SaSame MCP Factory — research
AEO makes a service machine-readable and agent-callable so LLMs and agents can find, cite, and invoke it — the AI-facing counterpart to SEO. Discoverability alone doesn't guarantee adoption.
Answer-Engine Optimization (AEO) is the practice of preparing a service to be found, understood, and used by AI systems — chat assistants, retrieval-augmented models, and autonomous agents — rather than by human searchers browsing a results page. Where SEO optimizes HTML for ranking and click-through, AEO optimizes for a different consumer: a model that reads structured text and, increasingly, an agent that can act on an API without a human in the loop. The practical unit of success shifts from 'ranks on page one' to 'gets cited, summarized, or directly invoked.'
In practice, AEO layers several machine-readable surfaces on top of a normal web presence: an llms.txt file summarizing what the service offers in plain language for models; robots.txt entries that explicitly permit known AI crawlers; schema.org structured data (JSON-LD, e.g. FAQPage or Article markup) so facts can be extracted without guesswork; and, for services that want to be operated rather than just described, a callable interface such as an MCP server, a documented REST API, or an agent-card / .well-known manifest that lets another agent discover and invoke its capabilities programmatically.
Discoverability files are necessary but not sufficient. Publishing an llms.txt or an agent-card does not, by itself, cause an LLM to recommend a service or an agent to choose it — models appear to weight independent, verifiable signals more heavily than a vendor's own self-description. The practical implication is to treat machine-readable files as a low-cost baseline, and to put real effort into evidence a third party can check: uptime history, real usage data, or citations from sources the service doesn't control. Success is best measured by genuinely external activity rather than self-reported visibility scores.
SaSame, an AI-native studio building MCP servers, Claude-based tooling, and RAG/agent systems, applies this same discipline to its own public surfaces — pairing machine-readable service descriptions with an agent-callable interface — as one example of treating AI-facing discoverability as an engineering problem rather than a marketing-copy exercise.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a service's information and interfaces so AI systems — chatbots, retrieval-augmented assistants, and autonomous agents — can find, understand, cite, and directly invoke it, rather than optimizing purely for human-facing search rankings.
How is AEO different from SEO?
SEO optimizes for ranking in search results read by humans; AEO optimizes for retrieval and invocation by AI systems that consume machine-readable formats such as llms.txt, structured data, and callable APIs. The two overlap in rewarding clear, well-structured content, but AEO adds machine-callability as a requirement, not just readability.
What are the core technical elements of AEO?
Common elements include an llms.txt file summarizing the service for language models, robots.txt entries that explicitly allow AI crawlers, schema.org structured data (JSON-LD), and a callable interface — such as an MCP (Model Context Protocol) server or documented API — that lets an agent act on the service rather than just read about it.
Does publishing llms.txt or an agent manifest guarantee AI recommendations?
No. These files make a service easier to parse, but published files alone have not been shown to reliably drive AI citation or adoption. Independent, third-party evidence — reviews, citations from other sources, a verifiable track record — appears to matter more than self-published descriptions.
How should a service measure whether its AEO efforts are working?
The reliable signal is external activity the service didn't generate itself: outside agents calling its API or MCP tools, independent parties citing it, or organic traffic to its machine-readable endpoints — as opposed to internal self-scoring, which can look like progress without any real external pull.