About OSI

The Open Semantic Interchange (OSI) is a collaborative, open-source effort dedicated to standardizing and streamlining semantic model definitions across the data analytics, AI, and BI ecosystem.

Why OSI?

The Challenge: Semantic Fragmentation

• Metric Drift: Inconsistent KPIs across different dashboards.
• Manual Translation: Costly, error-prone reconciliation efforts.
• Hallucinations: Unreliable AI grounding from conflicting data logic.
• Integration Debt: Complex N-to-N custom integrations between proprietary tools.

The Solution

• Single Source of Truth: Unified semantic and metric definitions.
• Native Interoperability: Direct exchange between platforms and AI agents.
• Trusted AI Grounding: Agents reasoning accurately based on business logic.
• Reduced TCO: Lower costs through automated model exchange.

Core Classes

The OSI specification defines the following core classes:

• Semantic Model: The top-level container that represents a complete semantic model, including datasets, relationships, and metrics.
• Data Sets: Logical datasets represent business entities or concepts (fact and dimension tables). They contain fields and define the structure of the data.
• Fields: Row-level attributes that can be used for grouping, filtering, and in metric expressions.
• Measures: Quantitative measures defined on business data, representing key calculations like sums, averages, ratios, etc. Metrics are defined at the semantic model level and can span multiple datasets.
• Dimensions: Categorical attributes (Where, When, Who).
• Relationships: Relationships define how logical datasets are connected through foreign key constraints. They support both simple and composite keys.