Organizations are starting to use AI to generate SQL but analysts worry about correctness, data leaks, inefficient or unsafe queries (e.g., Cartesian joins, missing WHERE filters), and cost implications on cloud warehouses. There's no easy guardrail that programmatically validates AI-written SQL before it runs.
Why now: Growing use of LLMs to auto-generate SQL and rising cloud warehouse costs make automated pre-execution auditing both valuable and feasible with today's static analysis and cost-modeling techniques.
A service that scans SQL queries (especially AI-generated), runs static analysis and optional dry-run on sample data, flags potential correctness/security/performance issues, estimates cost (bytes scanned), and automatically rewrites or suggests optimized versions. MVP: linter rules, heuristic detectors (missing join keys, unbounded aggregations), cost estimator for Snowflake/BigQuery, and a 'safe run' sandbox.
Built for: analytics teams and consulting firms using AI-assisted query generation against cloud warehouses
Business model: subscription
AI SQL Safety & Optimization Auditor targets a medium-sized market ($100M–$1B TAM). Existing solutions are incomplete or outdated — there's clear room for a better product.
Underserved
Medium
Startup (3 Months)
High
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Includes: 10 competitors found, 10 risks identified, full business plan, market research