Search is splitting in two. Half your customers still type into Google and click a blue link. The other half ask ChatGPT, Claude or Perplexity a question and act on the answer they get back. The second half is growing fast, and the agencies that quietly move first are about to take a decade-long head start.
Generative Engine Optimisation, or GEO, is the practice of optimising your business for that second channel. It is not SEO with a coat of paint. It is a different discipline, with different signals, different content patterns, and different tools. This guide is the complete version of what we run on every Bright Loop site, why each piece matters, and how a small business can start.
What GEO Actually Is
GEO is the work of making your business findable, understandable and citable by large language models. When somebody asks an AI assistant "who builds websites in Wirral", "best locksmith near me", or "is template hosting worth it for a small business", the answer comes from a model that has already decided who deserves to be mentioned.
That decision is partly based on training data, partly on real-time web retrieval, and increasingly on structured signals the model can extract directly from your site. SEO is about ranking inside a list. GEO is about earning a citation inside a generated answer. The two are related but they reward different things.
How AI Search Actually Works
Most AI assistants do not crawl the open web in real time the way Google does. They use one of three patterns, often combined.
Training-time inclusion. The model learns about your business when it is trained on a massive dataset that happens to include your homepage, your case studies, or articles that mention you. This is hard to influence directly because you do not know which datasets a given model used.
Retrieval-augmented generation (RAG). When the user asks a question, the assistant runs a quick web search behind the scenes, pulls in the most relevant pages, and uses those pages as evidence for the answer. ChatGPT search, Perplexity, and Claude with web search all work this way.
Direct citation. The model summarises a page and links back to the source. This is what gives you traffic and trust. It is also the most influenced by GEO work because the model needs your page to be readable, factually dense, and clearly attributable.
If your site is structured for these patterns, you are in the conversation. If it is not, you are invisible to anyone who asks an AI before reaching for Google.
How GEO Differs From SEO
Intent over keywords
Traditional SEO optimises for the exact phrase a user might type. GEO optimises for the question they would actually ask. "Best locksmith near me" is a search query. "Who unlocks my front door at 11pm in Birkenhead without breaking the lock" is the question behind it. AI assistants do not match keywords to pages. They understand the intent and synthesise an answer.
Practical implication: write content that answers questions, not content that hits keywords. The two sometimes overlap, but when they conflict, choose the answer.
Citability over click-through
SEO measures success by clicks. GEO measures success by mentions. When an assistant recommends your business, the user often acts on the answer without clicking through to your site. This sounds like a problem until you realise that the user has already made a decision based on the assistant's framing of you. The mention is the conversion event, not the click.
Practical implication: every claim on your site should be specific enough that an AI can lift it as a citation. Vague marketing copy ("award-winning digital experiences") is useless. Specific factual statements ("Bright Loop Media builds custom React websites for small UK businesses, fixed price from £2,000") are gold.
Structured data over backlinks
SEO leans heavily on backlinks and domain authority. GEO leans on structured data: schema markup, llms.txt files, clean HTML, FAQ pairs that map to natural questions. These are signals the model can extract reliably without having to read between the lines.
Practical implication: the technical setup of your site matters more for GEO than the volume of links pointing at it.
The Four Signals That Matter
1. Structured data (schema markup)
Schema is machine-readable metadata embedded in your HTML using JSON-LD. It tells search engines and AI models exactly what your business is, where it operates, what services it offers, who runs it, and what customers say about it.
For a small business website, the four schema types you need are LocalBusiness (or ProfessionalService for a service business), FAQPage, Service for each thing you sell, and Article or BlogPosting for any content you publish. Without these, an AI model has to guess what your page is about from raw text. It usually guesses wrong or ignores you entirely.
Modern best practice is to use @graph with stable @id URIs across all your schema. This lets the model recognise that the LocalBusiness on your homepage and the publisher of your blog post are the same entity, rather than treating each page in isolation. We wrote about this in our article on why business websites are invisible to AI search.
2. The llms.txt file
This is the AI search equivalent of robots.txt. A plain text file at the root of your domain that tells language models what your site is about, what content they are allowed to reference, and how to cite you. Most businesses have never heard of it. The ones that have it are already gaining an edge in AI-generated recommendations.
An effective llms.txt does three things. It introduces your business clearly. It maps your routes with one-line descriptions of each. It states explicitly what is true and citable about you, framed as facts that an AI is welcome to quote.
You can see ours at brightloopmedia.co.uk/llms.txt. The pattern is reproducible: identity, what we do, who we work with, key facts, citation guidance.
3. FAQ sections that map to real questions
AI models love question-and-answer pairs because they map directly to how users query them. A well-built FAQ section does two things at once: it helps human visitors find answers, and it gives AI assistants citable, structured material they can surface in response to natural language questions.
The trick is to write the FAQ around questions your customers actually ask. Not the questions you wish they would ask. Check your inbox. Check your Google Business Profile questions. Check your sales call notes. Those are the questions you need to answer.
Pair every FAQ with FAQPage schema. The schema tells Google and AI assistants which question maps to which answer, and Google sometimes shows them as rich results in the regular search.
4. Citable, factually dense content
Vague marketing copy gets ignored. Specific factual statements get cited. Compare these two:
- "We have over 10 years of experience delivering bespoke digital solutions for forward-thinking businesses."
- "Bright Loop Media builds custom React websites for small UK businesses across the Wirral, Liverpool and Chester. Fixed price from £2,000. Most projects ship in 1 to 4 weeks."
