What is AI search optimization, and how is it different from the SEO I already do?

Traditional SEO earns you a position on a results page. AI search optimization, sometimes called generative engine optimization, earns you a place inside the answer itself when someone asks ChatGPT, Gemini, Perplexity or Google AI Overviews a buying question. The work is genuinely different: assistants care less about keywords and more about whether your business resolves as a clear entity, whether your pages are machine-readable by construction, and whether your claims are specific enough to cite. Existing SEO is a foundation, not a substitute; a site can do fine in classic Google results and still be invisible in AI answers.

Can you guarantee my business gets mentioned in ChatGPT or AI Overviews?

No, and nobody honest can, because the assistants decide what they cite and their behaviour keeps changing. What we control is everything that makes citation more likely: server-rendered pages with semantic structure, a consistent entity across directories and professional profiles, hand-written JSON-LD schema, an llms.txt file with a deliberate AI crawler policy, and content rebuilt so a direct answer sits near the top with dated, attributable claims. Then we measure instead of guessing: recurring prompt runs track your answer share, citation counts and sentiment over time, so you see movement, or the honest lack of it, in real numbers.

Do I need a whole new website, or can this work on my current site?

Both engagements exist, and the scoping call decides which fits. The GEO layer is applied to a site that already ranks respectably but never appears in AI answers: we repair structured data, entity consistency, crawler policy and content citability on what you have, then establish monitoring. The full build pairs with our web design work and integrates retrieval into the information architecture from the wireframe stage onward, which is the right call when the current site is due for replacement anyway. Because we build retrieval systems ourselves, including RAG pipelines, embeddings and rerankers, we optimise for how those systems actually read pages.

AI Search Optimization — get cited when the answer replaces the link

Get found and cited inside ChatGPT, Gemini, Perplexity and Google AI Overviews. Entity work, structured data, llms.txt and AI answer monitoring.

Get a fixed-price quote Send a message and we'll call you back

We reply within 4 business hours
4.8 from 16 Google reviews
Six countries

At a glance

Delivered by: Senior people, no junior hand-offs
Where: Brampton, Canada and Patiala, India
Every build: Fast on a phone, works with screen readers
You own: The code, the domain and every account

AI SEARCH OPTIMIZATION

Entity and knowledge graph work · schema.org depth · llms.txt · Citation worthy content · AI answer share monitoring · We build the retrieval systems we optimise for

Search used to end in a click. Increasingly it ends in a paragraph: ChatGPT answers, Gemini summarises, Perplexity cites three sources, and Google writes an AI Overview above the results. Your rankings can hold steady while your sessions fall, because the answer got assembled somewhere you have no analytics on. Nothing broke. The interface moved.

AI Search Optimization, often called Generative Engine Optimization, is the work of becoming one of the sources that answer is assembled from, and of shaping what gets said about you when it is.

What AI search optimization means in our hands

We build the retrieval systems we optimise for. Redenn ships RAG pipelines, embeddings, rerankers and evaluation harnesses for clients, and runs its own AI chatbot in production. We know how a passage actually gets selected: it has to be retrievable, self contained, unambiguous about who is making the claim, and corroborated elsewhere. That is an engineering view of the problem. See /services/llm-development for the other side of the same coin.

Entity first, keywords second. A model does not rank you, it resolves you. Before anything cites you it has to know the business exists, what category it sits in, who runs it, where it operates and what it sells. We treat your entity as the deliverable: a linked schema graph on your own domain, consistent naming and description across every profile and mention, and clean signals for the knowledge panel.

Machine readable by construction. AI crawlers are less forgiving than Googlebot. Content that only appears after JavaScript runs, or hides behind an accordion, often is not there at all as far as retrieval is concerned. We serve clean, server rendered HTML with real semantic structure, and we verify it rather than assume it.

