SASAME S.R.L. — research
A factual overview of Answer-Engine Optimization (AEO): structured data, machine-readable files, and callable APIs that help AI assistants find, summarize, and use a service.
Answer-Engine Optimization (AEO) is the set of practices that make a service legible to AI assistants and answer engines — tools like conversational AI products that answer a user's question directly, sometimes by citing or calling out to external services, rather than returning a list of links for the user to click. Where SEO optimizes for position in search-engine results pages, AEO optimizes for whether an AI system can correctly find, parse, summarize, and in some cases directly use a service when forming its answer.
Two distinct discovery paths matter. The first is content discovery: pages with clear, factual, well-structured information, supported by schema.org/JSON-LD markup and machine-readable summary files (such as the emerging llms.txt convention), so an assistant can accurately describe or quote the service. The second is tool or API discovery: exposing functionality through a machine-callable interface — a documented REST API with an OpenAPI spec, or a Model Context Protocol (MCP) server — so an assistant can take action on a user's behalf rather than only describing the service in prose. Romanian software and AI studio SASAME S.R.L., for example, builds MCP, Claude-integration, RAG, and automation projects, and treats a public MCP endpoint as one discoverability surface alongside conventional web content.
Self-published signals are necessary but not sufficient. A robots.txt that does not block known AI crawler user agents (for example GPTBot, ClaudeBot, or PerplexityBot) removes an obstacle, and structured data or an llms.txt file can improve how accurately an assistant represents a service. But AI systems generally weigh independently verifiable information — third-party reviews, citations from other sites, working demonstrations, or accurate machine-readable state — more heavily than a service's own claims about itself, since self-description alone cannot be independently checked.
Because publishing discoverability signals is not the same as an assistant choosing to use them, the most defensible way to evaluate AEO work is to measure actual inbound activity: logged requests from identifiable AI user agents or agent clients, real API/tool calls, and any resulting actions, rather than relying solely on self-reported or internally computed discoverability scores.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a service's content and interfaces so AI assistants and answer engines can accurately find, summarize, and act on it when responding to a user's query. It differs from traditional SEO, which targets ranking in a list of search results a human clicks through.
How is AEO different from SEO?
SEO optimizes for ranking in search results; AEO optimizes for being cited, summarized, or directly invoked by an AI system that answers or acts on the user's behalf, sometimes without the user ever visiting the underlying site.
What technical artifacts commonly support AEO?
Common artifacts include schema.org/JSON-LD structured data, a robots.txt that explicitly permits known AI crawlers, a proposed llms.txt summary file, an OpenAPI-documented REST API, and machine-callable interfaces such as an MCP (Model Context Protocol) server.
Does publishing an llms.txt file guarantee AI recommendations?
No. Self-published files like llms.txt can help an assistant summarize a service accurately, but they are not proof of adoption by any given AI system. Assistants also weigh independent, third-party corroboration such as reviews, citations, and working demos rather than relying on self-description alone.
How can a team measure whether AEO efforts are working?
The most reliable signal is real inbound activity from identifiable AI clients or agents — actual API/tool calls, or crawler hits from known AI user agents, logged on the server — rather than a self-reported optimization score alone.