Modern websites are no longer static HTML pages.
They rely on JavaScript, client-side rendering, cookie banners, and dynamic layouts. As a result, traditional crawlers and HTML parsers often fail to analyze what users actually see.
We recently built an AI Webpage Analysis API that works on fully rendered web pages, not raw HTML.
Instead of parsing markup, the API:
- Renders the page in a real browser
- Waits for JavaScript execution
- Handles cookie banners
- Captures a full-page screenshot
- Runs AI analysis on the rendered output
This enables things like:
- AI-generated page summaries
- Topic extraction
- Readability scoring
- Detection of blank or blocked pages
Real example (request & response)
In this article, I share a real production example, including the exact API request and response when analyzing a live website:
👉 https://snapshot-site.com/posts/ai-webpage-analysis-api
You can see how a single API call returns:
- A full-page screenshot
- A semantic summary of the page
- Extracted topics
- Quality and readability signals
Why this approach matters
Most AI tools analyze raw HTML.
That works poorly on modern websites.
By analyzing what users actually see, AI insights become:
- More accurate
- More reliable
- Easier to automate at scale
This approach has been useful for:
- SEO & content audits
- Page quality validation
- Monitoring dynamic websites
- Competitive analysis
I’m curious to hear how others handle webpage analysis on JS-heavy sites.
Are you relying on HTML parsing, browser rendering, or something else?
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