AccessLoop

Inspiration

Digital accessibility is often treated as a final checklist item rather than something that is continuously considered during development. Accessibility audits can also be difficult to interpret: identifying a problem is useful, but developers still need to understand why it matters and how to fix it.

We built AccessLoop around a simple idea: accessibility should be part of the development loop, not an afterthought.

Our goal was to use AI to make accessibility feedback faster, easier to understand, and more actionable for developers and teams.

What it does

AccessLoop helps identify accessibility barriers in digital experiences and turns those findings into clear, actionable guidance.

Instead of simply reporting that something is wrong, AccessLoop is designed to help close the loop between detection and remediation. It explains accessibility issues, provides context around their impact, and guides developers toward practical fixes.

The result is a workflow where teams can:

Identify accessibility problems.
Understand why they create barriers.
Receive actionable recommendations for fixing them.
Iterate toward a more inclusive experience.

How we built it

We designed AccessLoop as an AI-assisted accessibility workflow rather than just another static auditing tool.

The application combines an accessibility analysis pipeline with an AI layer that interprets findings and transforms technical results into useful developer guidance. We focused on creating a simple interface around that process so users can move naturally from discovering an issue to understanding and addressing it.

A major part of the project was connecting the different pieces into a cohesive loop: collecting information about the experience being analyzed, identifying potential accessibility concerns, reasoning about those findings, and presenting useful remediation guidance back to the user.

During the hackathon, we prioritized building an end-to-end working prototype and making the experience useful rather than attempting to cover every accessibility scenario.

Challenges we faced

One of our biggest challenges was that accessibility is contextual. A technical rule violation does not always tell the whole story, while some real accessibility barriers cannot be captured by a simple automated check.

That meant we had to think carefully about where deterministic accessibility checks end and where AI reasoning can add value without pretending to replace human judgment.

We also had to balance the scope of the idea with the limited time available during a hackathon. Building the analysis, AI reasoning, user experience, and integrations into one working system required us to make deliberate trade-offs and focus on the most important user journey.

Another challenge was making the AI's feedback genuinely useful. A long explanation of an accessibility standard is far less valuable to a developer than a concise explanation of what is wrong, who it affects, and what they should change.

What we learned

Building AccessLoop reinforced that AI is most useful here not as a replacement for accessibility standards or human evaluation, but as a bridge between them and the people building software.

We learned how important it is to combine structured analysis with contextual reasoning, and how much the presentation of accessibility feedback matters. Developers are far more likely to act on an issue when the path from problem → impact → solution is clear.

Most importantly, the project changed the way we thought about accessibility itself. Making technology accessible should not be a one-time audit at the end of a project. It should be a continuous feedback loop throughout the development process.

That idea became AccessLoop.