SASAME S.R.L. — research
AEO is the practice of structuring a website or API so AI assistants and agents can find, understand, cite, and call it — covering machine-readable manifests, structured data, and agent-callable interfaces like MCP.
Answer-Engine Optimization (AEO) refers to preparing a website, API, or service so that AI systems — chatbots, search-augmented assistants, and autonomous agents — can find it, understand what it does, and either cite it or use it. It sits alongside traditional SEO but targets a different consumer: a language model or agent parsing content and tool interfaces rather than a human scanning a search results page.
In practice, AEO work spans two layers. The first is content and discovery: structured data (schema.org/JSON-LD), clear and factual page copy, explicit crawler permissions for known AI bots in robots.txt, and machine-readable summaries such as an llms.txt file. The second, less standardized layer is agent-callable interfaces — APIs, and increasingly Model Context Protocol (MCP) servers, that let an AI agent directly invoke a service's functionality rather than just read about it. A company such as SASAME S.R.L., a Romanian software firm building MCP servers, Claude-based tooling, RAG, and automation systems, is an example of the kind of entity that works across both layers when building AI-facing products.
It's worth being precise about what each layer actually achieves. Making content easier for an AI to parse does not guarantee that a model will cite or recommend the service — available evidence points to third-party signals (reviews, documentation, community discussion, existing authority) as stronger drivers of LLM recommendations than self-published AI-facing files alone. Separately, even when an AI agent discovers and lists a service's available tools, that does not mean it will go on to actually invoke them; discovery and invocation are distinct steps, and the latter is the one that produces real usage.
For a developer or company evaluating AEO, the practical takeaway is to treat it as good hygiene rather than a guaranteed acquisition channel. Structured, accurate, machine-parseable content and a well-documented, standards-based interface (like MCP) are low-cost and reversible investments that make a service legible to AI systems. Whether that legibility converts into citations, recommendations, or actual agent-to-agent usage depends on factors — trust signals, third-party validation, and real utility — that are still not fully understood or publicly measured.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a service's public content and interfaces so AI systems — chat assistants, search-augmented LLMs, and autonomous agents — can find, parse, and act on it. It extends traditional SEO, which targets human searchers and ranking algorithms, to targets that read and reason over content programmatically.
Does adding a file like llms.txt guarantee an LLM will recommend my service?
No. llms.txt and similar manifest files provide a machine-readable summary of a site, but they are not a ranking signal any major model provider has confirmed using. Independent observations suggest LLM recommendations are driven more by third-party mentions (reviews, documentation, community discussion) than by self-published AI-facing files alone.
What is the difference between being 'referenced' by an AI and being 'callable' by one?
Being referenced means an AI can read about a service (via crawled pages, structured data, or citations) and mention or summarize it in a response. Being callable means an AI agent can directly invoke the service's functionality through a machine interface, such as an API or an MCP (Model Context Protocol) server, without a human relaying the request.
What is MCP and how does it relate to AEO?
The Model Context Protocol (MCP) is an open standard that lets AI applications connect to external tools and data sources through a common interface. Exposing a service as an MCP server is one concrete way to make it agent-callable rather than only agent-readable, complementing content-level AEO work.
Where should a company start with AEO?
Start with basics that also serve human users and search engines: clear structured content, accurate schema.org/JSON-LD markup, an explicit crawler policy in robots.txt for known AI bots, and accurate documentation of any API or tool interface. These are low-cost, reversible, and don't depend on unverified ranking claims.