# How CrawlFAQs Uses Vision AI to Understand Your App
Building documentation tools that actually understand web applications requires solving a fundamental challenge: how do you programmatically extract meaningful information from something designed for human eyes? Traditional approaches parse HTML, but that misses the forest for the trees - it sees the DOM structure but doesn't understand what users actually experience.
We took a different approach: what if our documentation tool could literally see your application the way your users do? That's where vision AI comes in.
Why Vision Models?
Traditional crawlers can extract text, identify links, and map navigation flows. But they fundamentally don't understand the visual context that makes user interfaces intuitive. They can't tell you that a prominent green button is the primary call-to-action, or that a navigation sidebar organizes features into logical groups, or that a dashboard displays key metrics in a specific visual hierarchy.
Vision models like Qwen 2.5 VL change this completely. They process screenshots the same way humans do - understanding spatial relationships, visual prominence, design patterns, and contextual meaning. When our crawler captures a screenshot of your dashboard, the vision AI doesn't just see pixels - it understands that this is a dashboard, identifies the key metrics, recognizes the chart components, and comprehends how users would navigate the interface.
The Technical Architecture
Our crawling pipeline combines Playwright browser automation with state-of-the-art vision AI. Here's how it works:
First, Playwright navigates your application just like a real user would. It handles single-page app routing, waits for dynamic content to load, executes JavaScript, and captures full-page screenshots. This isn't a simple DOM scraper - it's a full browser experiencing your application exactly as your users do.
Each captured screenshot is then analyzed by Qwen 2.5 VL, a vision-language model specifically optimized for understanding user interfaces. We send the model a carefully crafted prompt that asks it to identify UI elements, understand their purpose, extract visible text, recognize interaction patterns, and structure this information into a consistent schema.
The model returns structured JSON describing everything it observed: navigation elements, primary actions, form fields, content sections, visual hierarchy, and contextual relationships between elements. This structured data becomes the foundation for documentation generation.
Handling Complex Interactions
Modern web applications aren't static pages - they're interactive experiences with dynamic content, conditional rendering, and complex state management. Traditional documentation approaches struggle with this complexity, but vision AI handles it naturally.
When our crawler encounters a form, the vision AI doesn't just see form fields - it understands the flow of user input, recognizes validation patterns, identifies the submit action, and comprehends the expected user journey. When it sees a data table, it recognizes filtering controls, sorting interactions, and pagination patterns.
We've tuned our prompts and data extraction pipeline through thousands of real-world applications, teaching the system to recognize common UI patterns while remaining flexible enough to understand novel interfaces. The result is robust documentation that captures not just what your UI looks like, but how it works.
From Vision to Documentation
The structured facts extracted by our vision AI become the raw material for documentation generation. Another AI model takes these facts and transforms them into natural, helpful documentation: FAQs that answer real user questions, tutorials that guide users through complex workflows, and help articles that explain features in context.
This two-stage approach - vision for understanding, language models for generation - gives us the best of both worlds. The vision AI ensures accuracy and completeness by actually seeing your application, while the language model ensures readability and usefulness by crafting content optimized for your users.
The future of documentation is visual, automated, and intelligent. And it's available today.