Beyond GEO: Cascading Queries, NEEATT Trust Signals, and Why AI Prefers Answering From Memory Over Search Results - Jason Barnard On The James Dooley Podcast

How AI Recommends Businesses in 2026 (James Dooley Interviews Jason Barnard)

Video by: James Dooley. Host: James Dooley. Guest: Jason Barnard, Founder and CEO of Kalicube®. January 23, 2026

TL;DR: Securing a recommendation from AI Assistive Engines in 2026 requires shifting your focus from ranking entire webpages to optimizing specific “chunks” of authoritative information. Jason Barnard, the World Authority on AI Brand Intelligence and CEO of Kalicube®, joins James Dooley to explain that the future of digital visibility is AI Assistive Agent Optimization. The goal is to move beyond being “found” to becoming the “preferred solution” - achieving a state where AI recommends you from its own “memory” (training data) rather than having to look you up in an index.

Key Strategies Discussed:

• The Kalicube Process™™: A strategic framework centered on Understandability, Credibility, and Deliverability. Success depends on the AI knowing who you are and trusting you enough to proactively advocate for your brand.
• From Pages to Passages (Chunks): AI doesn’t rank pages; it recommends “chunks” or passages of information. Content must be structured so that algorithms can easily extract and use specific sections to answer conversational user queries.
• Cascading Queries: Algorithms use “Query Augmentation” to ask themselves related questions (e.g., “Who is X?”, “X reviews,” “X credentials”) to build a complete profile. You must provide clear answers for this entire “fan out” of related searches.
• NEEATT Trust Signals: Beyond traditional E-A-T, Kalicube emphasizes NEEATT: Notability, Expertise, Experience, Authority, Trustworthiness, and Transparency. These signals are the primary currency AI uses to choose one business over another.
• First-Party vs. Third-Party Balance: Third-party corroboration (reviews, press) is meaningless without a strong first-party source (your Entity Home). You must provide the “curriculum” on your site, which is then validated by the rest of the web.
• AI as Your Digital Sales Force: When you “train” the AI through consistent digital footprints, the LLM effectively becomes a well-trained employee, acting as your advocate and closer during the final decision-making stage of the user journey.
• The “Niche Down” Mandate: The only way for small businesses or tradesmen to beat Fortune 500 companies is to dominate a hyper-specific niche. Prove you are the absolute best in one narrow category to secure the AI recommendation.
• Strategic AI Brainstorming: Use your daily AI assistant to audit your brand. Ask it to describe who you are and then ask: “If Jason Barnard were to advise me on a strategy based on this, what would he say?” to unlock immediate strategic paths.

The discussion focused on how AI Assistive Engines are changing the rules of visibility - shifting the objective from ranking webpages to becoming the preferred recommendation within AI-driven decision-making.

Speaking on the podcast hosted by James Dooley, Jason Barnard outlined why traditional SEO approaches are no longer sufficient in 2026. The challenge is no longer being found. It is being chosen by AI systems.

From Search Results to AI Memory

A key insight from the session is that AI does not rely solely on live search results. Instead, it increasingly responds from its own “memory” - the accumulated training data and structured understanding it has built over time.

This creates a new benchmark for success.

Brands that depend only on rankings may still appear in search results. However, brands that are clearly understood, well-structured, and consistently validated are the ones AI systems recall and recommend directly - without needing to “look them up.”

Why AI Recommends “Chunks,” Not Pages

The conversation highlighted a fundamental shift in how content is processed.

AI systems do not rank entire pages. They extract and use specific passages or “chunks” of information to answer user queries. This means content must be structured so that key ideas are:

• Clear and self-contained
• Contextually complete
• Easy for algorithms to extract and reuse

Well-structured content increases the likelihood that AI will select and surface it as part of its response.

Understanding Cascading Queries

Another critical concept discussed was cascading queries.

When evaluating a brand, AI systems do not stop at a single question. They automatically generate related queries to build a complete understanding, such as:

• Who is this person or company?
• What are their credentials?
• What do others say about them?
• Are they trustworthy?

To succeed, brands must provide clear, consistent answers across this entire “fan-out” of queries. Any gaps or inconsistencies weaken the AI’s confidence and reduce the likelihood of recommendation.

NEEATT: The New Standard for Trust

The session introduced an expanded framework for evaluating trust: NEEATT.

Beyond traditional signals, AI systems assess:

• Notability
• Expertise
• Experience
• Authority
• Trustworthiness
• Transparency

These signals form the foundation of how AI determines which brand to recommend. Strong performance across all six areas increases algorithmic confidence and strengthens positioning in AI-generated responses.

Balancing First-Party and Third-Party Signals

A key strategic takeaway was the relationship between first-party and third-party data.

Your website - your Entity Home - serves as the central source of truth. It provides the structured narrative that AI systems rely on to understand your brand.

