Convert “it's broken” client feedback into real GitHub issues via one AI-powered link

Many product, support and QA teams still receive unstructured client feedback—phrases like “it’s broken,” screenshots or vague emails—that routinely require minutes-to-hours of developer triage and costly context switching. That friction increases mean-time-to-repair, frustrates customers, and wastes engineering cycles on clarifying questions instead of fixes. A practical product is a single AI-powered feedback link embedded in client-facing apps or support replies that converts freeform text, screenshots and stack traces into structured, repo-aware GitHub issues. The backend would combine LLMs, deterministic parsers and OCR, call GitHub APIs to pre-fill titles, labels, severity, reproduction steps and suggested assignees, and offer a lightweight human-in-the-loop review before creating the issue. An MVP can be implemented as a GitHub App plus hosted inference and per-org onboarding, with a go-to-market model consistent with the $1,000 ARR-per-org assumption that produces an $8.0B TAM (8M teams). This opportunity is timely: LLM-driven automation materially reduces manual triage, API-first git hosts make deep repo integrations practical, and engineering efficiency is a priority—our internal assessment scores market attractiveness 74/100 and revenue potential 94/100. To win you must deliver highly accurate, repo-aware extraction, enterprise-grade security and auditability to earn trust, and be honest about limits—access/permission hurdles, potential model hallucination and change-management costs are real challenges that will determine adoption.