Qursor vs Visiby: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Qursor and Visiby — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Qursor
Qursor
Chrome extension to visually point at UI elements and copy clean, structured, code-aware context for AI coding assistants.
Key features
- Visual Element Pointing: Hover or click to target an exact UI element on any webpage using an overlay inspector, removing ambiguity about which element is referenced.
- Structured Context Copy: Exports clean, code-aware context (selectors, element attributes, and surrounding DOM snippets) formatted for use with AI coding assistants and prompt inclusion.
- Selector and Metadata Extraction: Gathers useful element metadata such as CSS selectors, IDs, classes, text content, and basic accessibility attributes to help generate accurate code or tests.
- Clipboard & Prompt Integration: Copies the extracted context to the clipboard in developer-friendly formats so it can be pasted directly into AI prompts, editors, or issue trackers.
- Lightweight Chrome Extension: Works in the browser as an extension without needing to modify the target site, enabling fast inspection across sites and apps.
- Context Preservation: Captures surrounding DOM hierarchy and nearby elements to preserve relevant UI context for tasks like layout fixes or component identification.
- Code-Aware Formatting: Produces output tailored to coding workflows (clean snippets and structured descriptions) to improve the quality of AI-generated code and suggestions.
- Visual website inspection directly in Chrome
- Point-to-select exact UI elements on a page
- Copy clean, structured, code-aware context for use with coding assistants
- Generates element selectors and surrounding context suitable for prompts
- Lightweight browser-integrated workflow for prompt preparation
Best for
- Bug Reproduction with AI: Capture exact element context and paste into a prompt so an AI assistant can reproduce and suggest fixes for a UI bug with precise selectors and DOM context.
- Test Generation: Provide an AI model with element selectors and surrounding DOM to automatically generate end-to-end or component tests (e.g., Selenium, Playwright) that target the correct elements.
- Component Refactoring: Extract a component's DOM snippet and metadata to prompt an AI assistant to refactor the component or convert it into a framework-specific component.
- CSS Fixes and Styling Changes: Supply the exact element and its computed context to an AI assistant to propose or generate accurate CSS rules or style adjustments.
- Accessibility Improvements: Copy element attributes and labels to give an AI prompt the necessary context to recommend ARIA roles, labels, or accessibility fixes.
- Documentation and Code Reviews: Quickly capture UI snippets and context to include in PR descriptions, issue reports, or to ask an AI for a review of UI-related code changes.
- Provide precise UI context to AI coding assistants for generating or modifying front-end code
- Extract selectors and markup snippets for writing automated tests
- Debugging and reproduction of UI issues by sharing structured element context
- Accelerate prototyping by copying focused UI fragments into code-generation prompts
- Onboarding or documentation: capture exact UI element context for guides
Visiby
FNA Technology
AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.
Key features
- AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
- Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
- Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
- Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
- Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
- Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
- Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
- White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.
Best for
- AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
- Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
- Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
- Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
- Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
- Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.