The Architecture Behind Accurate Agentic Analytics
Enterprise AI analytics breaks down for one reason:
Context instability.
Data is fragmented across warehouses, MCP systems, and unstructured repositories.Schemas evolve. Business definitions drift. Assumptions multiply.
Without a governed context system, AI-generated SQL and Python become inconsistent, non-deterministic, and difficult to trust in production.
This whitepaper outlines the architectural approach required to solve that problem.
- Why semantic layers and prompt engineering alone fail in complex, multi-system enterprise environments
- How to bootstrap enterprise context from schemas, query logs, BI models, documentation, and usage signals
- How to refine and stabilize accuracy through feedback loops, benchmarking, and drift detection
- How to execute deterministically across warehouses, MCP-backed systems (GA, Salesforce, HubSpot, Jira), and unstructured data
- The architectural patterns required to achieve 95%+ accuracy at enterprise scale
Trusted by industry leaders.
