GitHub built an internal pipeline using GitHub Actions, GitHub Copilot, and GitHub Models to convert accessibility feedback into tracked, prioritized, actionable issues. Before this system, reports scattered across backlogs, bugs lingered without owners, and users got silence. The problem was structural: accessibility issues cross navigation, authentication, settings, and shared components simultaneously, meaning no single team owned them and existing triage processes were never designed to handle them.
The architecture is event-driven. Issue creation triggers Copilot analysis via the GitHub Models API. Status changes hand off between teams. Resolution triggers a follow-up to the original submitter. Ninety percent of accessibility feedback enters through the GitHub public discussion board, where other users add context before a team member even opens a tracking issue. The system was built by hand starting mid-2024. GitHub now says the same architecture could be assembled in a fraction of that time using Agentic Workflows and natural language.
The full article is worth reading for the specifics: the seven-stage flow from intake through improvement, how WCAG mapping and severity scoring get generated automatically, and how prompt refinement feeds back into Copilot analysis over time. The methodology, called Continuous AI, is positioned as a replicable model for any team doing accessibility work at scale. The architecture diagram alone repays the click.
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