However, this narrative must be supported by external validation. Reviews, press coverage, and third-party mentions reinforce credibility, confirming that the claims made on your site are accurate and trustworthy.

Without this balance, AI systems struggle to reconcile conflicting or incomplete information.

AI as Your Digital Sales Force

The webinar framed AI as more than a discovery tool. It is increasingly acting as a digital sales force.

When properly trained through a consistent and structured digital footprint, AI systems can:

• Recommend your brand during research
• Validate your expertise during consideration
• Reinforce trust at the point of decision

At this stage, AI effectively becomes an extension of your business - guiding prospects toward you with confidence.

The Power of Niche Authority

For smaller brands, the session emphasised the importance of niching down.

Competing broadly against larger, established players is increasingly difficult. However, dominating a highly specific niche allows AI systems to clearly identify a brand as the best solution within that category.

This clarity significantly increases the chances of being recommended, even against much larger competitors.

Using AI to Audit Your Own Brand

The discussion concluded with a practical strategy: using AI itself as a diagnostic tool.

By asking AI systems to describe your brand and evaluate your positioning, you can uncover gaps, inconsistencies, and missed opportunities. This approach provides immediate insight into how machines currently perceive your identity - and where improvements are needed.

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Transcript: How AI Recommends Businesses in 2026 (James Dooley Interviews Jason Barnard)

[00:00:00] James Dooley: How AI Recommends Businesses in 2026. Nowadays, artificial intelligence and the LLMs are recommending lots of different companies, whether the Fortune 500 companies or solopreneurs and tradesmen. So Jason, you started talking about Answer Engine Optimization back in 2017 with your white paper, with Trustpilot, and webinar series with Semrush. Everyone thought you were crazy, including myself.

[00:00:38] You even coined the term Answer Engine Optimization. Now, it’s mainstream. What do you see today that others still don’t? 

[00:00:51] Jason Barnard: I think what I’m seeing today that others don’t is that Answer Engine Optimization was started in 2017 in a real sense, in that the machines, Google started giving answers. A lot of people talk about Generative Engine Optimization today, but actually it’s AI Assistive Engine Optimization.

[00:01:13] Generative is what they do, in terms of how they create those answers. But we’re talking about how does the AI assist people. And then the future is gonna be AI Assistive Agent Optimization. So I’m not looking at just today, AI Assistive Engine Optimization. I’m looking at the future, AI Assistive Agent Optimization. And that’s the point that people are missing. GEO, Generative Engine Optimization, which is a super popular term right now, in my opinion, is already out of date.

[00:01:56] James Dooley: Yeah, that makes sense. 

[00:01:57] Jason Barnard: And they’re missing the fact that we are gonna move very quickly through AI Assistive Engine Optimization to AI Assistive Agent Optimization. 

[00:02:07] James Dooley: Yeah. So with regards to recommendations, I want to just break things down for anyone who might be watching this that might not be very advanced with LLMs or AI and stuff like that.

[00:02:17] The only two questions that I’ve got, is being recommended by AI, whether that’s ChatGPT or Gemini or Claude or Perplexity, is that the same as ranking in Google or is it completely different? 

[00:02:33] Jason Barnard: It’s significantly different. With ranking Google, you’re thinking about pages. In AI and AI Overviews and ChatGPT and Google AI mode, you’re thinking of passages. Google calls them passages, Microsoft calls them chunks. It’s passages, chunks of information that the AI is pulling out. So you need to break your pages down in your own mind into these chunks. So in order to get into those results in the AI, you need to be thinking about the different parts of the page that answer specific questions within the overall question.

[00:03:09] People talk about query fan-out. I talk about cascading queries. And it’s basically the machine asks itself, what are the related queries around this that will help me answer the query better? So if I ask, who is Jason Barnard, the cascading queries, the query fan-out is going to be, who is Jason Barnard, Jason Barnard reviews. Jason Barnard credentials, Jason Barnard career. So that it can answer the question better. And the key here is, a friend of mine was talking about the difference between a child saying something from memory and a child having to look something up in an encyclopedia. If you can get the LLM to answer without looking at the search results and without looking in the Knowledge Graph because it’s able to say it from its existing knowledge, you’re winning the game because you’re gonna be the preferred solution, because it prefers answering off the top of its head.

[00:04:07] James Dooley: Yeah, for sure. On there, with regards to cascading queries, I think there’s a Google pattern for query network, which is part of query augmentation, which has been around. Everyone’s become obsessed with query fan-out as if it’s a brand new feature. And query augmentation has been around for a long time, within Google.

[00:04:27] But I’ve got a follow up question then with regards to two different companies. They’re both trying to do the passages of content and the chunks, and they’re both trying to rank for specific queries. What makes AI trust one company and one website over another website? 

