Case study
LLMs File traffic baseline (May 28, 2026)
This page documents real operational numbers from our own server logs. We publish the baseline and decision process to keep recommendations concrete.
Snapshot (log date: May 28, 2026)
- Clean daily unique IP: 60
- Total clean requests: 204
- Status mix: 200=186, 304=6, 404=11, 405=1
- AI referrals: 0 (early stage)
- Observed crawler signals: GPTBot and ClaudeBot active; OAI-SearchBot low-volume; Claude-SearchBot and PerplexityBot absent in the last 72h
What pages got real visits first
Top landing behavior was practical and troubleshooting-heavy: Cloudflare 52x pages, AI crawler log checks, and core root files (/robots.txt, /sitemap.xml, /llms.txt).
This confirmed that operational queries can pull early traffic faster than broad educational pages when the site is still young.
How we measured
# Daily traffic baseline report
sudo /usr/local/bin/llmsfile-traffic-report /var/lib/caddy/logs/llmsfile-access.log /home/ubuntu/llmsfile-reports
# Opportunity view (top pages by unique IP)
sudo /usr/local/bin/llmsfile-opportunity-report /var/lib/caddy/logs/llmsfile-access.log /home/ubuntu/llmsfile-reports
# AI referral isolation
sudo /usr/local/bin/llmsfile-ai-referral-report /var/lib/caddy/logs/llmsfile-access.log /home/ubuntu/llmsfile-reportsActions taken after the baseline
- Expanded Cloudflare troubleshooting coverage from 521-526 to 520-526 and added cross-links.
- Added feed and sitemap automation for faster update discovery.
- Improved log-quality filtering to remove synthetic monitor noise from growth decisions.
- Added bilingual intent pages where search logs showed repeated troubleshooting demand.
What this does not prove
These numbers do not prove ranking success yet. They only establish a baseline and identify which page intents produce measurable first-stage demand.
We still need sustained indexing growth, richer evidence pages, and stable AI referral gains before claiming strong GEO performance.