Netlify AI bot tracking

Netlify teams can combine server-side logs, user-agent categories, and Web Analytics to understand AI bot traffic without relying only on client-side analytics.

[01]

Workflow setup

Prerequisites

A Netlify site with the right plan and permissions for logs, log drains, or analytics.

A user-agent taxonomy or bot list for the AI services you want to monitor.

A Trakkr workspace tracking crawler behavior, AI traffic, or visibility outcomes.

Setup steps

  1. 1

    Collect server-side request data

    Use Netlify log drains where available to capture requests that may never run browser analytics, including crawler and bot requests.

  2. 2

    Apply user-agent categories

    Use Netlify user-agent category data to distinguish AI agents and crawlers from browsers, previews, and non-AI automation.

  3. 3

    Pair logs with Web Analytics

    Use Web Analytics for top pages, referrers, and source behavior, while treating server logs as the better source for crawler discovery.

  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 bot tracking into a visibility report 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

[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.