Playbook

llms.txt for AI discovery for AR/VR platforms

A practical playbook for spatial computing marketers to improve llms.txt—with checks, fixes, and measurement.

Why llms.txt matters in AR/VR

spatial computing marketers cannot win AI shortlists on content alone if llms.txt is broken. llms.txt is a root-level orientation file that summarizes your product, key URLs, and citation preferences for AI systems—without replacing crawlable pages.

In AR/VR, common blockers include: Buyers ask AI assistants before visiting vendor sites; Category pages are thin or JS-only; Competitors appear in ChatGPT answers for “AR/VR platforms” first. Marketplace listing pages can outrank your homepage in AI answers if your product facts live only behind auth.

What to check

  1. /llms.txt present at the site root with an accurate product summary
  2. Optional /llms-full.txt for longer documentation
  3. Links to pricing, docs, and canonical product pages
  4. Consistency between llms.txt claims and live page content

AR/VR-specific page priorities

  • Methodology — ensure this URL is crawlable HTML with facts assistants can quote when answering “best AR/VR platforms for teams evaluating options”
  • FAQ — ensure this URL is crawlable HTML with facts assistants can quote when answering “best AR/VR platforms for teams evaluating options”
  • Buyer guide — ensure this URL is crawlable HTML with facts assistants can quote when answering “best AR/VR platforms for teams evaluating options”

Fix guidance

Ship a truthful llms.txt, keep it updated after launches, and still maintain crawlable HTML for every URL you list.

Deep dive: llms.txt for AI discovery. Industry hub: AI visibility for AR/VR platforms.

Measure with BatSignal

  1. Run a Visibility Scan on your AR/VR site
  2. Inspect the pillar tied to llms.txt
  3. Ship the prioritized fixes and copy-paste deliverables
  4. Re-verify within 30 days to confirm movement

Related

FAQ

What is llms.txt for AR/VR platforms?

llms.txt is a root-level orientation file that summarizes your product, key URLs, and citation preferences for AI systems—without replacing crawlable pages. For AR/VR, this shows up when buyers ask “best AR/VR platforms for teams evaluating options” and when AI crawlers attempt to fetch your commercial pages.

How do we improve llms.txt?

Ship a truthful llms.txt, keep it updated after launches, and still maintain crawlable HTML for every URL you list. Industry-specific must-have pages include Methodology, FAQ, Buyer guide.

How does BatSignal score this?

Content readiness / discovery signals. See the [methodology](/methodology) and related guide: /guides/llms-txt.