How to Rank in Claude, ChatGPT & Gemini AI Search (James Dooley Interviews Charles Floate)
Listen on your favourite platform
| Platform | Link |
|---|---|
| YouTube | Listen on YouTube → |
What Does “How to Rank in Claude, ChatGPT & Gemini AI Search (James Dooley Interviews Charles Floate)” Talk About?
This episode of the Fatrank Podcast features James Dooley interviewing SEO expert Charles Floate on the strategies brands can use to improve their visibility across AI search platforms including ChatGPT, Claude, Gemini, and Perplexity. The conversation digs into how different AI models retrieve and process information, with Charles explaining that ChatGPT tends to expand user queries into longer, more formal search strings drawn from training data, while Claude keeps initial queries short and refines its searches based on what it finds. Understanding these distinct retrieval behaviours is presented as foundational to building an effective AI visibility strategy.
The episode also covers the concept of grounding, which refers to the process by which AI models go out and fetch fresh, real-time information rather than relying solely on potentially outdated training data. James and Charles explain that Gemini pulls live results from Google while ChatGPT uses Bing for grounded queries. They then move into training data strategy, discussing how brands can improve their chances of being ingested into AI training datasets by spreading their content, PDFs, and brand mentions across authoritative data sources like Common Crawl, Internet Archive, and Scribd. The conversation wraps up with a discussion on entity consensus, the importance of consistent sentiment across sources, and why multimodal content including video is increasingly valuable as AI systems evolve beyond text.
“If you can understand what the AI model already knows about your niche, you can reverse engineer the sources it trusts and continually recommends.”
— Charles Floate
Who Are the Guests on “How to Rank in Claude, ChatGPT & Gemini AI Search (James Dooley Interviews Charles Floate)”?
Charles Floate is an SEO strategist and digital marketing expert with a deep focus on search algorithms, link building, and emerging AI search technologies. In this episode he demonstrates extensive knowledge of how large language models retrieve and rank information, explaining the technical differences between platforms like Claude, ChatGPT, and Gemini at a level that is accessible to both practitioners and brand strategists. His insights into training data ingestion, entity consensus, and multimodal AI position him as a leading voice in the evolving field of AI search optimisation.
James Dooley is the host of the Fatrank Podcast and an established figure in the SEO and digital marketing community. He guides the conversation with well-structured questions that draw out actionable detail from Charles, touching on practical areas like listicle strategy, grounded search, query fan-outs, and the role of video in AI visibility. His ability to translate complex technical concepts into clear follow-up questions makes the episode highly accessible for a broad audience of marketers and business owners.
What Are the Key Takeaways From “How to Rank in Claude, ChatGPT & Gemini AI Search (James Dooley Interviews Charles Floate)”?
Here are the key points discussed in this episode:
- Different AI platforms retrieve information in distinct ways, so brands should tailor their content strategy to how each model, such as ChatGPT or Claude, actually constructs and refines its search queries.
- Grounded search means AI models fetch live information from sources like Google or Bing rather than relying solely on training data, making fresh and indexable content essential for AI visibility.
- Content used to target AI platforms, particularly listicles, must include honest pros and cons rather than purely promotional language, as AI models like Claude perform additional searches to validate factual accuracy.
- Getting into AI training data is a volume and authority game, and brands improve their chances by spreading consistent mentions, PDFs, guest posts, and podcasts across high-authority data sources that AI companies pull from.
- Modern AI systems are multimodal, meaning brands should build entity presence across text, video, audio, and imagery, with video being especially important for Gemini which can access YouTube transcripts even when they are not publicly visible.
“The data also needs consistency. It needs to reinforce the same sentiment, the same people, opinions and locations connected to that entity. If different sources all say completely different things, the AI gets confused and cannot properly trust the entity.”
— Charles Floate
Is “How to Rank in Claude, ChatGPT & Gemini AI Search (James Dooley Interviews Charles Floate)” Worth Listening To?
This episode is a genuinely useful resource for anyone trying to understand how AI search actually works under the hood rather than at a surface level. Charles Floate goes beyond general advice and gets into specific mechanics, such as how Claude uses short initial queries and iteratively refines its searches, or how ChatGPT expands a simple user query into a much longer formal string using training data. These details give practitioners something concrete to act on rather than vague guidance about producing quality content.
