Enterprise semantic layer for governed AI and analytics consumption
Problem: Analytics and AI consumption needed to scale from trusted metric definitions instead of duplicated logic, disconnected dashboards, or unsafe direct access to business data.
Impact: Established a stronger foundation for consistent metrics, governed self-service, and AI-driven data consumption from approved enterprise definitions.
- Architected and delivered an enterprise semantic layer using Cube as a governed source of truth for business metrics.
- Exposed trusted metrics through a Model Context Protocol interface for standardized consumption by AI agents, LLM-powered applications, conversational analytics, and self-service dashboards.
- Connected governance practices to how teams define, discover, reuse, and safely consume business logic without disclosing confidential internal architecture or company data.