RESEARCH REPORT
Metadata: The Critical Foundation for AI Agents & Natural Language to SQL Use Cases
How context improves AI-generated SQL accuracy by 38%, powering enterprise AI workflows at scale.
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Inside the Report
What's Inside
Take a deep dive into our testing methodology and results.
The AI-Data Challenge
The promises, hidden complexities, and failure points of natural language data access.
The Metadata Advantage
What makes metadata effective for AI, and the role of semantic layers and data catalogs.
Cost-Benefit Analysis
Upfront investments, ROI, benchmarks, and cost optimization strategies.
Implementation & Implications
The 3-step implementation guide, quick start checklist, and talk-to-data initiatives.
Why This Matters
95%
Failure rate of AI pilots in production
38%
SQL accuracy improvement with rich semantic metadata
2,662%
ROI from SQL accuracy improvements
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The Problem
Why AI fails without context.
LLMs can now generate syntactically correct SQL from natural language. But there’s a catch.
What’s syntactically correct is often semantically wrong.
Countless failed production rollouts prove that generating a query isn’t the challenge. It’s generating the right query – one that understands business logic, unwritten rules, and specific database conventions.
See how it's doneThe Solution
Our Approach
3-Pronged Evaluation Architecture
SQL generation, multi-layer validation, and LLM-as-judge
522 Evaluations
Testing the same 174 queries to achieve 95% confidence intervals
4 Principles for Metadata Effectiveness
Analysis-based test optimizations.
Real-World Value Benchmarks
Using verified stats to measure ROI
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