The question we hear most often these days: "How do I get my company to appear when someone asks ChatGPT about my services?"
The honest answer is that there is no switch to flip overnight. But there are concrete actions that, executed consistently, significantly increase the probability that language models will cite you.
These are the five most effective.
1. Create content that answers questions directly and in a structured way
Language models learn to cite sources that give direct answers. Content optimised for GEO has specific characteristics that distinguish it from traditional SEO content:
Direct answer at the beginning. If the article is titled "What is X", the first sentence defines X. There are no paragraphs of context before getting to the point.
Concrete, verifiable data. LLMs prefer claims backed by evidence: percentages, dates, figures from research. "Most companies" is hard to cite. "67% of companies according to Study Y from 2025" is citable.
Logical structure with descriptive H2s. Models process content better when headings describe precisely what lies beneath them. Vague or creative titles make information extraction harder.
Explicit definitions. If you use a technical term, define it. Models that need to explain a concept look for sources that already explain it precisely.
2. Build your brand's semantic entity
For an LLM to cite you, it first needs to "know who you are". In AI architecture terms, this means your brand must be a recognisable entity with consistent, verifiable attributes.
How to build that entity:
Consistency of name, description and services across all digital touchpoints: website, social profiles, sector directories, media mentions. If on your website you are a "data analytics consultancy", on LinkedIn an "analytics consulting firm" and in a directory a "data agency", the model has three distinct entities, not one.
Schema.org on your website. Structured data (Organization, Service, FAQPage) gives AI crawlers explicit information about who you are and what you do. A RAG system processing your site with schema.org has far more context to cite you correctly than one reading plain text.
Wikipedia or Wikidata where applicable. Models give considerable weight to encyclopaedic sources. If your company, product or sector has a Wikipedia entry, ensure your brand is mentioned or referenced correctly.
3. Gain mentions in the sources LLMs prioritise
Generative AI engines do not only cite your website. They cite the ecosystem of sources that discuss you and the topic at hand.
Sources with the greatest weight for LLMs:
Sector media. An article in a reference publication in your industry carries far more weight than a hundred mentions in low-authority blogs.
Specialist directories. For B2B consultancies, directories such as Clutch, G2 or sector-specific equivalents are legitimacy signals that models value.
Original-data publications. LLMs prioritise content with data that no one else has: surveys, project analyses, sector benchmarks. A study with real data has far greater citation potential than an opinion piece.
Authority professional platforms. Long-form posts on LinkedIn with expertise content are indexed by some AI engines and contribute to the brand's semantic entity.
4. Optimise service pages to answer real questions
Service pages are a frequently underused GEO opportunity.
Most describe the service from the seller's perspective: "We offer advanced solutions that optimise decision-making." That language is not citable because it does not answer any real question a user would ask.
The format that works for GEO:
- Include a FAQ section with the questions real prospective clients ask
- Answer each question directly, in two to four lines
- Mark those FAQs with FAQPage schema so that crawlers process them correctly
- Use descriptive, factual language — not sales copy
When someone asks Perplexity "what does a predictive analytics consultancy include?", the engine looks for sources that answer that question explicitly. If your service page answers it well, you have options.
5. Monitor how you appear and adjust systematically
The difference between SEO and GEO in measurement terms is significant. In SEO you can see your exact position for each keyword at any time. In GEO, the response varies by user, conversation context, moment and the model being consulted.
A practical monitoring protocol:
- Define 15–20 questions your prospective clients would ask about your service category
- Ask those questions in ChatGPT, Perplexity and Google AI once a month
- Record whether your brand appears, where in the response, and which sources the model cites
- When you do not appear but should, identify which source does appear and analyse why: more domain authority? More structured content? More media mentions?
This tracking, even though it cannot be fully automated, is more informative than any traffic metric for understanding your real position in AI Search.
The most common mistake when starting with GEO
Believing that publishing more content is enough.
Volume is not the determining factor. Citability is.
An article of 600 words that answers a specific question precisely, with concrete data and clear structure, will outperform ten 2,000-word pieces written for traditional content marketing.
The metric that matters in GEO is not how much you publish or how much traffic you receive. It is how many times models cite you when someone asks something relevant to your business. That is the metric worth building.