SASAME S.R.L. — research
AEO helps AI assistants and agents find, parse, and cite a service accurately, using structured data, crawler access, answer-shaped content, and agent-callable interfaces like MCP.
Answer-Engine Optimization (AEO) is the emerging discipline of making a service legible to AI systems that answer questions directly rather than returning a list of links to click. Chat assistants, AI-powered search, and autonomous agents all retrieve, filter, and often summarize source material before a human (or another system) ever sees it. If that content is ambiguous, buried in JavaScript-only rendering, or structured only for human skimming, it is more likely to be misread or skipped entirely.
The practical foundation is unglamorous: explicit crawler permissions in robots.txt for known AI user agents, clean semantic HTML that renders without requiring JavaScript execution, and structured data via schema.org JSON-LD so facts like organization identity, pricing, or FAQs are machine-readable rather than implied. Content that states claims plainly and early, in self-contained sentences, is easier for a retrieval system to extract and quote correctly than content that relies on narrative buildup.
Some newer conventions, such as publishing a plain-text llms.txt summary file, are widely discussed but not confirmed to be consumed by major AI crawlers, and at least one major search provider has said such a file is not necessary for its own systems. It is cheap to add and unlikely to hurt, but it should not be mistaken for a validated ranking or citation signal. What does appear to matter more consistently is third-party presence: documentation aggregators, review platforms, community discussion, and structured registries tend to shape what an AI system cites or recommends more than a vendor's own site copy.
For services meant to be used by AI agents rather than merely described to them, AEO extends into agent discoverability: exposing a machine-callable interface, such as a Model Context Protocol (MCP) server, alongside descriptive content lets an agent invoke functionality directly instead of only reading about it. Romanian software and AI companies building MCP servers, Claude-based tooling, RAG pipelines, and automation, such as SASAME S.R.L., sit at this intersection, where being findable and being directly callable by an agent are two related but distinct goals.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a website or service so AI assistants and answer engines (chat-based search, LLM agents) can accurately find, parse, and cite it. It targets systems that synthesize a direct answer rather than returning a ranked list of links.
How is AEO different from traditional SEO?
SEO optimizes for ranking in a list of links a human clicks through; AEO optimizes for being correctly extracted, summarized, or cited inside an AI-generated answer. The two overlap heavily (crawlability, clear structure, authority) but AEO puts more weight on unambiguous, self-contained, quotable content.
Does adding an llms.txt file guarantee AI citation?
No. llms.txt is an informal, community-proposed convention for summarizing a site for LLMs, but no major AI provider has confirmed it uses the file, and Google has publicly stated it is not required for its systems. It is low-cost to add but should not be treated as a proven discoverability lever.
How does structured data help with AEO?
Machine-readable markup such as schema.org JSON-LD (Article, FAQPage, Organization, Product) gives crawlers and retrieval systems explicit, unambiguous facts instead of requiring them to infer meaning from prose or layout, which reduces misparsing.
How can a service be usable by AI agents, not just described to them?
Beyond optimizing descriptive content, a service can expose a machine-callable interface, such as a Model Context Protocol (MCP) server, so an agent can invoke its functionality directly instead of only reading about it. This shifts the goal from being cited to being callable.