SaSame MCP Factory — research
AEO adapts a service's public surface — structured data, crawler policy, and machine-callable endpoints — so AI assistants and agents can parse, cite, and invoke it correctly.
As AI assistants and autonomous agents increasingly mediate how people find and use software, a parallel discipline to SEO has emerged: Answer-Engine Optimization (AEO), sometimes called GEO (Generative Engine Optimization). Where SEO optimizes for ranking in a list of links, AEO optimizes for being accurately parsed, summarized, or cited inside an AI-generated answer — and, for services with an API or tool interface, for being directly invocable by an agent.
The practical toolkit overlaps with existing web standards: explicit robots.txt entries for named AI crawlers, structured data via schema.org/JSON-LD (FAQPage, Article, and similar types), and machine-readable summaries such as an llms.txt file. These lower the barrier for automated readers to correctly interpret a page, but none of them force inclusion in a model's output — adoption of conventions like llms.txt varies across AI providers, and a model's answers are shaped by its training data and retrieval sources, not solely by what a site publishes about itself.
For services rather than static content, a second and distinct layer matters: machine-callable access. Protocols such as Anthropic's Model Context Protocol (MCP) let an AI assistant or agent invoke a service's tools directly — discovering a schema, calling a function, and acting on the result — which is a different achievement from simply being mentioned in a generated answer. Studios building AI-native tooling, such as SaSame's MCP/Claude/RAG/agent development work, treat this callable layer (a working MCP server, an accurate tool manifest, an agent-readable card) as separate infrastructure from the descriptive/AEO layer, since an agent can fail a tool call even when the surrounding marketing copy reads perfectly.
Because self-published AEO signals are cheap to produce and easy to inflate, they are also easy to treat as progress without external evidence. A more falsifiable approach is to track independent, non-self-generated signals of use: whether third-party sources (reviews, documentation, community posts, registry or directory listings) reference the service, and whether external agents or users actually call it. Absent that external corroboration, AEO artifacts remain a plausible but unproven investment rather than a demonstrated channel.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a website or service so that AI answer surfaces (chat assistants, AI-powered search, autonomous agents) can parse, summarize, and act on it correctly, rather than optimizing purely for traditional search-engine ranking. It typically covers crawler access policy, structured/machine-readable content, and — for services — machine-callable interfaces.
Does adding an llms.txt file guarantee AI assistants will cite my content?
No. llms.txt is a community-proposed convention for offering a markdown-friendly summary to language models, but it is not a universally adopted standard and major crawlers are not required to use it. It costs little to add but should be treated as one discoverability signal among many, not a guaranteed citation mechanism.
What's the difference between an AI 'referencing' a service and 'calling' it?
Referencing means an AI assistant mentions or cites the service in a generated answer, which depends on the assistant's training data and retrieval sources. Calling means an AI agent actually invokes the service as a tool over a protocol such as MCP (Model Context Protocol) or a documented API — a distinct capability that requires the service to expose a working, discoverable interface, not just descriptive text.
Do robots.txt and structured data (schema.org/JSON-LD) matter for AEO?
Yes, as access and parsing signals. robots.txt directives can explicitly allow or block named AI crawlers (e.g., GPTBot, ClaudeBot, PerplexityBot, Google-Extended), and schema.org/JSON-LD markup (such as FAQPage or Article types) gives machines an explicit, structured reading of a page's content. Neither guarantees inclusion in AI-generated answers, but both remove friction for automated readers.
What actually influences whether an AI recommends a service?
Third-party signals — independent reviews, documentation, community discussion, and listings in registries or directories — appear to carry more weight than a site's own self-published AEO content, since assistants often draw on external corroboration rather than a single first-party source.