AEO · GEO · AI Overviews

Rank #1 on Google.
Still invisible
to AI.

Search has split in two. Traditional rankings still matter — but an entirely separate layer of AI-generated answers, citations, and recommendations is now shaping buyer decisions before they ever reach your website. We make sure you appear in both.

What Changed

The search landscape shifted.
Most businesses didn't notice.

For 25 years, SEO meant one thing: rank on Google. That is still true — and still important. But a second, parallel search layer has emerged that operates on entirely different rules, responds to entirely different signals, and is used by an increasingly large proportion of buyers before they ever open a search engine.

Before
2022
Search meant Google
Rank #1 on Google, get the click. Optimise title tags, build links, write content. The funnel started when someone hit your site.
Now
2026+
Search means Google + AI
A buyer asks ChatGPT to recommend your category. Gets 3 names. Yours isn't one of them. Never reaches Google. Never sees your #1 ranking. The funnel starts before the search engine.
58%
of adults use AI chatbots for product and service research
39%
of B2B buyers begin vendor evaluation with an AI query
64%
of Google searches now trigger an AI Overview above organic results
Two Disciplines, One Strategy

AEO and GEO —
what they actually mean.

AEO
Answer Engine Optimisation
Optimising to appear in direct answers generated within search engines — Google's AI Overviews, featured snippets, People Also Ask boxes, and voice assistant responses. AEO targets the moment a user searches on Google but never scrolls past the AI-generated answer at the top.
Where it shows up
Google AI Overviews Featured snippets People Also Ask Voice search results Google Assistant
GEO
Generative Engine Optimisation
Optimising to be cited, recommended, or mentioned by standalone AI platforms — ChatGPT, Perplexity, Claude, Gemini, and others. GEO targets the moment a buyer bypasses Google entirely and asks an AI chatbot to recommend a product, vendor, or service in your category.
Where it shows up
ChatGPT Perplexity Google Gemini Microsoft Copilot Claude
The overlap: The signals that improve AEO and GEO are largely the same — structured data, authoritative third-party citations, E-E-A-T signals, and content that directly answers questions. A strategy built for one improves the other. We treat them as one integrated programme.
The Decision Mechanism

How AI systems decide
who to recommend.

AI systems don't rank pages. They build probabilistic models of which entities are most authoritative, most relevant, and most cited in the context of a given question. Understanding this model is the foundation of every strategy we build.

01
🗞️
Third-party citations and mentions
Impact: High
The single most influential signal. AI systems build their understanding of "who is authoritative in category X" primarily from what other authoritative sources say about you — review platforms (G2, Capterra, Trustpilot), industry publications, analyst reports, and editorial coverage. A business not mentioned in any of these is largely invisible to AI recommendation systems, regardless of how good its own website is.
02
🧱
Structured data and entity definition
Impact: High
AI crawlers read structured data to understand what an entity is, what it does, and how it relates to other entities. Schema markup — particularly Organization, Product, SoftwareApplication, LocalBusiness, and FAQ — gives AI systems a machine-readable definition of your business that reduces ambiguity and increases the probability of accurate citation.
03
✍️
Content that directly answers evaluation questions
Impact: High
"What does [product] do?", "Who is [brand]?", "How does [product] compare to [competitor]?" — these are the questions AI systems synthesise answers to. If your own content doesn't answer them clearly and factually, the AI system will synthesise from whatever third-party sources do — which may be competitors, reviewers, or outdated information.
04
🔗
Backlinks from authoritative sources in your category
Impact: Medium-High
Links remain a primary signal — not because AI systems read PageRank directly, but because the publications that rank highly (and therefore are trusted as training sources) are the ones that link out editorially. Being cited in an article that itself ranks well is a multiplier. Being listed in a respected industry directory is another form of the same signal.
05
👤
E-E-A-T signals — experience, expertise, authoritativeness, trust
Impact: Medium-High
AI systems are trained to weight content from people with demonstrated real-world expertise more heavily. Bylined content, linked author bios, qualifications, speaking credits, press mentions, and social proof all contribute to the author and entity trust signals that AI systems use to calibrate recommendation confidence.
06
📊
Review volume, recency, and sentiment
Impact: Medium
For product and service recommendations, review platform data is weighted heavily. Volume (total reviews), velocity (recent reviews), rating (aggregate sentiment), and response behaviour (does the company engage?) are all signals AI systems use to assess whether a business is actively operating, reputable, and worth recommending.
Google AI Overviews

The answer that appears
before your #1 ranking.

