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.

[01]

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

  1. 1

    Authorize the Netlify site

    Connect the site through Trakkr OAuth and select the production site that should receive crawler monitoring.

  2. 2

    Deploy the generated Edge Function

    Use the generated one-file function to classify matching request signatures and send bounded event fields to Trakkr.

  3. 3

    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.

  4. 4

    Segment important paths

    Track docs, product pages, comparisons, pricing, and llms.txt separately so crawler volume does not hide missing high-intent pages.

  5. 5

    Send findings into Trakkr reports

    Use Trakkr to compare bot access with visibility, AI traffic, citations, and action status for the same pages.

[02]

What to measure

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
[03]

How Trakkr fits

Turn the platform signal into action

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.

Share-of-voice and site-grader tools give teams a fast benchmark before they set up a deeper integration.

[04]

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.

[05]

FAQ

Web Analytics is useful for human traffic context. Trakkr crawler monitoring uses the generated Edge Function for matching request evidence.

[06]

Next workflows

Build a server-side AI bot baseline

Start with Netlify logs and categories, then use Trakkr to explain which crawler patterns matter for visibility.