Server-side analytics for AI & bot traffic
Agent Monitor captures and classifies AI & bot traffic using server-side data. Across 94M+ visits on 249 sites, 65% of traffic was bots - 24% AI bots like ChatGPT, Gemini, and Claude. None of this appears in GA4. We use transparent server-side signals to classify every visit. Get bot profiles, per-bot rankings, AI assistant traffic, and global benchmarks. Built by an SEO agency that needed real data.
Hey PH, I'm Marcin, co-founder of Top Online - an SEO agency behind Agent Monitor. I also co-authored the most comprehensive SEO handbook in Poland, so I've been deep in search and analytics for a while. Agent Monitor started because our analytics stopped making sense. Clients' traffic was dropping in GA4, but rankings were fine and conversions held up. When we dug into server logs, we found the missing piece - AI bots were all over our clients sites, and no tool was showing it. We looked for so
A very, very good tool that I’ve been using since the very first MVP version launched. Thanks to it, I can predict and adjust my strategy around how to work with and position my website in LLMs. I can see exactly which subpages are being picked up, what kind of traffic they generate, in which results they appear, and which articles are showing up. The latest update with the addition of specific URL tracking was absolutely brilliant. Now I can track every single page and every content block I pub
We have had so many issues with bot issues, amazing to have a tool that can actually give me the exact number!
Congrats on the launch! The gap between GA4 data and what’s actually happening in server logs is something many teams probably don’t even realize exists. How do you distinguish between legitimate AI crawlers (for indexing or training) and more aggressive scraping or data harvesting bots, and how granular is the reporting when it comes to identifying patterns over time?
For a team already using Cloudflare/WAF rules and GA4, what’s the concrete workflow you recommend: where does Agent Monitor sit in the stack, and what decisions does it enable week-to-week (e.g., allowlist/rate-limit/block, content changes, infra planning)?
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.