Google AI Overviews now appear for a majority of informational and commercial queries. They sit above the organic results, synthesise an answer from multiple sources, and attribute citations to those sources. Being cited in an AI Overview drives significant qualified traffic — and being absent hands those impressions to competitors.

Google AI Overview — example
Best project management tools for remote teams
Remote teams benefit most from tools that combine task management with async communication. Notion, Linear, and Your Brand ✓ are consistently recommended for their real-time collaboration features and integration depth...
Sources cited:
g2.com yoursite.com techradar.com capterra.com
What triggers an AI Overview
AI Overviews appear for queries Google classifies as having a clear informational or synthesisable answer — most "best X for Y", "how to", "what is", and comparison queries. They are less common for pure navigational queries (branded searches) and transactional queries (direct purchase intent).
How Google selects cited sources
Google cites sources it deems most authoritative and relevant to the query. Pages that rank well for the query, have strong E-E-A-T signals, and contain structured, directly-answerable content are most likely to be cited. Being cited in an AI Overview is distinct from ranking #1 — some cited sources rank on page 2 or lower.
Why citation is worth pursuing
AI Overview citations appear above all organic results. A citation link in an AI Overview receives prominent, above-the-fold placement. CTR from AI Overview citations is lower than from organic #1, but the brand impression is stronger — the user reads your brand name in the answer before deciding whether to click anything.
What we do to improve citation probability
We implement FAQ and HowTo schema, write directly-answerable content in the exact format AI Overviews extract from, build topical depth that signals subject-matter authority, and monitor which queries for your category trigger AI Overviews so we can target them specifically.
ChatGPT & Perplexity

The buyer who never
opens Google.

ChatGPT and Perplexity are used differently from Google — more conversational, more evaluative, more "help me decide" than "show me options." This buyer is often further along in their purchase intent. Being absent from their AI response is equivalent to not being in the consideration set at all.

How ChatGPT and Perplexity decide what to recommend
Training data weighting
ChatGPT's base recommendations come from its training data — what was written about your category across the web before the cutoff. Products and brands mentioned frequently in authoritative contexts are weighted higher. This is the hardest layer to influence quickly — it requires sustained, long-term presence in authoritative publications.
Real-time web search (Perplexity, ChatGPT with browsing)
Perplexity and ChatGPT with web browsing enabled retrieve live web results and synthesise from them. This means current rankings, current review content, and current editorial coverage all influence recommendations in real time. This is the highest-leverage layer — it responds to SEO and content work within weeks, not months.
Retrieval from specific sources
Both platforms weight certain source types heavily: review aggregators (G2, Capterra, Trustpilot), technology media (TechCrunch, The Verge, Wired), and established industry publications. If your product isn't listed, reviewed, or mentioned in these sources, it is structurally disadvantaged regardless of how good your own content is.
User query phrasing
Conversational queries ("I need a tool that does X for a team of Y") retrieve different results than navigational queries ("best X software"). We research the specific phrasing patterns used in your category and build content and external citations aligned to those exact query forms.
What we build to improve your AI recommendation presence
Review platform presence
G2, Capterra, TrustRadius, Trustpilot — whichever is most relevant to your category. We build the initial profile, implement a review generation strategy, and ensure the listing content is structured for AI extraction.
Editorial coverage in AI-indexed publications
We identify and pursue placement in the specific publications that ChatGPT and Perplexity weight most heavily for your category — not generic PR, but targeted coverage in the sources these systems are trained on.
Comparison and alternative content
"[Product] vs [Competitor]" and "[Category] alternatives" — the specific page types AI systems cite most frequently when answering evaluative queries. We build or optimise these for your product.
Entity disambiguation content
"What is [your brand]?" — a clean, structured answer on your own site and on external profiles that makes it unambiguous to AI systems what your product is, who it's for, and what category it belongs in.
Structured FAQ content
Direct-answer format content covering the questions AI systems are asked about your category. Each answer is structured as a standalone extractable unit — the format AI systems prefer to cite.
Structured Data & Schema

Telling AI systems exactly
what you are.

Structured data is the machine-readable layer that lets AI crawlers understand your content with precision rather than inference. It is the foundation of both AEO and GEO — and the single highest-leverage technical change most businesses have not yet fully implemented.

