Software teams and documentation owners—particularly API and SDK product teams at startups and enterprises—routinely face stale or incorrect code examples that diverge from production and slow developer onboarding, increasing support load and developer friction. This is a widespread operational cost: with an estimated 1.5M engineering teams globally, manual upkeep of examples is time-consuming and error-prone. You could build an AI-driven pipeline that continuously extracts runnable snippets from source code and tests, validates them in sandboxed CI, synthesizes human-friendly examples and SDK snippets, and auto-generates PRs to update the docs repo for maintainer review; the workflow would run per-PR or on a nightly cadence to minimize noise. A commercial model targeting $5K ACV per team maps to a $7.5B addressable market and aligns with an assessed revenue potential of 80/100 if you can capture meaningful enterprise adoption. The market is attractive now because LLMs and code models have materially improved at extracting, summarizing and transforming code, organizations have accepted PR-based automation as a CI control plane, and API-first adoption increases the need for accurate, consumable examples—hence the market score of 92/100. To differentiate, focus on engineering rigor: combine model extraction with execution-and-test validation to hit high precision (e.g., >95%), provide enterprise policy hooks and secure multi-language sandboxes, and optimize for low-noise PR workflows—but be candid that maintaining execution environments, handling language and SDK edge cases, and preventing noisy or insecure updates are the primary technical and go-to-market challenges.