What makes this episode especially worth your time is the way it bridges technical AI behaviour with practical brand strategy. The discussion on Common Crawl, training data ingestion timelines, entity consensus, and multimodal content gives listeners a layered understanding of how AI visibility is built over time. Whether you are an SEO professional, a brand manager, or a content strategist, the frameworks discussed here are directly applicable and represent some of the most forward-looking thinking available on the subject of AI search optimisation.
Who Should Listen to “How to Rank in Claude, ChatGPT & Gemini AI Search (James Dooley Interviews Charles Floate)”?
This episode is ideal for:
- SEO professionals looking to adapt their strategies for AI-powered search platforms and understand how LLMs retrieve and rank information.
- Digital marketers and content strategists who want to build brand visibility in ChatGPT, Claude, Gemini, and Perplexity.
- B2B and SaaS brands aiming to improve entity recognition and citations across AI tools that their target audiences are already using.
- Business owners and brand managers who want to understand how training data, entity consensus, and multimodal content contribute to long-term AI discoverability.
Where Can You Listen to Fatrank Podcast?
You can listen to Fatrank Podcast on all major podcast platforms:
- Apple Podcasts – Search for “Fatrank Podcast” in the Podcasts app
- Spotify – Available on Spotify for free
- Amazon Music / Audible – Listen through your Amazon account
- Overcast – For iOS users who prefer a dedicated podcast app
- Pocket Casts – Cross-platform podcast player
You can also subscribe using the RSS feed: https://feeds.transistor.fm/fatrank-podcast
What Are Listeners Saying About This Episode?
“The breakdown of how ChatGPT versus Claude handle search queries differently was genuinely eye-opening. I had no idea Claude was refining its searches iteratively based on initial results. This episode immediately changed how I think about structuring content for AI platforms.”
“Charles Floate explains the grounding concept better than anything else I have read or watched on the topic. The point about Gemini accessing YouTube transcripts even when they are not publicly visible is something I am already testing. Highly practical episode.”
“The section on entity consensus and why inconsistent information across sources confuses AI models was a lightbulb moment for me. I work in B2B SaaS and the advice about Claude being the dominant platform in that space alone made this worth listening to.”
James Dooley and Charles Floate discuss how brands can increase AI search visibility across platforms like ChatGPT, Claude, Gemini and Perplexity. The conversation explains how grounded search queries, training data, listicles and entity consensus influence AI rankings and citations. Charles Floate breaks down how different AI models retrieve information, why query structures vary between platforms, and how brands can optimise content for AI discovery. The podcast also covers Common Crawl, PDFs, guest posts, multimodal AI, YouTube transcripts, semantic consistency and third-party trust signals. They explain why balanced sentiment, factual accuracy and broad entity coverage are essential for improving AI visibility. This video is aimed at SEO professionals, digital marketers and brands looking to improve AI search rankings, LLM citations and entity recognition in modern search algorithms.
James Dooley: How to increase AI search visibility. Today I'm joined with Charles Floate.
Charles Floate: Thanks for having me.
James Dooley: How are we doing?
For anyone who wants to get higher rankings and more citations in places like Claude, Perplexity, ChatGPT and Gemini, how do you increase visibility within AI?
Charles Floate: The first thing is you need to have a defined approach around where your audience is.
Most audiences are probably using ChatGPT, but if you're in B2B or SaaS, it is more likely to be Claude right now. First, you need to figure out where your audience is. Then you need to figure out how to approach rankings for that specific niche. Right now, listicles and content on your own site that can get picked up in grounded search queries are the most important. Those queries actually change depending on the model you are using. OpenAI and ChatGPT tend to use very long, formal queries that are extracted from their training data. For example, if you type “best CRM tools for accountants” into ChatGPT, it might expand the query into something like “best CRM for small business accountants, HubSpot versus Trello versus Atlassian” and turn it into a much longer query. Claude, on the other hand, tends to keep queries between one and six words, then refines the query after the initial search based on the information it gets back. Depending on what comes back from that first search, it will continue doing additional searches based on the original context. So first you need to understand where your audience is. Then you need to create content targeted around how the AI model actually finds and retrieves information. There are also some rules around listicles. You cannot be too self-promotional. You need to include drawbacks and honest opinions. You cannot make exaggerated claims like saying nobody else in the world compares to your brand. You need honesty, factual information and balance. Claude also performs additional search queries to validate some of the facts and findings.
