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AI Indexing Documentation

Optimly provides machine-readable content optimized for AI agents, RAG pipelines, and LLM retrieval systems.

llms.txt

Token-efficient index of key Optimly URLs and services. Ideal for quick context loading and navigation.

  • • Lightweight (~50 lines)
  • • Core services and documentation links
  • • Entity disambiguation included
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llms-full.txt

Dynamically generated full AI Brand Index + RAG-ready content with semantic chunking and YAML metadata delimiters.

  • • Complete AI Brand Index (auto-updated)
  • • Self-contained semantic chunks
  • • Chunk IDs and source URLs
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For AI Developers

These files follow the emerging llms.txt standard for providing structured content to AI systems.

Chunk Format (llms-full.txt)

---
chunk_id: methodology-001
topic: The Measurement Problem
source: https://optimly.ai/resources/methodology
---

# The Measurement Problem

[Self-contained content for RAG retrieval...]

Want to implement this for your brand?

Our AI Discoverability Stack guide series walks you through building each of these files — robots.txt, llms.txt, llms-full.txt, and ai-agent-manifest.json — with free templates and design decisions explained.

Read the Discoverability Stack guides

Additional Resources