Paris Childress of Hop AI and Simon Young of Question.Marketing take one of Simon's clients, WhatsApp Business Platform Stitch AI, and start building its visibility inside ChatGPT, Google AI Overviews and Google AI Mode in public. No case study gloss. Day one, live, including the baseline nobody wants to publish: Stitch is mentioned in one prompt out of thirty.

Over the next 60 days this series follows the whole build. This first session covers how the prompt set gets chosen, how share of voice is measured and why it is harder than SEO ever was, how a knowledge base of 1,335 sales call recordings gets turned into content AI models will cite, and what "information gain" actually means when everyone is drowning in AI slop.

WHAT YOU'LL TAKE AWAY

How to attribute leads from AI when there is no click to track
Why the prompt set is the part most businesses get wrong
How to find the words your customers really use, using competitor reviews
Why first-party knowledge beats volume when you want to be cited
How to baseline your share of voice before you spend a penny on content

CHAPTERS

00:00 Who's on this and what we're building
01:00 From black hat SEO to answer engines: Simon's route in
02:00 Where did all the clicks go?
03:00 Calling out AI referral numbers nobody can prove
04:00 Is GEO a real channel, or SEO with a new sticker?
05:00 Zero-click search: why an AI referral leaves no trail
07:00 The "how did you hear about us?" fix for AI attribution
08:00 What Stitch AI does, and why the category is hard to name
10:00 "Bulk WhatsApp": the term customers search, not the one you'd pick
13:00 Using G2 to find the category your competitors sit in
14:00 Mining 500 competitor reviews for the voice of the customer
15:00 Inside the knowledge base: 1,335 sales calls in 24 hours
17:00 Information gain explained: scoring what models haven't seen
18:00 AI slop, the Anthropic watermark and where LinkedIn lands
21:00 Does AI-generated content get penalised? The honest answer
23:00 Building 30 prompts across top, middle and bottom of funnel
25:00 Can you retire a prompt once you own it?
26:00 The baseline: mentioned in 1 prompt out of 30
27:00 Which engines we measure, and which we ignore for now
29:00 Turning the gaps into a content plan
31:00 Case studies, comparison pages and keeping data safe
32:00 The 60-day goal and what happens on Day 2

ABOUT THE GUESTS

Paris Childress is the founder of Hop AI and GEOForge, the platform used throughout this session to build the knowledge base, track prompts and measure share of voice.
https://hoponline.ai

Simon Young runs QuestionMarketing, a done-for-you AI implementation and answer engine optimisation consultancy in Doncaster, South Yorkshire. He has been publishing on answer engine optimisation since October 2019, more than three years before ChatGPT existed.
https://question.marketing
https://www.linkedin.com/in/aeo

Stitch AI is an official Meta business partner and WhatsApp communication platform for businesses running messaging at scale.

SUBSCRIBE for Day 2, where the first content drafts come out of the knowledge base and we see whether the share of voice moves.

Questions for the next session go in the comments. We read them live.

Generative engine optimization (GEO), also called answer engine optimisation (AEO) or AI search optimisation, is the practice of getting a brand named and cited inside AI-generated answers rather than ranked in a list of links. This session covers AI visibility, share of voice measurement, LLM citations, prompt tracking, ChatGPT, Google AI Overviews, Google AI Mode, zero-click search, information gain, first-party knowledge bases and build-in-public GEO strategy for startups and B2B SaaS.

#GEO #AEO #AIVisibility #GenerativeEngineOptimization #AISearch #ChatGPT #B2BSaaS

They are asking: “How can we use AI to produce more content?”

The better question is: “What proprietary knowledge do we have that AI cannot find anywhere else?”

In the second episode of GEO Day 1, Simon Young and Paris Childress will examine why a proprietary knowledge base is becoming the most important content asset a company can build.

The internet is rapidly filling with traceable AI slop: articles assembled from the same public sources, repeating the same claims, following the same structures and offering no meaningful new information. The wording may be different, but the underlying content is not.

What's more... Anthropic just announced an AI watermark similar to Gemini's SynthID, and other LLMs are sure to follow suit. Soon, All AI-assisted content will be easy to spot everywhere it's published.

This creates a fundamental problem.

If your AI tools are grounded in the same public information available to everyone else, they will produce variations of what already exists. Publishing faster will simply help you create more interchangeable content.

That content will struggle to earn attention, trust, links and citations—whether the reader is a human, Google or a large language model (LLM).