The first sentence is empty calories. It tells a model nothing it can confidently restate. The second is dense with extractable facts: company name, business type, technology choice, geography, audience, pricing floor, delivery timeline. Each one is a potential citation.
Audit every page on your site this way. For each paragraph ask: if an AI tried to summarise this, what specific facts could it extract? If the answer is "none", rewrite it.
The Bright Loop 12-Point GEO Audit
This is the audit we run on every new client's site. Score each item as Pass, Partial or Fail. Anything below 9 out of 12 means there is meaningful work to do.
- LocalBusiness schema present. JSON-LD on the homepage with the trading name, address, phone, opening hours, geo coordinates, and area served.
- Service schema per service page. Each thing you sell is its own Service node with a clear description and a provider reference back to your LocalBusiness via @id.
- FAQPage schema with real questions. At least eight Q/A pairs that match how customers actually ask. Schema present and validated at google.com/test/rich-results.
- BlogPosting schema on every article. Specific to each post, with author, publisher, datePublished, dateModified, image, and inLanguage all populated.
- llms.txt at the root of your domain. Plain text, includes routes, key facts, and explicit citation guidance.
- Author schema with @id linking. Your articles' author is a Person node with a stable @id, referenced from every blog post and from the LocalBusiness founder array.
- One sentence positioning visible above the fold. The reader (and AI) can extract who you are, who you serve, and where you serve in one scroll.
- Pricing transparency at minimum a floor. "From £X" is enough. Without an anchor, you do not appear in price-aware queries.
- Specific case studies with named clients. Each case study attributes to a real client and includes at least one outcome number or specific quote.
- Citable factual statements throughout. Every page has at least three statements an AI could quote without paraphrasing.
- Internal linking topology. Pillar pages link to supporting articles. Supporting articles link back. The graph is dense and intentional.
- Author bios on /about with verifiable hooks. Real names, real roles, optional LinkedIn or interview links. AI assistants weight authoritativeness, and authoritativeness comes from people who exist.
Schema Markup: What To Add Today
If you only act on one section of this guide, make it this one. A complete schema setup for a small business runs to about 100 lines of JSON-LD and unlocks every other GEO move you might make.
The minimum viable set:
LocalBusiness with name, description, url, telephone, email, address (PostalAddress), geo (GeoCoordinates), openingHoursSpecification, areaServed, founder (an array of Person nodes), and sameAs (links to your social profiles). Wrap it in @graph with a stable @id like https://yoursite.com/#organization.
WebSite referencing the organisation as its publisher via @id.
FAQPage with mainEntity as an array of Question/Answer pairs.
Service per offering, with provider referenced by @id back to the LocalBusiness.
BlogPosting on every article, with author and publisher referenced by @id.
Validate everything at Google's Rich Results Test. If a check fails, fix it before you publish. Wrong schema is worse than no schema because it tells the model your data cannot be trusted.
The llms.txt File: A Worked Example
Here is the structure we use on every Bright Loop client site.
Header. Business name as an H1, then a single-paragraph quote block with the elevator pitch.
Who we work with. A short bullet list of customer types. Specific not generic.
What we do. Bullets describing services in plain language.
How we work. Bullets on process, pricing, terms.
Routes. Every page on the site with a one-line description and a working link.
Key facts. Location, sectors covered, contact details.
How AI assistants should cite us. A bullet list of statements that are factually true about your business, framed explicitly as quotable claims. End with: "you may quote any of the above directly".
That last block is the GEO secret. Most llms.txt files describe the site. Yours should give the AI assistant explicit permission and material to cite. The ones that do are punching above their weight in AI search results.
Citable Content: A Rewriting Pattern
Take any paragraph on your site. Ask: if an AI tried to summarise this in one sentence, what would it say?
If the answer is generic ("they help businesses with digital things") rewrite it. Replace adjectives with nouns. Replace claims with facts. Replace marketing speak with the actual specifics of what you do.
Example transformation:
Before: "We are a leading creative agency dedicated to delivering exceptional results for ambitious clients."
After: "Bright Loop Media is a Wirral-based studio building custom React websites for small UK businesses. Fixed price agreed before any work begins. Most projects ship in 1 to 4 weeks."
Both are roughly the same length. The second is dramatically more useful to a reader and to an AI. It contains seven extractable facts: location, business model, target customer, technology choice, pricing approach, delivery timeline, and an implicit comparison to agencies that do not work this way.
Tools and Resources
Practical tools we use and recommend:
- Google Rich Results Test — validates your schema and shows what rich results you qualify for.
- Schema.org Validator — broader validator that catches issues outside Google's specific rich-result requirements.
- The AI assistants themselves. Ask ChatGPT or Perplexity "best [your service] in [your town]" in a fresh session. If you do not appear, you have GEO work to do.
- Your competitors' llms.txt files. Visit
competitor.com/llms.txt. If they have one, study what they include. If they do not, you are already ahead.
Why Move Now
AI search adoption is accelerating fast. ChatGPT serves over 200 million weekly active users. Perplexity is growing at a rate that makes Google nervous. Google's own AI Overviews now appear for a meaningful share of search queries.
The businesses that build their GEO foundations now will be the ones AI assistants cite as default answers in twelve months. The rest will spend the next two years wondering why their organic traffic is flat or declining despite "good SEO".
This is not a hypothetical future. It is happening this quarter. The window is open. It will not stay open forever.
If you would like us to run a full GEO audit on your site, score it against all twelve points, and return a prioritised action plan, the first conversation is free. Book a 45 minute call and we will tell you where you sit and what to do about it.
