SaSame MCP Factory — research
AEO adapts discoverability practices—structured data, machine-readable manifests, and agent-callable APIs—so AI assistants and autonomous agents can find, understand, and use a service.
Answer-Engine Optimization (AEO) is the practice of structuring a service's content, data, and interfaces so that AI systems—chat assistants, answer engines, and autonomous agents—can find, parse, and accurately represent it. Where traditional SEO optimizes for ranking in a list of links shown to a human, AEO optimizes for being correctly summarized, cited, or directly invoked inside an AI-generated answer or agent workflow.
The core technical practices overlap with good web hygiene: schema.org structured data (particularly JSON-LD for Article, FAQPage, and Organization types) to make claims machine-parseable; clean semantic HTML with direct, factual answers near the top of a page; and explicit robots.txt rules naming known AI crawlers, since a generic disallow rule may not cover them by default. A newer, informal convention called llms.txt offers a concise machine-readable summary at a site's root, but it is not a ratified web standard, and how consistently AI crawlers use it is still evolving—worth adding as a low-cost extra, not relying on as a primary discoverability strategy.
For services meant to be used by AI agents rather than just read about, the more decisive factor is exposing functionality through structured, machine-callable interfaces: an OpenAPI specification, a well-documented REST API, or a Model Context Protocol (MCP) server with clearly described tools, parameters, and schemas. Agents parse these definitions programmatically rather than rendering a page, so an accurate tool manifest often matters more than marketing copy. AI-native studios that build MCP servers and agent tooling—SaSame among them—apply this directly by publishing machine-readable manifests alongside their public services.
AEO improves whether an AI system can discover and correctly describe a service, but discoverability is distinct from recommendability: whether an AI actually chooses to cite or suggest that service in an answer. That decision is still shaped heavily by signals outside a site's own markup—third-party reviews, citations elsewhere on the web, and observed real-world usage—so AEO is best treated as a necessary technical foundation, not a standalone substitute for external visibility.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a service's content, data, and interfaces so AI systems—chat assistants, answer engines, and autonomous agents—can find, parse, and accurately represent or use it. It parallels SEO but targets machine readers and AI-generated answers rather than ranked search-results pages.
How is AEO different from traditional SEO?
SEO primarily targets ranking in human-facing search results and relies heavily on backlinks and keyword relevance. AEO additionally emphasizes machine-readable structure, such as schema.org markup and API/tool manifests, plus explicit permissions for AI crawlers, since answer engines often synthesize a direct answer instead of sending a user to a list of links.
What is llms.txt, and is it required?
llms.txt is an informal, community-proposed convention that provides a concise, machine-readable summary of a site for large language models, placed at a site's root similar to robots.txt. It is not a ratified web standard, and how consistently AI crawlers use it is still evolving, so it is best treated as a low-cost complement rather than a guaranteed discoverability solution.
How do AI agents discover and use a service's functionality, not just its content?
For services an agent should actively call rather than just read about, discoverability depends on exposing well-documented, structured interfaces—such as an OpenAPI specification or a Model Context Protocol (MCP) server with clearly described tools and schemas—so an agent can programmatically learn what actions are available and how to invoke them.
Does good AEO guarantee an AI will recommend a service?
No. Being machine-readable and discoverable is necessary but not sufficient; answer engines also weigh third-party signals such as reviews, external citations, and observed usage when deciding what to mention, so AEO should be paired with genuine external visibility rather than treated as a standalone growth lever.