The alternative is high information gain content.

High information gain content gives the reader something genuinely new: an expert observation, original data, a customer insight, a tested methodology, a detailed case study, a contrarian conclusion or evidence that advances the existing conversation.

But information gain cannot be manufactured through prompting alone.

It must be grounded in proprietary knowledge.

That means building a structured knowledge base containing the information your business knows but the wider internet does not:

- Interviews with subject-matter experts
- Customer questions, objections and buying language
- Original research and internal data
- Case studies with specific evidence and outcomes
- Product knowledge and implementation experience
- Proprietary frameworks and methodologies
- Opinions formed through real-world experience
- Lessons from successes, failures and edge cases

This knowledge base becomes the grounding layer for content production. AI can then help retrieve, organize and transform that knowledge into useful assets without replacing it with generic internet consensus.

The result is not “AI-generated content.”

It is company-generated knowledge, structured and amplified by AI.

In this LinkedIn Live session, we will discuss:

- Why generic AI content is becoming increasingly easy to recognize
- What “information gain” actually means in practice
- Why prompting cannot compensate for weak source material
- What belongs inside a proprietary knowledge base
- How expert interviews can systematically capture institutional knowledge
- How one source of proprietary insight can support articles, landing pages, sales content and other formats
- How a knowledge base strengthens both traditional content marketing and GEO
- Where human expertise must remain in the workflow
- How startups can begin building this asset without a massive content operation

This is not a session about producing more content.

It is about building a source of truth that allows your company to publish content competitors cannot easily reproduce—and that humans and AI systems have a reason to trust, reference and cite.

If AI can recreate your article without knowing anything about your company, the article probably contains very little defensible value.

Join Simon and Paris for a practical discussion about building the knowledge foundation behind sustainable AI visibility.

In this episode, Paris Childress, founder of Hop AI and co-founder of GEO Forge, looks at what may be the beginnings of an entirely new search ecosystem — complete with its own analytics, advertising infrastructure, crawlers, and measurement challenges.

In this episode:

📊 Google's new GEO reporting already has a data problem
Google Search Console's new generative AI performance reporting is giving marketers first-party visibility into AI citations and impressions — but a logging bug is also a reminder that GEO measurement is still very immature.

💰 Advertising inside AI answers is becoming a real media channel
Similarweb is now tracking advertising placements across ChatGPT, Google AI Mode, and AI Overviews. The bigger takeaway: paid AI search is quickly becoming measurable enough for marketers to treat it as a legitimate advertising channel.

🎯 OpenAI is building serious advertising attribution infrastructure
ChatGPT advertisers now have access to increasingly familiar performance marketing technology, including pixel-based conversion matching. OpenAI's advertising stack is starting to look a lot more like the infrastructure marketers already know from Google and Meta.

🍎 Why is Applebot dramatically increasing its crawling capacity?
Apple has reportedly expanded Applebot's crawl infrastructure by thousands of IP addresses. Apple hasn't explained why — but it's a fascinating signal about where Apple's AI and search ambitions could be heading.

🔎 Ahrefs is trying to estimate AI prompt demand
Ahrefs has introduced an "AI-adjusted volume" metric designed to approximate demand across AI platforms. But can we ever have a true equivalent of SEO keyword volume for AI prompts?

My prediction: probably not.

🏷️ AI has a brand categorization problem
A large language model (LLM) can know plenty about your company while still failing to associate your brand with the category you want to own. That creates a critical new GEO challenge: category association.

Comparisons, customer proof, partner references, reviews, earned media, and consistent positioning may become increasingly important signals for teaching AI systems where your brand belongs.

📉 “Ranking in ChatGPT” may be the wrong KPI entirely
New research suggests different ChatGPT modes can rely on dramatically different retrieval systems and source sets.

That's why GEO isn't simply about getting a webpage to “rank.”

The real objective is getting the right passages, claims, and brand associations surfaced by AI systems at the right moment.

And that requires marketers to rethink some of the fundamental assumptions we've carried over from SEO.

Subscribe to The GEO Show for ongoing analysis of the news, experiments, strategies, and emerging tactics shaping visibility across ChatGPT, Google AI, Perplexity, Claude, and the rest of the AI search ecosystem.

💬 Question: Do you think we'll ever have a reliable equivalent of keyword search volume for AI prompts — or is prompt volume fundamentally impossible to measure?

In this episode, Paris Childress, founder of Hop AI and co-founder of GEO Forge, breaks down five stories that could have major implications for B2B and SaaS marketing teams.

