Traditional search engines return a list of links; an AI assistant returns an answer. That shift — from "here are the results" to "here is the answer" — is why SEO alone no longer covers everything a company needs to do to be found.

From SEO to AEO and GEO

SEO (Search Engine Optimization) optimizes to rank well in a results list. AEO (Answer Engine Optimization) optimizes to be the answer an AI assistant gives directly, without the user clicking anything. GEO (Generative Engine Optimization) goes one step further: it optimizes for how generative systems, which synthesize information from multiple sources, cite, summarize, and recommend a company.

They are not concepts that replace SEO; they are layers added on top. A site still needs to follow the good technical practices described in Google's official documentation for developers. The difference is that there is now an additional audience reading the site: not only people browsing, but agents and language models processing content to answer third-party questions.

The basic building blocks

Before anything advanced, some fundamentals still apply — only now they serve two audiences at once:

  • Clear, well-structured content, with direct answers to the questions the company solves, not only generic marketing copy.
  • Structured data (JSON-LD) describing organization, services, and content so machines can interpret without ambiguity.
  • An llms.txt file (and, when it makes sense, an llms-full.txt version) summarizing the site in a format meant to be read by language models, following the convention consolidating for that purpose.

Explicit signals matter

Much of optimization for AI is, in practice, explicitly declaring what is already true. A correct robots.txt remains the base, but can now also include a signal such as Content-Signal, proposed by contentsignals.org, declaring whether content may be used for search, as input to AI systems, or for model training. That removes ambiguity: instead of a crawler having to guess the site's intent, the intent is declared.

The advanced layer: exposing an MCP server

All of the above helps an agent read and understand the site. But reading static content has a limit: it does not answer questions about data that changes, such as an updated service catalog, availability, or correct contact information at query time.

That is where Model Context Protocol (MCP) comes in, the open standard to connect language models to tools and data sources. Instead of an agent having to scrape HTML and hope the information is up to date, it can call a tool and receive real data, straight from the source.

A real implementation example

yanmantovani.com exposes its own MCP server at https://www.yanmantovani.com/api/mcp, with tools such as get_agency_info, list_services, list_products, list_case_studies, and get_contact — an agent can query those tools and get structured, current answers, instead of interpreting loose text. Around that, the site also publishes a server card at /.well-known/mcp/server-card.json, an API catalog at /.well-known/api-catalog following RFC 9727, and responds with Markdown content when the request asks for Accept: text/markdown — all designed so an agent can discover and use those sources without manual intervention.

Summary

AEO and GEO are not a marketing badge: they are the natural extension of good SEO technical practices to a scenario where part of relevant traffic is no longer a person clicking, but an agent processing and deciding on someone's behalf. Companies that declare their signals clearly and, when it makes sense, expose real data via MCP, get ahead in this new discovery format.