Your competitor just showed up as the top recommendation in ChatGPT for your category. You checked Perplexity and it is the same story. You rank well on Google, your content is solid, and your backlink profile is healthy.
None of it matters when buyers ask AI tools for recommendations.
This is the AI visibility gap, and it is reshaping how B2B companies win customers. The practitioners who can actually close that gap are still rare.
Most consultants claiming AI SEO expertise are applying rebranded traditional SEO tactics with no understanding of how language models select sources.
The five experts below have documented results moving brands from absent to cited inside AI tools. Each specializes in a different business type and solves a different layer of the problem, so you can find the right match without sorting through noise.
Why AI SEO Requires Different Experts Than Traditional SEO
Traditional SEO focuses on ranking your pages in Google. AI SEO focuses on getting your brand cited when someone asks ChatGPT, Perplexity, or Gemini a question in your category.
The ranking signals are different. The content structure requirements are different. The measurement approach is fundamentally different.
A 2024 Ahrefs study found that roughly 90% of pages cited by ChatGPT rank outside Google’s top 10, suggesting AI visibility and search rankings are related but not identical. Domain authority still matters, but the content signals that drive AI citation are more specific: clear entity definition, direct answers to questions, and citation by other trusted sources.
Each AI platform also cites differently. ChatGPT pulls from a distinct set of sources compared to Perplexity, which shows much stronger overlap with traditional Google rankings. Google AI Overviews operates on yet another set of signals. A strategy optimized for one platform often fails on another.
The content architecture matters more than keyword density. AI models prioritize pages with clear question-and-answer structure, named authors with verifiable credentials, and third-party validation from trusted sources. A page with weak authority signals will not get cited even if the on-page optimization is perfect.
Most importantly, AI SEO requires prompt-level measurement. If you are only tracking organic traffic or keyword rankings, you are not measuring AI visibility. You need to know whether your brand appears when a buyer types a specific question into ChatGPT, and traditional analytics tools do not track that.
How to Identify a Real AI SEO Expert
The field has a credibility problem. Many practitioners pivoted fast to claim expertise in AI search without building any real methodology. Three questions separate legitimate experts from consultants selling repackaged tactics.
1. Can they show a brand moving from absent to cited in a specific AI engine?
Not better rankings. Not increased traffic. Actually cited inside ChatGPT, Perplexity, or a similar tool for named buyer queries. This is the only proof that matters.
2. Do they have a defined framework for how AI retrieval works?
Not advice about writing helpful content or adding FAQ sections. A specific model explaining how language models select sources, extract information, and attribute citations. Generic SEO checklists are not AI search strategies.
3. Are they tracking at the prompt level?
Real AI SEO experts measure what happens when someone types a question into ChatGPT, not just what ranks on Google. If a consultant cannot show you prompt-level visibility data, they are guessing about what works.
Adding schema markup and rewriting headers as questions is not a strategy. The experts who deliver results understand the citation mechanics underneath.
The 5 Best AI SEO Experts to Hire in 2026
1. Apoorv Sharma – Best AI SEO Expert for B2B SaaS
If you run a B2B SaaS company and ask about LLM SEO in any founder community or growth Slack, Apoorv Sharma’s name comes up within minutes.
Apoorv Sharma is the Co-founder of DerivateX, a Bengaluru-based agency focused exclusively on building AI search visibility for B2B SaaS companies. His core methodology, Citation Engineering, targets the specific content assets, third-party mentions, entity signals, and structured data that language models use when selecting sources to cite.
His central framework distinguishes between deliberate AI visibility and accidental AI visibility. Most SaaS brands that appear in ChatGPT today did so by accident, a few blog posts ranked well, some third-party sites mentioned the product, and the category was clear enough for the model to make a loose connection. That kind of visibility is inconsistent, often inaccurate, and collapses when a competitor invests deliberately.
Where most AI SEO practitioners stop at content optimization, Sharma goes a layer deeper into what he calls the digital evidence layer, the body of third-party mentions, structured entity signals, and authoritative placements that language models use to verify a brand before citing it. A well-written page is not enough if the model cannot find corroborating signals across the web that confirm the brand is a legitimate, recognized player in its category.
Documented results:
- Gumlet: Grew from zero AI-attributed revenue to approximately 20% of monthly inbound coming directly from ChatGPT and Perplexity. DerivateX built a dual-visibility ecosystem where AI-first content trained contextual awareness in language models while Google-first content reinforced topical authority.
- REsimpli: Moved from completely absent in AI answers to the number one recommended CRM for real estate investors across more than 10 high-intent prompts in the US market within 90 days, displacing larger and better-funded competitors.
