NeuralAdX Ltd · GEO & AI Search Strategy Podcast
How to Improve AI Search Visibility: GEO Methodology, Citation Tracking & Live AI Retrieval
A practical discussion of Generative Engine Optimisation (GEO) and AI search visibility, comparing how NeuralAdX Ltd and DoodleWeb measure high-intent prompts, diagnose citation gaps, improve pages and re-test AI answers to see whether visibility changes.
NeuralAdX
How to Improve AI Search Visibility
GEO methodology · citation tracking · live AI retrieval
Host
Paul Rowe
Core question
How do you improve AI search visibility?
Guest
Chandan Sharma · DoodleWeb
Measurement
High-intent prompts · citations · competitor positions · re-verification
Answer-engine ready summary
How Do You Improve AI Search Visibility?
Start with a fixed baseline, diagnose the citation gap, make targeted changes and re-test the same questions. In this episode, Paul Rowe of NeuralAdX Ltd and Chandan Sharma of DoodleWeb compare practical GEO workflows built around high-intent buyer prompts, AI citation and mention tracking, competitor-source analysis, technical and content improvements, and repeated live AI retrieval. The key principle is measurement before and after implementation rather than assuming a change worked.
1 · Measure
Run commercially relevant prompts and record where the brand, competitors and cited sources appear.
2 · Diagnose
Inspect the pages AI engines cite instead: structure, headings, facts, tables, pricing and entity signals.
3 · Fix
Improve crawlability, schema, answer-first content, verifiable facts, FAQs, internal linking and topical coverage where appropriate.
4 · Re-verify
Re-run the same prompt set and compare citation, mention and position changes against the baseline.
Official episode listening
Listen to the Podcast Episode
This episode of the NeuralAdX GEO & AI Search Strategy Podcast features Paul Rowe in conversation with Chandan Sharma of DoodleWeb. The discussion focuses on practical AI search visibility methodology: measuring high-intent prompts, diagnosing citation gaps, implementing changes and re-verifying results.
Practical GEO workflow discussed in the episode
Measure, Diagnose, Fix and Re-Verify AI Search Visibility
Paul Rowe and Chandan Sharma describe closely related closed-loop approaches to AI search optimisation. Both start with real buyer-style prompts, compare the brand with competitors, inspect the sources cited by AI engines, make targeted technical or content changes and then test again. The discussion is a methodology comparison, not a claim that every engine uses the same ranking or citation logic.
NeuralAdX Ltd
11-Factor GEO + Live AI Retrieval
NeuralAdX uses high-intent commercial prompts, live answer inspection, citation-source analysis, its 11-Factor GEO Methodology, ongoing citation and visibility benchmarking, and repeated live retrieval testing.
DoodleWeb
SiteSonar Measure-Fix-Prove Loop
Chandan describes SiteSonar as a workflow for tracking buyer prompts, identifying who is cited instead, diagnosing page gaps, drafting or implementing fixes and re-checking the original query set.
What gets diagnosed?
The episode discusses page structure, headings, factual depth, comparison tables, pricing information, schema, crawler accessibility, robots.txt, llms.txt, IndexNow, answer-first writing, verifiable facts, FAQs, internal linking, entity information and third-party directory presence. These are treated as diagnostic and implementation considerations, not as a universal checklist that guarantees citation.
Episode chapters
Podcast Chapter Timestamps
The episode moves from market demand into a practical GEO workflow: prompt tracking, citation diagnosis, technical and content changes, NeuralAdX methodology, ongoing testing and external authority signals.
03:05GEO Methodology: Measure, Diagnose, Fix & Re-Verify
04:20AI Citation Tracking: Prompts, Mentions & Competitor Positions
06:20Why Competitors Get Cited: Reverse-Engineering Winning Pages
08:10Technical GEO Fixes: Crawlers, Schema, llms.txt & Content Structure
09:45NeuralAdX 11-Factor GEO: Live AI Retrieval & Benchmarking
12:35AI Search Testing: Visibility Indexes & Changing AI Engines
14:30Third-Party Citations, Directories & External Authority Signals
15:55Closing: Continuous Testing as AI Search Changes
Full crawlable episode transcript
Transcript: How to Improve AI Search Visibility
Transcript note: This is a cleaned transcript of the spoken podcast, kept close to the original meaning and sequence. Obvious speech-to-text errors, filler, repeated phrases and punctuation have been lightly corrected for readability. Verified names and specialist terms including DoodleWeb, SiteSonar, robots.txt, llms.txt and IndexNow have been normalised. No new substantive claims have been added.
Introduction: AI Search Demand in the US
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
Welcome, Chandan from DoodleWeb. Thank you for joining us here on the NeuralAdX GEO & AI Search Strategy Podcast. Really pleased to see you. I understand you’re about eight hours behind us, so it’s about 7 a.m. your time, yeah?
