This article details how to build a trusted semantic layer for enterprise AI using Snowflake Horizon Context. It emphasizes that a semantic layer acts as the trust boundary for AI, ensuring that metrics are governed, versioned, and certified. The author advocates for domain-oriented semantic models over monolithic ones to clarify ownership, allow independent evolution, manage complexity, and align AI agents with specific business domains. A complete walkthrough of a production-grade revenue semantic view is provided, highlighting key design decisions. AI
IMPACT Establishes a framework for improving AI reliability in enterprises by ensuring data governance and metric certification.
RANK_REASON Article describes implementation patterns for a specific product feature (Snowflake Horizon Context) to improve AI trustworthiness, rather than a novel release or research.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
