Developers increasingly rely on editor-embedded AI autocompletes, and those assistants sometimes overwrite SQL string literals in ways that break queries, introduce logic bugs, or create injection-like risks. This affects backend engineers, data analysts, DBAs, and anyone editing SQL in IDEs or managed dashboards (Supabase, Neon, AWS Console), a broad slice of the roughly 40M-developer market. You could build a contextual guardrails platform that detects SQL string-literal boundaries at the AST/token level and either blocks, sanitizes, or surfaces a review step before an AI-generated edit is applied; delivery would include IDE plugins, browser extensions for web SQL editors, and optional server-side middleware for hosted consoles. Core features would be model-agnostic edit interception, deterministic string-literal recognition, project-specific policy rules, audit logs, diff previews, and a rollback API to satisfy compliance and traceability requirements. With an addressable tooling spend estimated at $8.0B (40M developers × $200 ARR), a market score of 92/100 and revenue potential at 80/100, timing is favorable due to rapid AI embedding in editors and increasing demand for auditable automation controls. This product can differentiate by combining low-latency client-side checks, AST-aware determinism, and enterprise-grade audit trails rather than relying on brittle ML heuristics that produce false positives. Real challenges are integration breadth (many editors and AI providers), tuning friction so guards don’t become annoying, and demonstrating clear ROI to teams that today tolerate occasional autocomplete glitches.