **Context-Aware Reporting:** Powers intuitive dashboards that display telemetry within its exact spatial context (e.g., room occupancy, floor-level energy use). * **Abstracted API Access:** Provides a single, standardized API interface to feed normalized data directly into custom applications or third-party tools, such as tenant employee apps, IWMS, and enterprise ERP systems. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.akenza.io/data-fusion-layer/data-fusion-layer.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.