GEO / AI-SEO

What the GEO service includes

We prepare the client's site and content so that AI assistants (ChatGPT, Perplexity, Alice, Gemini) cite exactly them in their answers. The work includes an audit of current citation rates, reworking page structure for fact extraction by language models, implementing schema markup and building citable blocks with facts, figures and sources. We don't write marketing slogans for the client for the sake of volume — we rebuild the way information is presented to fit the logic of generative search. Separately, we set up technical signals of content accessibility for AI crawlers and check how assistants actually answer target queries before and after the edits. The result is measured not by ranking position but by the share of neural-network answers in which the client's brand is mentioned as a source, and here promotion in neural networks works as a separate channel rather than an add-on over ordinary SEO.

How the process technically works

Generative search works differently from classic search: the model doesn't return a list of links but assembles an answer from fragments it deemed authoritative and unambiguous. That's why we break content into self-contained meaning blocks, where each paragraph answers a specific question without referring to the rest of the page. On top of the text we layer schema markup that describes entities (the organization, product, service, author) in a machine-readable language, and this reduces ambiguity when the model extracts data. We then enrich the material with verifiable facts, statistics and links to primary sources, because it's precisely these elements that raise the likelihood of being cited. The final loop is empirical testing: we run target queries through the assistants themselves and see which wordings they pull in, then adjust the structure.

When and how the term appeared

The term Generative Engine Optimization (GEO) was introduced in a research paper posted to arXiv on 16 November 2023 under the number 2311.09735. It was prepared by a team led by Pranjal Aggarwal with the participation of Princeton University, the Georgia Institute of Technology, the Allen Institute for AI and the Indian Institute of Technology Delhi. The work was presented at the ACM SIGKDD conference (KDD 24) in Barcelona in August 2024, which established the field in the academic arena. The authors built the GEO-bench benchmark and tested optimization methods on roughly 10,000 queries, showing that adding statistics, quotes and links boosts content visibility in AI answers by up to 40 percent. The term AI-SEO took hold in the industry a little later as a practical synonym, once the mass use of ChatGPT and Perplexity made generative results a noticeable traffic source.

Why programmatic precision is critical

Citability in neural networks breaks on small things: a single incorrect piece of schema markup, a duplicate entity or a contradiction in the data — and the model either ignores the page or pulls an erroneous fact from it. This is an engineering task, not a creative one: JSON-LD requires a valid structure, otherwise the parser simply discards the whole block, with no partial result. We treat markup as code — we validate the syntax, check the relationships between entities and test how a fragment is extracted by real assistants. Here it's important to underline the positioning: ASI Robotics writes the software part and tunes the output, but doesn't substitute the client's facts — if the client provides the product data, we're responsible for packaging it correctly for machines, not for its content. Without this precision, promotion in neural networks turns into chance, where the result can't be repeated or scaled.

What tools we work with

The base layer is structuring content for an LLM: we rewrite pages into a question-and-answer format, pull out definitions and facts into extractable blocks and remove meaning-level references that break fragment extraction. The second layer is schema markup using the Schema.org vocabulary in JSON-LD format, describing the organization, services, FAQ, authorship and reviews in a machine-readable language. The third layer is work on citability: adding verifiable statistics, links to primary sources, expert wording and mentions on third-party authoritative sites that models take into account when assembling an answer. Technically we use structured-data validators, tools for testing through the AI assistants themselves and logging of which sources the neural network pulls in for target queries. This stack is tuned to the client's infrastructure — whether that's their CMS, templates or existing content base.

When the key standards appeared

The Schema.org vocabulary that schema markup relies on was launched on 2 June 2011 jointly by Bing, Google and Yahoo, and in November 2011 Yandex joined the initiative — this made the markup a cross-search standard. The JSON-LD format, in which it's recommended to implement structured data today, became a W3C Recommendation in January 2014 and then became Google's preferred format thanks to its ease of implementation and maintenance. In other words, the technical base for the machine description of entities formed a decade before generative assistants appeared. When the term GEO took shape in 2023, these standards were already mature and simply found a new use — feeding the answers of language models, not just the snippets of classic search. This explains the low competition: the tools have been known for a long time, but the market only recently began applying them deliberately for generative search.

Why you can trust this to us

The combined experience of the ASI Robotics development team exceeds 45 years in IT, and we run GEO as an engineering discipline, not as a set of copywriting tricks. We validate every piece of markup, check fragment extraction and test the assistants' answers on target queries before the production launch, rather than handing over edits blindly. Our profile is writing programs and configuring the client's infrastructure, so promotion in neural networks fits the same approach as software development: reproducibility, verifiability, version control. We honestly separate areas of responsibility — where the result depends on the client's facts or platforms, we say so directly rather than promising guaranteed citability as if by magic. The channel is new and competition is low, but the winner here isn't whoever started first but whoever builds the data structure carefully and checks it before rollout.