James Dooley: So when it comes to listicles and increasing AI visibility, you're saying the sentiment cannot just be positive, positive, positive. You need pros and cons as well?
Charles Floate: Exactly.
James Dooley: You also mentioned grounded data. What do you mean by grounding for anyone watching this?
Charles Floate: Grounding is when the AI goes out and searches for additional information.
Gemini will go to Google. ChatGPT will go to Bing. Claude uses a mixture of its own indexing and third-party sources. Grounding is making sure the AI is not relying on outdated training data from months ago. It ensures the model is using the freshest and most relevant information possible. Most AI model companies now force grounded searches for fresh queries or current events so they can provide more accurate and up-to-date information.
James Dooley: So when we break down AI visibility across Claude, ChatGPT and Gemini, should people be using different strategies for different LLMs?
Charles Floate: If you want to get really granular and only target one specific AI platform, then yes.
But if your audience is spread across multiple platforms, which is usually the case, then you need a broader strategy built around overlapping signals that all AI systems use. Some models have specific grounding methods, but there are also generalised practices across all of them. They all look for official domains, documentation and PDFs. Most of them also use some level of training data when generating search queries. They are not just using the user input. They are combining the user query with what they already know about that niche. If you can understand what the AI model already knows about your niche, you can reverse engineer the sources it trusts and continually recommends.
James Dooley: So when increasing AI visibility, we have the live search side of things. Gemini uses Google and ChatGPT uses Bing.
But what about training data? How can someone get into the training data, how long does it take, and how can brands become part of that dataset?
Charles Floate: How long it takes depends on the company.
Some companies release new models every few months. Others only release once a year. That affects how quickly new data gets ingested. Even if the last training date says November 7th, that does not mean your source was crawled on November 7th. It may have been crawled months earlier. Most companies are still using Common Crawl, Internet Archive and various PDF sources like Scribd and other large data repositories. If you can get your content, PDFs and brand mentions across overlapping data sources that AI companies pull information from, then you have a much better chance of getting into the next training cycle.
James Dooley: How much data is needed for a brand to become properly absorbed into training data?
Charles Floate: One PDF with one mention of your brand probably is not enough.
But if you have hundreds of pages, multiple PDFs, an entire website, guest posts, podcasts and other sources that all mention your brand, then there is a much larger chance of ingestion. It becomes a volume game, but there is definitely weighting applied to the authority of the sources themselves.
James Dooley: So when you're talking about a volume game, is that what people mean when they talk about consensus?
Charles Floate: Yes.
The more sources you appear on, the more likely it is that you will be included in training data and downloaded by one of the AI systems. But the data also needs consistency. It needs to reinforce the same sentiment, the same people, opinions and locations connected to that entity. If different sources all say completely different things, the AI gets confused and cannot properly trust the entity. That is important because if the AI ingests incorrect information about your business early on, it becomes much harder to change its understanding later.
James Dooley: A lot of people talk about query fan-outs.
Should brands proactively build out things like awards, case studies, testimonials and reviews across third-party sites to strengthen consensus? And what about video? Can video help improve AI visibility?
Charles Floate: Specifically for Gemini, we found that even if a video does not have public transcripts, Gemini still seems to have access to transcripts through the backend.
Gemini can pull transcript information from YouTube videos even when the transcript is not publicly visible. For Gemini specifically, video is very important. Other models handle video differently depending on the query and whether they prioritise YouTube content. But I would always recommend using video because modern AI systems are no longer just LLMs. They are multimodal. They can understand images, video, audio and text. Most AI systems are moving in that direction, so you should position your brand not just through text but also through audio, video and imagery to build stronger entity understanding and consensus across multiple formats.
James Dooley: Anyone watching this, I hope you enjoyed the video and podcast about increasing AI visibility.
Today I was joined by Charles Floate. Make sure you check out the links in the description. We have other videos covering how to rank in Bing search, increase exposure in ChatGPT, parasite SEO and what is working in today's algorithms. Charles, it has been an absolute pleasure.
Creators & Guests
Host
James Dooley is the founder of FatRank which is a UK lead generation company. James Dooley is the current CEO of FatRank that provides high-quality leads for UK business owners.