🏢
Organization
Defines your brand as a known entity — name, URL, logo, social profiles, founding date, description. This is the schema that feeds Google's knowledge panel and AI systems' understanding of who you are. Without it, AI systems must infer your brand identity from context rather than from a definitive source.
FAQPage
The schema most directly responsible for AI Overview citations. FAQ content with FAQPage markup is extracted as ready-to-cite answer units. Every page on your site with a Q&A section should have FAQPage schema — it is the single most direct route to AI Overview appearance.
📦
Product / SoftwareApplication
For product and SaaS companies, this schema defines your product's features, pricing model, category, and audience. It is how AI systems that answer "best X for Y" queries know that your product belongs in that category.
AggregateRating
Surfaces your review rating directly in search results and feeds AI systems' evaluation of your trustworthiness. Without this, AI systems must find your rating on third-party platforms — which may be incomplete or outdated.
👤
Person / ProfilePage
For founder-led businesses and individual experts, Person schema connects the human expertise behind the brand to the content they produce — feeding AI systems' E-E-A-T evaluation and making bylined content more attributable.
🗺️
LocalBusiness
For businesses serving local markets, LocalBusiness schema with service area, opening hours, and geo-coordinates is the primary schema feeding AI local recommendations and Google Maps AI summaries.
📋
HowTo
Step-by-step process content marked up with HowTo schema is prioritised by AI systems answering instructional queries. It signals that your content is a complete, authoritative guide rather than general commentary on a topic.
🔗
BreadcrumbList + WebPage
Site architecture signals that help AI crawlers understand your content hierarchy — which pages are most important, how topics relate, and how to navigate your content from a high-level topic to a specific answer.
E-E-A-T & Entity Signals

The credibility layer
AI systems trust most.

E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — is Google's framework for evaluating content quality. It is also, in effect, the framework AI systems use when deciding whose content to cite and whose brand to recommend. Building E-E-A-T is not a quick win — it is a durable, compounding asset.

E
Experience
First-hand experience with the subject matter — case studies, original data, real client results, demonstrated outcomes. AI systems weight content from people and businesses with documented real-world track records more heavily than content that is comprehensive but theoretical.
Signals we build
Named case studies Original research and data Client testimonials with specifics Before/after results documentation
E
Expertise
Demonstrated knowledge depth — not just covering a topic, but covering it at a level that signals genuine subject matter mastery. This is evaluated at the content level (depth, accuracy, nuance) and the author level (credentials, publications, professional history).
Signals we build
Author bio and credentials Bylined thought leadership Podcast and speaking credits Expert citations in content
A
Authoritativeness
Recognition by other authoritative sources in your field — editorial links, mentions in industry publications, quotes in news coverage, inclusion in "best of" roundups. This is the most externally-determined component and the one that requires the most sustained effort to build.
Signals we build
Editorial backlinks Industry publication features Review platform presence Directory listings in category
T
Trustworthiness
Signals of reliability, transparency, and safety — HTTPS, clear authorship, privacy policy, contact information, accurate business details, and a positive review profile. Trustworthiness is the foundation the other three components rest on. Without it, demonstrated expertise is insufficient.
Signals we build
Review generation strategy Transparency and About page depth Schema-verified business information Consistent NAP across platforms
Monitoring & Measurement

You can't improve what
you're not measuring.

AI visibility is harder to measure than traditional rankings. There is no "position 1" in an AI answer. But there are meaningful, trackable proxies — and we track all of them.

🔍
AI Overview tracking
We monitor which queries in your category trigger Google AI Overviews, track whether your site is being cited in those Overviews, and identify new Overview opportunities as Google expands the feature to new query types.
🤖
AI platform mention audits
Regular prompting of ChatGPT, Perplexity, Gemini, and Copilot with the queries your buyers actually use — to see whether and how your brand appears in responses, what your brand is described as, and which competitors are consistently recommended instead.
📢
Brand mention monitoring
Tracking where your brand name, product name, and key personnel are mentioned across the web — editorial publications, forums, review platforms, social media — as a leading indicator of growing (or declining) AI visibility.
Review platform velocity
Monitoring review volume, recency, and sentiment across G2, Capterra, Trustpilot, and relevant category platforms — the data layer AI systems use most heavily for product and service recommendations.
🔗
Citation source tracking
Monitoring which publications are citing your brand in contexts AI systems are likely to train on — and identifying gaps where competitor brands are being cited and you are not.
📊
Monthly AI visibility report
A consolidated report covering AI Overview citation status, AI platform mention audit results, brand mention volume trends, and recommended actions for the following month.
Is This Right for You?