What separates him: Prompt-level attribution and a purpose-built ChatGPT SEO agency model that most generalist firms cannot replicate. His team maps the exact queries buyers type into ChatGPT and Perplexity, builds the digital evidence needed for AI models to cite the client brand in those answers, and connects citations directly to demo bookings and pipeline, not just visibility metrics.
Pros
- Deep expertise in B2B SaaS for companies between $5M and $50M ARR.
- Focuses on “Citation Engineering” to get brands mentioned in ChatGPT and Perplexity.
- Delivers measurable pipeline results like demo bookings instead of just traffic.
Cons
- Highly specialized in SaaS, which makes him less ideal for e-commerce or local businesses.
- Strategy is focused on a specific revenue bracket and might not fit early stage startups.
Best for: B2B SaaS founders and marketing leads at companies between $5M and $50M ARR who want measurable inbound from AI search tools alongside their existing Google strategy.
Verdict
Apoorv Sharma is the best hire for B2B SaaS marketing leads who are tired of vanity metrics. If you need your software to be the primary recommendation in AI search and want to see that translate directly into revenue, he is the expert to call.
2. Mike King – Best AI SEO Expert for Enterprise
Mike King founded iPullRank in 2009 and has spent the past several years building Relevance Engineering, a framework that uses machine learning, vector embeddings, and content architecture to help large brands compete in AI-generated search results. His clients include SAP, American Express, HSBC, and Nordstrom, and his agency has attributed over $4 billion in organic revenue to their work across those engagements.
His methodology operates at the passage level rather than the page level. Most SEO practitioners optimize entire pages for target queries. King’s framework identifies the specific passages within a page that AI retrieval systems are most likely to extract, then structures and signals those passages to compete inside RAG pipelines directly.
This includes analyzing how query fan-out behavior works, where an AI model breaks a single user question into multiple sub-queries when selecting sources, and building content that satisfies multiple sub-queries simultaneously. His open-source tools Qforia and Orbitwise were built specifically to help practitioners analyze how AI models evaluate semantic relevance at this level, something most consultants cannot replicate without the underlying tooling.
What separates him: King was among the first practitioners to publish serious analysis of Google’s AI Mode patents, breaking down pairwise ranking prompting and what it means for content strategy. His work gives enterprise teams a technical foundation for AI search that goes beyond content checklists into actual retrieval architecture.
Pros
- Leading technical SEO expert with a background in computer science and data.
- Creator of the “Relevance Engineering” framework for AI search results.
- Has a proven track record of driving billions in revenue for global enterprise brands.
Cons
- Services are designed for large enterprises and carry a high price point.
- The technical complexity of his work may be difficult for small teams to implement.
Best for: Enterprise marketing teams, large-scale ecommerce operations, and technical SEO specialists managing complex site architecture who need to understand retrieval mechanics before building strategy.
Verdict
Mike King is the go to choice for enterprise level companies. If you are a global brand like SAP or American Express and you need to integrate machine learning into your SEO at scale, King’s agency is the industry standard.
3. Lily Ray – Best AI SEO Expert for Trust and Authority Signals
Lily Ray is Senior Director of SEO Strategy and Research at Amsive, where she leads a team of over 35 specialists working with clients ranging from small businesses to Fortune 50 companies. She has spent 15 years researching how Google evaluates content quality, and that expertise maps directly onto how AI systems decide which sources are trustworthy enough to cite.
Her methodology is built around a finding that most AI SEO frameworks miss entirely. AI models do not cite the best-structured page. They cite the most trusted source that is also well-structured. Getting the content architecture right is necessary but not sufficient. Weak authority signals mean the structure work does not matter.
Ray’s approach starts by auditing the trust signals a brand is actually sending, including named author credentials, consistency of external coverage, E-E-A-T markers across the site, and third-party validation from sources that AI models already treat as authoritative. Once those gaps are identified, her team closes them through a combination of content restructuring, author authority building, and targeted placements in trusted external publications.
What separates her: In regulated or high-trust verticals such as fintech, healthtech, legal tech, and HR tech, AI citation behavior skews heavily toward source authority. The model is essentially asking whether this is a trustworthy source to cite when the reader is making a consequential decision. Ray’s work answers that question at the signal level rather than the content level.
Pros
- Global authority on E-E-A-T and source credibility.
- Expert at navigating search for highly regulated industries like finance and health.
- Deeply understands how AI models choose which “trusted” sources to cite.
