Netlify AI Bot Tracking Workflow | Trakkr
Netlify AI bot tracking
Trakkr installs a Netlify Edge Function that records matching crawler requests before browser analytics would run. Upstream blocks remain outside its view.
Explore crawler monitoring Read crawler docs
Workflow setup
Prerequisites
- A Netlify site and permission to authorize the Trakkr OAuth integration and deploy the generated Edge Function.
- A clear list of production paths you want to verify after the function deploys.
- A Trakkr workspace tracking crawler behavior, AI traffic, or visibility outcomes.
Setup steps
Authorize the Netlify site
Connect the site through Trakkr OAuth and select the production site that should receive crawler monitoring.Deploy the generated Edge Function
Use the generated one-file function to classify matching request signatures and send bounded event fields to Trakkr.Verify the live delivery path
Confirm the source check, then record a real matching request through the deployed function. A synthetic row only proves the dashboard path.Segment important paths
Track docs, product pages, comparisons, pricing, and llms.txt separately so crawler volume does not hide missing high-intent pages.Send findings into Trakkr reports
Use Trakkr to compare bot access with visibility, AI traffic, citations, and action status for the same pages.
What to measure
Signals to track
| Metric | Why it matters | Cadence |
|---|---|---|
| AI-agent requests | Shows server-side demand from AI systems, not just browser-visible sessions. | Weekly |
| Top crawled paths | Reveals which Netlify routes AI agents actually request. | Weekly |
| Top pages and referrers | Adds traffic context around pages that receive both human visits and AI-related requests. | Monthly |
| Crawler signal versus share of voice | Separates access problems from content and authority problems. | Monthly |
How Trakkr fits
Product workflow
Trakkr turns Netlify Edge Function events into a page-level request, citation, and referral comparison that non-engineers can use.
Crawler and traffic signals can be tied to llms.txt publishing, content actions, and stakeholder summaries.
Useful Trakkr links
AI search traffic index AI crawler monitoring AI traffic data llms.txt generator Crawler behavior research
Checks and sources
Common mistakes
- Relying only on client-side analytics for bot and crawler questions.
- Letting aggregate bot volume hide the pages AI systems should crawl.
- Treating user-agent categories as perfect identity instead of a practical monitoring layer.
Official sources used
Netlify Edge Functions Netlify Web Analytics
FAQ
Quick answers
Can Netlify Web Analytics track AI bots?
Web Analytics is useful for human traffic context. Trakkr crawler monitoring uses the generated Edge Function for matching request evidence.
Why use server logs for AI crawler tracking?
Crawler requests may never execute browser analytics. An edge request function can record matching activity before client-side code runs.
How does Trakkr fit with Netlify?
Trakkr compares Netlify crawler request signals with separately observed prompts, citations, referral traffic, reports, and actions.
Next workflows
Related platform pages
Cloudflare AI crawler monitoring
Turn Cloudflare AI Crawl Control into an AI visibility workflow: see which AI services reach your site, which paths they request, and whether crawler access lines up with Trakkr visibility changes.
Open workflowVercel AI crawler logs
Use a Trakkr project Log Drain for request delivery. Treat Vercel Bot Management and its AI-bot ruleset as a separate control surface for log or deny policy.
Open workflowGA4 AI search attribution
GA4 now recognizes AI Assistant traffic for supported referrers. Use that signal carefully, then connect it to Trakkr prompts, citations, and reports.
Open workflow
Build a server-side AI bot baseline
Start with Netlify logs and categories, then use Trakkr to explain which crawler patterns matter for visibility.