AI visibility matters most
to these businesses.

🖥️
SaaS and B2B software
B2B buyers are the heaviest AI chatbot users. "What's the best tool for X?" is now a standard opening move in vendor evaluation. If your product doesn't appear in ChatGPT or Perplexity responses for your category, you are missing the earliest and most influential stage of the buying journey.
🛍️
E-commerce and consumer brands
AI-powered Google Shopping and generative product recommendations are growing. Structured product data, review presence, and comparison page content determine whether your products appear in AI-generated shopping guides and "what should I buy" responses.
🏢
Professional services
When a prospective client Googles a firm name or asks Perplexity "who are the best [accountants / lawyers / consultants] for X" — what they find shapes whether they make contact. Professional service firms live and die on reputation in search, and AI is now part of that search.
📍
Local and multi-location businesses
Google's AI-powered local summaries and voice assistant responses increasingly synthesise local recommendations without showing a full results page. Being the recommended business in your city for your service category requires AI-specific local signals beyond standard local SEO.
🚀
Growth-stage companies building brand
Companies investing in brand-building now will benefit from AI visibility compounding over 12–24 months as their entity signals strengthen. Starting early is a structural advantage — AI systems are conservative about adding new entities and generous about recommending established ones.
🎯
Businesses where Google rankings are already strong
If you already rank well on Google but are concerned about the AI layer eating into your traffic and visibility, this service directly addresses that risk — extending your existing authority into the AI answer layer rather than starting from scratch.
How We Work

From AI audit
to sustained visibility.

01
AI visibility audit
We audit your current AI search footprint — prompting major AI platforms with the queries your buyers use, mapping your Google AI Overview presence, reviewing your structured data implementation, assessing your E-E-A-T signals, and benchmarking your citation presence against competitors in your category.
02
Gap and opportunity mapping
A clear map of where you appear, where you don't, which queries you should be appearing for, and which specific interventions will have the most impact. Prioritised by estimated return and effort — not by what's easiest to implement.
03
Structured data and content implementation
Schema markup implementation across all relevant page types. FAQ content architecture aligned to AI Overview extraction patterns. Entity definition content — on-site and off-site — that makes your brand unambiguous to AI systems.
04
Citation and authority building
Targeted placement in the review platforms, industry publications, and editorial sources that AI systems weight most heavily for your category. This is sustained work — not a one-off campaign. AI visibility compounds as citation depth grows.
05
Monitoring and monthly reporting
Monthly AI platform audits, AI Overview tracking, brand mention monitoring, and review velocity reporting — with a clear summary of how your AI visibility is changing and what actions are planned for the next month.

Common questions.

Related but distinct. Traditional SEO targets Google's blue-link results. AI Search Visibility targets the AI-generated answer layer — Google AI Overviews, ChatGPT recommendations, Perplexity citations, and voice search responses. Many of the underlying signals overlap (structured data, backlinks, E-E-A-T), but the optimisation targets and measurement methods are different. We offer this as a standalone service and as a layer added to traditional SEO engagements.

No — and any agency that claims they can should be treated with scepticism. AI recommendation systems are probabilistic, not deterministic. What we can do is systematically improve the signals that increase the probability of your brand appearing — and track those signals so you can see the work having an effect, even before you see the recommendation.

Google AI Overview citations — which respond to on-site structured data and content changes — can improve within 4–8 weeks. Generative AI platform recommendations (ChatGPT, Perplexity) are slower to change because they depend on the publication and citation landscape, which builds over months. Meaningful improvement in AI platform presence typically takes 4–6 months of sustained work.

No — it extends it. Traditional SEO (rankings, organic traffic) remains important. AI visibility adds a second layer of presence above and alongside those rankings. The businesses that will win in search over the next 5 years are those that invest in both — not one or the other.

Yes — that's the most common starting point. The audit will establish a baseline, and the strategy will prioritise the interventions with the fastest and highest-impact path to initial AI visibility. Starting from zero is not a disadvantage — it means there are clear, addressable gaps rather than marginal optimisations.

We track proxy signals: AI Overview citation presence (directly measurable in GSC and manually), brand mention volume and sentiment, review platform velocity, structured data validation, and regular AI platform audits (prompting with buyer queries and documenting responses). We report on all of these monthly with clear trend lines.

The AI layer is filling up.
Don't let competitors own it.

Start with an AI Visibility Audit — we'll map your current AI search footprint, benchmark against competitors, and show you exactly where the gaps are.