The technical side of ADA compliance that design alone won't fix

Clara Hayes — Tue, 30 Jun 2026 12:37:00 +0000

A lot of accessibility content stops at the design layer — contrast ratios, font sizing, layout. Aufait UX's ADA compliance guide is a useful reminder that a meaningful chunk of WCAG 2.1 AA conformance is implementation-level, not visual.

What needs to hold up in the actual build

Semantic HTML and ARIA roles — screen readers depend on correct structure, not just correct appearance. A visually perfect component built on generic soup will still fail accessibility testing.
Keyboard operability — every interactive element needs to be reachable and usable without a mouse, which often surfaces gaps in custom components (custom dropdowns, modals, date pickers) that look fine but trap focus or break tab order.
Accessible PDFs — frequently shipped as flat scanned documents with zero structure for assistive tech, a compliance gap that's easy to miss because PDFs don't get the same QA attention as the main site.
Video captions and audio descriptions — required for both Section 508 and ADA-aligned compliance, not optional polish, and easy to deprioritize under deadline pressure.

Why automated testing isn't enough

One stat from the piece worth internalizing: automated accessibility scanners typically catch only 30–40% of real barriers. The rest requires manual testing — actually navigating the product with a screen reader, actually tabbing through every interactive flow. Teams that rely solely on a Lighthouse score or an automated scanner are shipping with a false sense of compliance.

Where this fits into the dev workflow

For dev teams working alongside web ui design services, this is a good reminder that accessibility QA needs to happen at the code review level, not just the design review stage — a beautifully accessible Figma file can still ship a non-compliant build if implementation details (focus order, ARIA labeling, semantic structure) get missed during development. Building an accessibility checklist into PR review, the same way teams handle security or performance review, is the practical fix the guide points toward implicitly.

Also worth noting for teams offering ui design solutions to clients: litigation volume around ADA Title III continues to climb year over year, which makes accessibility increasingly a risk-management conversation for clients, not just a design preference.
Read Full guide Website ADA Compliance Guidelines

How an AI design tool stack actually fits into a real product workflow

Clara Hayes — Tue, 30 Jun 2026 10:06:10 +0000

Most "AI in design" content stays at the surface — screenshots of a chat prompt generating a UI. Aufait UX's writeup goes further into the parts that matter for anyone shipping product: how AI output integrates with the actual handoff to engineering.

A few technical details worth flagging for builders:

Output format matters more than the demo Figma Make outputs editable frames, not flattened screenshots — meaning design output stays inside the working file rather than needing to be redrawn from an image. It also generates component code and style references, which speeds up dev handoff considerably compared to tools that only produce static comps.

Model selection isn't a footnote The team uses Gemini 2.5 Pro for complex reasoning-heavy flows, Flash models for fast iteration cycles, and Claude Sonnet when they need a balance of structural logic and creative output quality. Worth remembering that "AI design tool" isn't one undifferentiated category — the underlying model choice changes what you get.

Mokkup.ai exports only as JPEG/PNG — no conversational refinement, no editable component output. Good for early wireframe-level thinking on interactive ui design for data-heavy dashboards specifically, not for anything downstream in the pipeline.

The part most relevant to engineering teams

The case study that stands out: a health-tech R&D platform needing a custom report-generation flow, where the team had already studied Salesforce and Power BI for reference but kept producing designs that either borrowed too heavily from existing patterns or didn't actually reduce interaction load. A prompt synthesizing their research plus design thinking got Figma Make to a structural thread they hadn't considered — which then became the basis for the actual build.

That's a meaningfully different use case than "AI generates a landing page," and it's the kind of example worth referencing when your team is evaluating whether a given AI tool earns its place in a real ux design services pipeline or just adds another revision loop nobody asked for.
The piece also covers a finding that matters for how teams scope AI use generally: a community platform project where field research overturned assumptions an AI personalization model would have shipped on by default — specifically around device ownership patterns in smaller Indian cities. No model flagged it. A researcher in the field did.

For dev and design teams collaborating around ui ux design services, this kind of tool-by-tool, output-format-aware breakdown is more useful than the usual "AI will change everything" framing — it actually tells you where in the pipeline each tool belongs and where it doesn't.
Full breakdown AI in UI/UX Design — Hype vs. Reality