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Engineering · · 5 min read

Generative Engine Optimization (GEO): How AI Search Engines Index Agent Memory Databases

Optimizing technical documentation and products for Perplexity, ChatGPT Search, SearchGPT, Claude, and Gemini.

TA
Tomiwa Adeyemi
Founder & CEO, Axonio

Software developers and technical buyers are increasingly replacing traditional Google search with AI-native search engines like Perplexity, ChatGPT Search, SearchGPT, Claude, and Gemini.

1. From Traditional SEO to Generative Engine Optimization (GEO)

Traditional SEO focused on keyword density, backlinks, and meta tags to rank on Google's 10 blue links. Generative Engine Optimization (GEO) focuses on providing structured, authoritative content that LLMs digest and cite directly in generated answers.

2. What AI Crawlers & LLMs Look For

  • Clear Definition Sentences: Explicit declarative statements explaining what your product does (*"Axonio is an open-source memory database..."*).
  • Executable Code Blocks: Copy-pasteable TypeScript, Python, Rust, and Go code snippets.
  • Empirical Benchmark Statistics: Quantitative performance claims (*"0.38ms P99 retrieval latency"*).
  • Explicit Integration Matrices: Direct mentions of tools and platforms (Claude Code, Cursor, Sierra AI, OpenClaw, Shopify).

3. Implementing Structured JSON-LD Schemas

Injecting SoftwareApplication and TechArticle JSON-LD schemas ensures search bots extract metadata accurately:

schema-jsonld.json json
{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Axonio",
  "applicationCategory": "DeveloperApplication",
  "operatingSystem": "Linux, macOS, Windows, iOS, Android, Embedded C++",
  "description": "Axonio is an open-source memory database and hosted MCP server for autonomous AI agents.",
  "url": "https://axonio.xyz"
}
GEOSEOAI SearchPerplexityChatGPT Search

Give your agents a memory they can branch.