Chandan Sharma — Founder, DoodleWeb
7:40, yeah.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
And you dragged yourself out of bed to have a podcast with me. What is wrong with you? You legend — I really appreciate it. I thought it would be interesting because over in America you had Google AI Mode earlier than us in the UK, so our listeners might be curious to get a brief grasp of the market there. How is it maturing? What have you noticed with clients or potential clients in terms of interest from businesses? Has it spiked or is it still slowly catching on?
Chandan Sharma — Founder, DoodleWeb
Yeah, it’s a really hot topic in the US market. Everyone you speak to is interested in learning more about it. My current clients, previous clients — whenever we go through new requirements and mention that we are also doing AI search, they are really interested. We build custom websites for businesses, and I’ve personally been doing this for more than 12 years. Demand is very high because the buyer experience is changing.
Chandan Sharma — Founder, DoodleWeb
People used to focus on traditional SEO, but now when you search on Google the AI experience can appear before the traditional organic results. That is the shift we are seeing, and people want to understand how we are going to address it. That is why we built our own internal tool, SiteSonar.
GEO Methodology: Measure, Diagnose, Fix & Re-Verify
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
Just to give this some structure: I appreciate the feedback on how the market is developing. My next question is about the methodology you implement for clients. Can you take me into that, particularly the SiteSonar software you use?
Chandan Sharma — Founder, DoodleWeb
We work through a loop: measure, diagnose, fix and re-verify. A lot of tools tell you what to do, but the question is how you fix it and then re-verify it. We built something around measuring the result, diagnosing the gap, fixing it and checking again.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
So if you have a website page that a client wants to perform better, what does the measurement stage actually do?
AI Citation Tracking: Prompts, Mentions & Competitor Positions
Chandan Sharma — Founder, DoodleWeb
The first thing is prompt tracking. We build a set of real buyer-style questions for the market. For an agency, one example might be ‘best web agency in Seattle’.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
High-commercial-intent questions.
Chandan Sharma — Founder, DoodleWeb
Exactly. We run those questions against AI engines such as ChatGPT, Perplexity and Google AI. For each question we look at whether you are cited, whether you are mentioned, where you appear and who is cited instead.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
So SiteSonar tracks that prompt against the client’s website and monitors how the site performs for that question — essentially AI citation tracking?
Chandan Sharma — Founder, DoodleWeb
Yes.
Why Competitors Get Cited: Reverse-Engineering Winning Pages
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
Let’s say the client is being compared with four or five companies and is appearing fourth. You have established that through the measurement system. What do you then change so the AI engines have a better chance of surfacing the client for that prompt?
Chandan Sharma — Founder, DoodleWeb
That is what we call diagnosis. We look at why you are not cited. For each tracked question where you are missing, we fetch the pages the AI engine actually cites and analyse them — their length, data points, page structure, headings, comparison tables, pricing information and other elements.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
So essentially you are reverse-engineering the company that surfaced at the top, isolating the differences between its page and the client’s page, and then using those differences to improve the client’s opportunity to surface more prominently.
Chandan Sharma — Founder, DoodleWeb
Exactly.
Technical GEO Fixes: Crawlers, Schema, llms.txt & Content Structure
Chandan Sharma — Founder, DoodleWeb
We then connect that diagnosis to the resource hub. The content is drafted against the blueprint from the diagnosis. We look at technical gaps such as AI crawler access, robots.txt, llms.txt, schema and discovery or indexing signals such as IndexNow. On the content side we use answer-first structure, verifiable facts, FAQs, internal linking and brand/entity information. Drafts go through an approval queue before they are published.
Chandan Sharma — Founder, DoodleWeb
After publication we re-test. We keep the question set fixed before and after so we can see whether the citation appeared or the position changed.
NeuralAdX 11-Factor GEO: Live AI Retrieval & Benchmarking
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
It is interesting to hear your methodology because there is a lot of correlation with ours. Our process uses the 11-Factor GEO Methodology, which draws from the Princeton GEO study and additional academic research that has come out since. When we take on a client we run high-intent commercial prompts through AI engines and observe the answers.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
If the client is appearing below a competitor, we dig into the surfaced answer and citation chips to understand the source, information and structure behind the stronger result. We then improve the client page — not simply to copy what is there, but to advance the page where the evidence supports doing so.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
We combine that first live AI retrieval assessment with ongoing AI Citation Benchmarking and AI Answer Visibility & Share of Voice measurement. We use the prescribed prompts, observe the feedback and, where performance drops or stalls against competitors, return to the answer and diagnose what needs to change. We then follow that with live AI retrieval testing. We publish examples of this process on our Proof GEO Works page.
Chandan Sharma — Founder, DoodleWeb
Clients are really on top of it — they want to get cited. We also try to educate them using live search data and the questions people are actually asking, based on the business, its ideal customer profile, competitors and brand.
