Large, multi-file pull requests are a common bottleneck for software teams—especially engineering organizations of 10–1,000 engineers—because they increase reviewer cognitive load, prolong time-to-merge, and hide independent change sets that could be landed earlier. Developers, tech leads, and release managers experience more merge conflicts, slower feedback loops, and lower deploy frequency as a result. An AST-driven AI would parse diffs semantically, identify independent change units (API contract updates, pure refactors, formatting, feature toggles), and synthesize bite-sized change plans that include split PR proposals, suggested commit boundaries, targeted tests, CI steps, and regression-risk annotations. The product would integrate with GitHub/GitLab and CI systems to scaffold branches or suggested PRs, surface explainable rationales for each split, and provide a human-in-the-loop workflow so maintainers can accept, edit, or reject automated plans. Combining deterministic AST analysis for safe refactoring with LLMs for natural-language explanations and test synthesis balances precision and productivity. The market is attractive now: developer tooling is a $25B opportunity (26M developers × ~$960/year), AI-for-code models are maturing, and organizations are actively investing to optimize dev velocity and shift-left automation—market score 94/100, revenue potential 86/100, competition medium. This idea is worth pursuing if you target enterprise buyers where ROI is measurable, start with 2–3 high-value languages and deep platform integrations, and prioritize AST precision, explainability, and low-friction adoption; the main challenges are building robust multi-language parsers, handling integration complexity, and earning developer trust, but these are manageable and critical levers of differentiation.