In this episode:

📈 ChatGPT referral traffic is surging
Demandbase data shows ChatGPT-referred visits to B2B websites increased more than 300% year over year. Is GEO starting to become a true performance marketing channel?

🤖 AI chatbots are changing how B2B buyers choose software
G2 research finds 71% of B2B software buyers now use AI chatbots for research — and 51% start with AI chatbots more often than Google.

🔎 OpenAI confirms “query fan-out”
ChatGPT can turn a single prompt into multiple targeted searches. We look at why those hidden queries matter for GEO — and how marketers can use Bing Webmaster Tools to uncover and optimize against them.

📌 Pinterest shows what GEO at scale can look like
Pinterest deployed a system using vision-language models, query prediction, dynamically generated collection pages, and internal linking — producing a reported 20% increase in organic traffic.

⚠️ Microsoft draws a line against “manipulative GEO”
Bing is now explicitly warning that abusive GEO tactics can result in reduced visibility or even delisting. But Microsoft and Google continue to suggest that good GEO largely comes from good SEO. I’m not convinced.

We also dig into one of the most important distinctions between traditional SEO and GEO: information gain.

In an internet increasingly flooded with generic AI-generated content, the brands that win AI visibility will be the ones that give large language models something genuinely new to learn — proprietary data, original research, first-hand experience, and unique expertise.

The GEO Show covers the news, strategies, experiments, and emerging tactics shaping how brands get discovered and cited by ChatGPT, Google AI, Perplexity, Claude, and other AI platforms.

If you're responsible for SEO, GEO, content, demand generation, or B2B marketing, subscribe and follow along.

💬 Question: Do you think GEO is fundamentally different from SEO — or is it simply the next evolution of it? 

Same recycled facts. Same obvious advice. Same five bullet points scraped from the same five sources—rewritten by a machine and published by a company with no genuine expertise in the final output.

This is what happens when everyone uses the same models, the same public information and increasingly similar prompts.

The result is an internet drowning in synthetic consensus.

And publishing more of it won’t win you visibility in Google, ChatGPT, Perplexity, Gemini or any other answer engine.

In GEO Day 3, Simon Young and Paris Childress will break down the alternative: anti-slop content.

Anti-slop content contains something an AI model cannot produce without access to your company’s knowledge, evidence and experience.

It might include:

• A subject-matter expert’s original point of view
• First-party research or proprietary data
• A detailed customer case with specific evidence
• A tested framework developed through real work
• An unexpected insight that challenges industry consensus
• First-hand experience with a problem, product or market
• A clear, defensible opinion—not a summary of everyone else’s
• Information that meaningfully advances the existing conversation

This is high information gain content: material that leaves the reader—and potentially the large language model (LLM)—knowing something it could not have learned from the other ten articles already ranking for the topic.

That distinction matters because Generative Engine Optimization (GEO) is not a content-volume competition.

AI systems already have an almost unlimited supply of generic explanations. They do not need your company to publish another “ultimate guide” built from information they can find everywhere else.

What they need are useful, credible and sourceable contributions.

Content with facts they can extract.

Evidence they can reference.

Ideas they can attribute.

Insights worth citing.

During this LinkedIn Live session, we’ll explore:

• How to recognize AI slop—even when it sounds polished
• Why “human-written” does not automatically mean high quality
• What information gain looks like in real B2B content
• Why proprietary knowledge is the foundation of defensible content
• How to extract original insight from internal experts
• How to turn interviews, research and customer evidence into GEO-ready assets
• Where AI strengthens the process—and where it destroys differentiation
• How to structure content so its strongest claims are easy to understand, extract and cite
• How to audit your current content for generic thinking
• What a scalable anti-slop content workflow looks like

This is not an anti-AI session.

AI is an extraordinary production and transformation tool. But it cannot manufacture genuine expertise, original evidence or lived experience. 

If the source material is generic, the output will be generic—regardless of how sophisticated the prompt sounds.

The winning model is not AI replacing human expertise.

It is human expertise, proprietary knowledge and original evidence—structured and amplified by AI.

By the end of this session, you’ll have a clearer standard for deciding what deserves to be published, what belongs in the recycling bin and what has a realistic chance of earning attention, trust and GEO visibility.

Because in a world where anyone can generate content, the advantage belongs to companies that actually have something to say.

Join Simon and Paris for The Anti-Slop Content That Wins GEO Love.