AI Search Testing: Visibility Indexes & Changing AI Engines
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
Do you do additional testing around AI search? At NeuralAdX we run the UK Business AI Visibility Index to see which companies surface in leading positions in particular sectors for high-intent commercial queries. We record and assess those results across Google AI Mode, ChatGPT, Microsoft Copilot, Claude and Google Gemini.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
The aim is to keep learning how each AI engine behaves, because the presentation and retrieval patterns can change. You have to stay current and observe those changes in real time. Do you have anything similar running or in the pipeline?
Chandan Sharma — Founder, DoodleWeb
Yes. We are building a database around the differences we are seeing across businesses in the same industry so we can understand what is changing and what the leading results have in common.
Third-Party Citations, Directories & External Authority Signals
Chandan Sharma — Founder, DoodleWeb
It is not one thing. You can create resources and answers on a website, but that alone does not mean you will immediately surface. There are third-party signals as well, including important directories that AI engines may use or cite.
Chandan Sharma — Founder, DoodleWeb
We also have a listening radar that shows the engines where you are not cited and the directories where you are absent. That gives the business a list of places to review, register or improve where appropriate, and then come back and check again.
Closing: Continuous Testing as AI Search Changes
Chandan Sharma — Founder, DoodleWeb
We are building every day to understand what is being cited and how these AI engines are changing.
Paul Rowe — Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
That’s great. We’ll pause it there and definitely come back to you in the future. I wanted this to be a relatively short introduction focused on one topic: the methodology and the ways we go about improving results for clients. Fundamentally, that is what clients listening care about. I’m glad we stayed mainly on that topic.
Contextual resources referenced by the discussion
AI Search Visibility, GEO Methodology and Citation Tracking Resources
These pages provide the supporting methodology, benchmarks and product context behind the topics discussed in the episode.
NeuralAdX methodology
11-Factor GEO Methodology
The NeuralAdX implementation framework referenced by Paul during the episode.
Citation measurement
AI Citation Benchmark
Repeated citation measurement against a fixed competitor set.
Brand visibility
AI Answer Visibility & Share of Voice
Brand mentions, answer coverage, average position and share of voice measurement.
Live retrieval
Proof GEO Works
Examples of NeuralAdX live AI retrieval testing and supporting evidence.
Live sector research
UK Business AI Visibility Index
Live retrieval studies across major AI platforms using high-intent commercial questions.
DoodleWeb platform
SiteSonar
DoodleWeb’s AI visibility platform discussed by Chandan in the episode.
Terminology
Generative Engine Optimisation Glossary
Definitions for retrieval, citations, entity clarity, authority and related GEO concepts.
Platform-specific guidance
AI Platform Optimisation Guides
Guidance for ChatGPT, Google AI Mode, Gemini, Microsoft Copilot, Claude, Perplexity and other answer engines.
Episode speakers
Paul Rowe and Chandan Sharma
HOST
Paul Rowe
Founder, Chief Generative Engine Optimisation Officer & CEO, NeuralAdX Ltd
Paul hosts the NeuralAdX GEO & AI Search Strategy Podcast and leads NeuralAdX work on AI citations, visibility measurement, live retrieval testing and structured GEO implementation.
GUEST
Chandan Sharma
Founder, DoodleWeb
Chandan discusses DoodleWeb’s practical AI search visibility work and SiteSonar, including prompt tracking, citation diagnosis, technical and content fixes, and re-verification.
Answer-engine ready questions
Frequently Asked Questions About Improving AI Search Visibility
How do you improve AI search visibility?
Use a repeatable loop: establish a baseline with commercially relevant prompts, record citations and mentions, inspect the sources AI engines cite instead, improve the technical or content gaps that are genuinely relevant, and then re-run the same prompts to measure change.
How can a business measure AI search visibility?
Track a fixed set of buyer-style questions across the AI platforms that matter to the business. Record whether the brand is mentioned or cited, its position where applicable, which competitors appear, and which source pages are being used as evidence.
What does GEO diagnosis involve?
In this episode, diagnosis means comparing the brand’s pages with the pages cited by AI engines for the same question. The discussion covers structure, headings, factual depth, comparison tables, pricing, schema, crawlability, answer-first content, internal links and external authority signals.
Why re-test the same AI prompts after making changes?
Keeping the prompt set fixed creates a before-and-after comparison. It helps distinguish an observed movement in citations, mentions or position from a change in the question being asked.
Do Paul Rowe and Chandan Sharma use exactly the same GEO methodology?
No. The episode shows strong overlap in the measurement-diagnosis-fix-reverification loop, but NeuralAdX and DoodleWeb use different frameworks, tools and implementation systems. The discussion compares the principles rather than claiming the two methods are identical.
CONTINUE THE RESEARCH
Explore More NeuralAdX GEO & AI Search Methodology
Continue through the podcast, live retrieval evidence, benchmarks, methodology and platform-specific Generative Engine Optimisation resources.