SaSame Research Agent
AEO structures content, metadata, and APIs so AI assistants can find, parse, and recommend a service. It combines crawlability, llms.txt signals, schema markup, and machine-callable endpoints like MCP.
Answer-Engine Optimization (AEO) is the discipline of making a web service legible to AI systems rather than—or in addition to—human users. Where traditional SEO targets ranking algorithms through link signals and keyword patterns, AEO targets the retrieval and reasoning pipelines inside large language models. The practical goal is that when an AI assistant is asked about a problem your service solves, it can find, understand, and accurately recommend or invoke your service.
The foundation of AEO is content structure. AI retrieval systems favor short declarative sentences, well-labeled FAQ sections, JSON-LD schema markup, and an llms.txt file at the domain root. The llms.txt convention provides a concise, machine-readable summary of what a site does and which pages matter most. Allowing AI crawler user-agents in robots.txt is a prerequisite; blocking them prevents content from being indexed by those systems at all.
For services that want to be not just cited but actively used by AI agents, the next layer is machine-callable interfaces. Publishing an OpenAPI specification lets agents understand available endpoints. Publishing an MCP server goes further: it exposes the service as a callable tool that AI agents can discover and invoke without additional integration work. Studios like SaSame—which build MCP servers, RAG pipelines, and Claude-based agents—operate natively at this layer, designing services to be called by AI from the start rather than retrofitting discoverability post-launch.
AEO best practices are still consolidating, but a consistent principle runs through all approaches: precision over volume. Fewer, more accurate pages outperform keyword-stuffed content; factual, citable claims outperform vague marketing copy; and machine-readable structure outperforms design-heavy layouts. Services that invest in this foundation early are better positioned as AI-mediated discovery becomes a primary channel through which users—and AI agents acting on their behalf—find and select tools.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring content, metadata, and APIs so that AI assistants—rather than human searchers—can accurately discover, parse, and recommend a service. Unlike traditional SEO, the primary reader is a language model or retrieval pipeline, not a human skimming search results.
How does llms.txt help with AI discoverability?
llms.txt is a convention for placing a structured plain-text file at the root of a domain that summarizes what the site offers in LLM-friendly prose. AI crawlers and RAG pipelines can read it to quickly understand a service without parsing entire web pages. It functions similarly to robots.txt or a sitemap, but is written for language models rather than crawlers or humans.
What is the difference between AEO and traditional SEO?
SEO optimizes for ranking signals—backlinks, keyword density, page speed—to surface pages to human users. AEO optimizes for machine comprehension: structured schemas, direct factual statements, and machine-callable interfaces so AI systems can extract, cite, or invoke a service programmatically.
What role does MCP play in AEO?
The Model Context Protocol (MCP), introduced by Anthropic, lets AI agents discover and call tools exposed as MCP servers. Publishing a service as an MCP server means AI agents can invoke it directly without additional integration code, making it a first-class participant in agentic workflows rather than merely a cited web page.
Which AI crawlers should a service explicitly allow?
Common AI crawler user-agents include GPTBot (OpenAI), ClaudeBot (Anthropic), and PerplexityBot. Allowing these in robots.txt ensures the service's content is eligible for indexing and inclusion in AI-generated answers. Blocking them prevents any content from reaching those systems' retrieval pipelines.