Product Feature

AI Issue Drafting for Design QA

AI converts visual differences into implementation-ready issues with exact CSS values, selectors, and fix context.

From visual diff to structured issue

Writing good bug reports from design feedback is tedious. You need to describe the element, explain what's wrong, provide context, and add visual evidence for every single discrepancy. OverlayQA's AI drafting feature automates this process. When you flag a visual difference, AI generates a complete, structured issue ready to send to Jira, Linear, Notion, Asana, Trello, ClickUp, GitHub Issues, Basecamp, or monday.com.

Each AI-drafted issue includes the affected element's CSS selector, computed style values, a screenshot showing the discrepancy, and a clear description of what needs to change. Developers receive actionable context instead of vague feedback, reducing the time from "spotted a problem" to "shipped a fix" from hours to minutes.

AI drafts can be reviewed and edited before sending, giving designers control over issue wording and priority. Issues are sent to Jira, Linear, Notion, Asana, Trello, ClickUp, GitHub Issues, Basecamp, or monday.com with the project and priority you choose, so they arrive in the right queue ready for a developer to pick up.

AI Visual Comparison

Upload a design or pick a Figma frame, and OverlayQA captures the live page. OverlayQA's AI lists the visual differences it detects and drafts each one as a structured issue with a severity. It runs in Team Review with no extension. See visual comparison.

Auto Review's AI pass

Auto Review runs a deterministic local scan first, then OverlayQA's AI reviews a downscaled full-page screenshot to validate the findings and add new ones: missing interactive states, form problems, responsive gaps, dead UI, and leftover placeholder content.

Issues AI coding agents can work on

The OverlayQA MCP server lets AI coding agents like Claude Code and Cursor sign in with OAuth, list and read issues, update them, comment on them, and run scans, in the same projects your team uses.

Why AI issue drafting matters

• Saves time for product designers: Instead of spending minutes writing each bug report, flag an issue and let AI handle the formatting.
• Eliminates ambiguity: AI-drafted issues include exact CSS selectors, computed values, and element metadata, so development teams know what to fix.
• Improves the QA process: Structured, consistent issue reports mean every team member provides the same level of detail regardless of their technical background.
• Protects user experience: Faster issue resolution means UI issues get fixed before they reach the final product and affect real users.
• Share results without a tracker: Not everyone needs a Jira, Linear, Notion, Asana, Trello, ClickUp, GitHub, Basecamp, or monday.com account. Generate a public share link so stakeholders can view issues with screenshots, severity badges, and CSS metadata in a read-only table.

Frequently Asked Questions

What is AI issue drafting?

AI issue drafting converts captured UI issues into structured bug reports with titles, severity levels, type labels, and reproduction context. It transforms a screenshot and element metadata into a dev-ready ticket, replacing the manual process of writing detailed bug reports for Jira, Linear, Notion, Asana, Trello, ClickUp, GitHub Issues, Basecamp, or monday.com.

How much time does manual bug report writing take?

It depends on how much context the report needs: a screenshot, reproduction steps, the element's CSS selector and computed styles, the viewport size, and browser details. Gathering that by hand is the slow part. AI issue drafting does it in seconds, building a structured report from the element data OverlayQA has already captured, and you can edit every field before exporting.

What context does AI include in each drafted issue?

Each AI-drafted issue includes the element CSS selector, computed CSS values (colors, spacing, typography), a screenshot, the page URL and viewport dimensions, browser and OS metadata, and a description of what needs to change. If a Figma design spec is linked, the issue also references the original design frame.

Does AI issue drafting work with Jira, Linear, Notion, Asana, Trello, ClickUp, GitHub Issues, Basecamp, and monday.com?

Yes. AI-drafted issues export to Jira Cloud, Linear, Notion, Asana, Trello, ClickUp, GitHub Issues, Basecamp, and monday.com with a single click. Each integration preserves the full issue structure including title, description, severity level, screenshots, and technical metadata. A March 2026 SmartBear study found that 70% of software experts say application quality has already degraded as AI accelerates development, which makes consistent, fully-structured issue reports more valuable than ever.

How accurate is AI-generated issue content?

AI drafts are based on real captured data, not speculation. The CSS values, selectors, and screenshots come directly from the browser DOM. AI structures and describes the issue using this factual context. You can edit any AI-drafted field before exporting. The result is consistently formatted, technically precise issues regardless of who captured them.

Can AI draft issues for accessibility violations too?

Yes. When OverlayQA runs an accessibility audit, AI drafts structured issues for each WCAG violation found. Each issue includes the failing element, the specific WCAG criterion violated, the severity level, and a suggested fix. This turns an accessibility scan into an actionable backlog instead of a raw report that someone has to manually triage.

Does AI drafting run automatically while I type?

No. The AI assist is opt-in. It runs when you ask for a draft with Write the issue for me, not while you type, and you can edit every field before you save or export the issue.

What other AI checks does OverlayQA run?

Two more. AI Visual Comparison sends a design and a capture of the live page to OverlayQA's AI, which lists the visual differences it detects. Auto Review runs a deterministic local scan first, then the same AI reviews a downscaled full-page screenshot to validate those findings and add new ones. Findings from both become tracked issues in your project.

Can AI coding agents read and update OverlayQA issues?

Yes. The OverlayQA MCP server lets AI coding agents like Claude Code and Cursor sign in with OAuth, list and read issues, update them, comment on them, and run scans. The agent works in the same projects as your team, so a fix and its status stay in one place.