Apache Ossie is an open-source specification under the Apache Incubator that standardizes how teams define and share semantic metadata across analytics, BI, and AI tools. It uses a declarative YAML format to describe metrics, dimensions, datasets, joins and relationships. Instead of each dashboard or report redefining “revenue” or “customer count,” every platform pulls from the same YAML model—so your finance, marketing, and sales dashboards all show identical numbers. The spec even includes AI context instructions so large language models can ground their answers in your official business logic. Behind the scenes, Ossie breaks models into datasets (fact and dimension tables), fields (attributes for grouping and filtering), metrics (aggregate expressions like SUM or AVERAGE) and relationships (foreign keys, simple or composite). A sample file might declare an “orders” dataset with a date dimension and a total_revenue metric defined in ANSI SQL. That single source definition then bolts onto any BI tool or data agent that supports the Ossie spec, eliminating custom integrations and reducing metric drift. More than 50 organizations have signed on so far: Snowflake, Databricks, Oracle, Salesforce, dbt Labs, Qlik, ThoughtSpot and many others. Working groups tackle areas like advanced expression syntax, model composability, catalog integration, ontology mapping and developer tooling. Recent milestones include the June 2026 launch of a Financial Services Semantic Working Group and the July 2026 rename from Open Semantic Interchange to Apache Ossie. The goal is clear: cut reconciliation costs, prevent conflicting dashboards and give AI engines reliable business context. You own your metrics definitions in a vendor-neutral spec, move them freely between platforms and trust every tool to speak the same language.