Many backend teams and DBAs routinely face a predictable but under-addressed source of latency: ORM-generated queries that explode into very large IN lists with many duplicate values, causing wasted CPU, inefficient index usage and increased cloud billing. This problem is ubiquitous — our TAM estimate is 1.5M development teams — and shows up as slow relational scans, lock contention, or ballooning serverless costs even when the application logic is correct. A practical product is a middleware layer that intercepts queries at the driver/ORM or proxy level, deduplicates IN-list values, and rewrites queries into semantically equivalent forms (e.g., bounded joins, indexed temporary sets, or parameterized blocks) with provable preservation of results and predictable latency overhead (target <1ms per rewrite). Delivering this as an open-source core plus a commercial control plane that integrates with existing observability (traces/metrics) would let teams see per-query savings and adopt safe “opt-in” rewrites, driving an average ACV in the target ~$6,000 range for teams that care about DB performance. This market is attractive now because ORMs are expanding the surface area of machine-generated SQL, cloud/serverless billing makes inefficient queries financially visible, and teams expect actionable fixes surfaced by consolidated observability — supporting the $9.0B market thesis. To stand out you must prioritize correctness across SQL dialects, minimal runtime overhead, and fast time-to-value via instrumentation and rule templates; that is a defensible product stance, but be honest that proving semantic safety across transactions, handling diverse ORMs, and competing with incumbents (APM and DB vendors) are real challenges.