Emoji control inaccessible to screen readers — auto-detect + ARIA-fix tool

Many modern web and mobile interfaces use emoji and inline icons that are visually meaningful but frequently inaccessible to screen readers, either being read verbatim as Unicode characters or omitted entirely when developers treat them as decorative. This creates a recurring problem for accessibility engineers, QA and product teams at enterprises and mid-market companies — the target market of roughly 1.4M organizations with a $6K average accessibility tooling spend (totaling an $8.4B market) — because the bug is small per instance but pervasive across componentized UI libraries. You could build a developer-first tool that auto-detects emoji and icon usage via static analysis and runtime instrumentation, infers intended semantics with an AI-assisted model, and proposes or submits ARIA fixes as code patches or PRs tied to component definitions. Core features would include framework integrations for React/Vue/web components, a component-mapping layer to fix repeated instances at source, CI gating and audit logs for compliance evidence, plus a confidence score and human-in-the-loop workflow to prevent incorrect labeling. This market is attractive now because regulatory pressure (WCAG/ADA enforcement) and rising litigation are driving companies to buy automated accessibility tooling, while componentization concentrates repeatable bugs and AI-assisted development reduces the marginal cost of generating ARIA fixes. Against a medium competitive landscape, the product can differentiate by specializing on emoji/icon classes of bugs, optimizing a high-precision inference model trained on popular UI libraries, and delivering enterprise-grade PR automation and compliance artifacts; challenges remain in semantic ambiguity, cross-framework maintenance, and developer trust, so pursue this if you can demonstrate high precision in suggested fixes and smooth CI integration through pilot customers.