What a typical engagement includes

An AI visibility baseline: a prompt set built from the questions your buyers genuinely ask, run across the major assistants and AI Overviews, logging who gets cited, what gets said about you, and where competitors are named instead
An entity and knowledge graph audit covering name, address, founding, leadership, services and description across every source a model can reach
Structured data built or repaired: Organization, Service, FAQPage, Article, Person and Product, connected with @id and sameAs so the graph resolves as one entity
An llms.txt file and a deliberate AI crawler policy: what you invite assistants to read, what you keep out, and a machine readable map of the pages that matter
Content rebuilt to be citable: a direct answer near the top, defined terms, dated and attributable claims, named authors, first hand experience instead of paraphrased consensus
A source consistency pass across directories, professional profiles, review platforms and press, so corroboration works in your favour
Monitoring: recurring prompt runs tracking answer share, citation counts and sentiment inside answers, reported on a cadence you choose

The stack and the approach

Delivery: Next.js 16, server rendered, Lighthouse 95+ and WCAG 2.1 AA as standard. Retrievability, speed and accessibility are the same problem wearing different hats.
Markup: hand written JSON-LD graphs rather than plugin output, validated and version controlled with the rest of the site.
Measurement: an internal harness that runs your prompt set against model APIs on a schedule, so change over time is evidence rather than anecdote.
Foundations: classic search discipline underneath, because assistants retrieve from the indexed web. This sits on top of SEO, never instead of it.

What this looks like in practice

Two shapes these engagements usually take.

A GEO layer on an existing site. A business that still ranks respectably but has stopped appearing inside AI answers. We baseline the prompt set, repair the entity and schema graph, publish llms.txt, rewrite the highest value pages to be quotable, tidy the off site sources, then stand up monitoring and hand it over.

Build and GEO together. A rebuild where information architecture, schema graph and content model are designed for retrieval from the first wireframe rather than retrofitted later. This pairs with /services/web-design, and ongoing monitoring can fold into a Care Plan.

Pricing

Scoped per project. The cost depends on how many locations you carry, how far your markup and off site sources have drifted, and how much content needs rebuilding. You get a fixed price after a scoping call. Contact us for a quote, or send a URL through the free audit first.

Ready to be the source, not the omission

Tell us the questions your buyers ask before they buy. We will run them through the assistants and show you exactly who gets cited today. Every brief gets a reply within four business hours.

Start an AI Search Optimization project → — or book a 30-min call

In every project, every tier

What you get regardless of what you buy

One fixed price

The scope and the number are agreed before work starts. No surprise add-ons.

Loads in under a second and a half

Speed is something we build to, not something we measure at the end and apologise for.

Built for phones first

Designed on a phone-sized screen and then opened out, because that is where your customers are.

Found on Google from day one

The things search engines need are in place before launch, not bolted on six months later.

Works with screen readers

Keyboard navigation and assistive technology are tested on every page, not just the home page.

Support after launch

We are still here after the invoice clears. Care plans start at $49/mo if you want us on call.

How it runs

From your first message to launch

Brief
You tell us the goal. We reply within 4 business hours.
Scope
A 30-minute call, then a fixed-price proposal within two business days.
Build
You watch it happen in the client portal, not in a status email.
Launch
We ship, measure, and hand over every account and password.

Proof

Recent work

Browse all 22 →

Before you decide

Questions people ask about ai search optimization — get cited when the answer replaces the link

Traditional SEO earns you a position on a results page. AI search optimization, sometimes called generative engine optimization, earns you a place inside the answer itself when someone asks ChatGPT, Gemini, Perplexity or Google AI Overviews a buying question. The work is genuinely different: assistants care less about keywords and more about whether your business resolves as a clear entity, whether your pages are machine-readable by construction, and whether your claims are specific enough to cite. Existing SEO is a foundation, not a substitute; a site can do fine in classic Google results and still be invisible in AI answers.
No, and nobody honest can, because the assistants decide what they cite and their behaviour keeps changing. What we control is everything that makes citation more likely: server-rendered pages with semantic structure, a consistent entity across directories and professional profiles, hand-written JSON-LD schema, an llms.txt file with a deliberate AI crawler policy, and content rebuilt so a direct answer sits near the top with dated, attributable claims. Then we measure instead of guessing: recurring prompt runs track your answer share, citation counts and sentiment over time, so you see movement, or the honest lack of it, in real numbers.
Both engagements exist, and the scoping call decides which fits. The GEO layer is applied to a site that already ranks respectably but never appears in AI answers: we repair structured data, entity consistency, crawler policy and content citability on what you have, then establish monitoring. The full build pairs with our web design work and integrates retrieval into the information architecture from the wireframe stage onward, which is the right call when the current site is due for replacement anyway. Because we build retrieval systems ourselves, including RAG pipelines, embeddings and rerankers, we optimise for how those systems actually read pages.

The general before-you-call questions live on the FAQ page.

Ready to talk about your project?

One sentence about the goal is enough to start. We will tell you honestly whether this is the right service for it.

Get a fixed-price quote See pricing

We reply within 4 business hours.