[00:04:46] Jason Barnard: It’s a lovely question because there are multiple layers going on here. If the AI doesn’t have good grip on either of them, let’s say we’ve just got two. If it doesn’t really understand either of them, it will just look at listicles and pick the ones on the listicles because it doesn’t have better resources. And that’s an easy way to win the game short term, if the situation is that the competitor is not understood. But if they understand both of them, i.e., it knows off the top of its head, what each one of them does and how they answer the question or solve the problem, they’re gonna look for the one that has the most credibility signals that it has picked up.

[00:05:26] So it’s E-E-A-T and we call it at Kalicube®, N-E-E-A-T-T. Because we add Notability and Transparency to Experience, Expertise, Authoritativeness and Trustworthiness. 

[00:05:40] James Dooley: And when we’re looking at artificial intelligence recommending you, how important is it with the information that’s on your own website, also known as a first-party source versus what others are saying about you on a third-party source?

[00:05:56] How important is both of them and is one more important than the other? 

[00:06:01] Jason Barnard: Well, the third-party sources don’t mean anything if there isn’t a first-party source. So you have to have the first-party source, so that’s non-negotiable. Then you’ve said what you’ve gotta say and you’ve said, this is who I am, this is who I serve, and this is why I’m the best.

[00:06:18] The machines won’t believe you on your own good word. You need the corroboration. So both are necessary. Without the first party, the third party means nothing. And without the third party, the first party means nothing. Start with first party, build third party. There’s no point in repeating a hundred thousand times, on your own website, exactly the same information. You need corroboration on third-party sites. 

[00:06:43] James Dooley: Yeah, and then another thing is you talk about being found versus being recommended by the LLMs. What’s the difference? 

[00:06:52] Jason Barnard: Well, you can be found by the machines as they crawl around a web. Super important. You’re in the index, you’re in Google’s index, you’re in Bing’s index, or you are being used by ChatGPT and the crawlers coming to your website. They found you.

[00:07:07] Do they actually respect you? Do they care about you? Are they, and we were talking about this earlier on, are they your well-trained employees? The answer is probably no, and the difference there is training. If you train them to use the information they found about you in the way that your real employees would be using it, you’re gonna win the game.

[00:07:29] Train the AI to use your information in the way that you would train your employees to do it. 

[00:07:34] James Dooley: Yeah, I mean, hearing episode number two in this playlist, we speak about the digital sales force. AI employees recommending you at that 11th hour is the difference between you winning and losing a job to a competitor.

[00:07:48] We took in one of the other episodes, we spoke about brand entity SEO for tradesmen and local people. And this leads me onto the next question with regards to a small business like a tradesman versus the big brands, the Fortune 500 companies, how can little, small business try to compete against big brands with AI recommendations?

[00:08:13] Jason Barnard: Yeah, I love this. Just because throughout the history of the internet, and I started in 1998, so it’s not the whole history of the internet that I’ve been through. I missed out on a big chunk at the beginning. But I’ve seen these opportunities where people suddenly say, oh, the little guy can compete against the big guy.

[00:08:30] And it happened in ’98 when I started. It’s happened multiple times as algorithms update. And the AI revolution started out with people saying that, and now people are saying, oh, actually, famous companies or big companies are always gonna have the advantage. The trick has always been niche down. If you can niche down, you can always beat the big guy. Niche down, prove that you are the best for that very specific niche.

[00:08:58] And once you’ve mastered that, expand out. But trying to beat the big guy, toe to toe, is a losing battle. 

[00:09:05] James Dooley: Yeah. So if someone watching this now, whether they’re a solopreneur or tradesman, high net-worth individual, a business owner, whoever it is that’s watching it, what is a key takeaway now? If someone wants to start being recommended by the LLMs, whether it’s ChatGPT, Perplexity, Claude, Gemini. What’s step number one?

[00:09:27] Jason Barnard: If I were re if I were just starting now, here’s a nice trick. Go to the AI you use every day. It knows you. It knows who you are. It knows what your business is, and it understands what you’re trying to achieve. Do a quick brainstorm with it. Ask it. Describe in couple of paragraphs, who am I, what do I do? Who do I serve? Why am I the best? Get the answer to that and then ask it, if Jason Barnard were to advise me on a strategy for my company or myself, given that, what would he say? He could do it with any of them. This is the really sweet part. All of them know my methodologies back to front, upside down, round and round. They will give you a very good answer as to what I would advise. That’s my advice. 

[00:10:28] James Dooley: Great bit of advice. Anyone watching this? This is episode number nine in 11-part playlist series with regards to how AI recommends you and obviously, advancing on how to try to get recommended by the artificial intelligence.

[00:10:43] Jason, it’s been absolute pleasure. 

[00:10:45] Jason Barnard: Thank you, man.