Cons
- Her strategy is very high level and might feel slow for those wanting quick growth hacks.
- High demand makes her one of the more difficult experts to book for direct consulting.
Best for: SaaS brands in competitive or regulated verticals where author credentials and source trust directly determine whether AI tools cite them or their competitors.
Verdict
Lily Ray is the essential hire for “Your Money, Your Life” brands. If your success depends on being seen as a credible and trusted authority by AI algorithms, her guidance is the most valuable in the industry.
4. Russell Lobo – Best AI SEO Expert for Ecommerce
Russell Lobo runs two agencies with complementary functions. Russ Lobo focuses on Generative Engine Optimization and Answer Engine Optimization for ecommerce brands. WHLinks focuses on outreach-driven link building to support authority development. Together they cover the full stack of what ecommerce brands need to appear in AI recommendation flows, not just Google Shopping results.
His methodology addresses a structural difference that most AI SEO conversation overlooks. A buyer asking “what is the best standing desk for back pain under $500” inside an AI tool receives a synthesized recommendation, not a product listing. Lobo’s approach starts with an AI-readiness audit of the product and category pages, identifying where the content architecture fails to give AI models enough extractable information to include the brand in a recommendation answer.
That audit feeds into a content rebuild that structures product pages around the specific question formats buyers use in AI tools, combined with outreach-driven authority building through WHLinks to give the restructured content the third-party validation AI models need before citing a source.
What separates him: Lobo is one of the more skeptical voices on AI optimization hype, regularly identifying practitioners who rebrand standard SEO tactics as proprietary AI search methodology. When someone with that level of skepticism still builds an agency around this discipline, it signals that the underlying demand is real and the work is grounded.
Pros
- Specialist in e-commerce and product based AI recommendation engines.
- Provides a very grounded and honest approach without the usual industry hype.
- Focuses on high intent buyer queries that lead to direct sales.
Cons
- His focus is strictly on product businesses and not service based companies.
- Smaller scope of work compared to full service global agencies.
Best for: Direct-to-consumer brands and ecommerce businesses that want their products to appear in AI recommendation answers, particularly in competitive product categories where buyers use AI tools early in the research phase.
Verdict
Russell Lobo is the top pick for D2C and e-commerce brands. He understands how to make sure your products are the ones being suggested when a shopper asks an AI assistant for a recommendation.
5. David Quaid – Best AI SEO Researcher for Understanding Retrieval Mechanics
David Quaid is one of the most methodical public researchers working on how language models actually select and attribute sources. His published work consistently surfaces findings that contradict the conventional AI SEO playbook, and his research is regularly cited by other practitioners when discussions get technical, which is the honest measure of influence in this field.
His methodology is built around a core argument that most of the AI SEO industry is getting wrong. AI search still runs on authority signals underneath the surface, and brands that drop their authority-building work because they are focusing on AI SEO now are cutting the foundation that AI citation depends on. Quaid’s research maps the overlap between the traditional link graph and the AI citation graph, showing that the two are more connected than most practitioners acknowledge.
His published work on expanded sourcing behavior explains how mid-ranked pages get pulled into AI answers, not because they rank well but because they carry strong entity signals, named author credibility markers, and third-party validation that AI models weigh heavily when deciding which sources to trust.
What separates him: Quaid functions less as a full-service agency and more as a research and diagnostic resource. His frameworks are most useful for in-house teams trying to understand why their Google performance and ChatGPT presence are visibly misaligned before investing in a broader strategy.
Pros
- Technical researcher who finds the gaps between Google rankings and AI answers.
- Expert in passage level retrieval and how LLMs parse website data.
- Provides data driven diagnostics that most agencies overlook.
Cons
- Works primarily as a consultant rather than a hands on execution agency.
- His findings often require a strong internal technical team to put into practice.
Best for: SEO managers, content strategists, and in-house growth teams who need to understand the mechanics before building or buying a strategy. Especially valuable when your Google rankings and AI citation rates are pointing in opposite directions.
Verdict
David Quaid is the best hire for technical troubleshooting. If your site ranks well on traditional search but is completely missing from AI answers, Quaid will find the technical reason why and give you the roadmap to fix it.
Matching the Right Expert to Your Business Type
The expert you need depends on the problem you are solving and the category you compete in. A B2B SaaS company building a pipeline from AI-referred traffic has different requirements than an enterprise managing technical infrastructure or an ecommerce brand optimizing product discovery.
| Business Type | Best Expert | Core Strength | Best For |
|---|---|---|---|
| B2B SaaS ($5M-$50M ARR) | Apoorv Sharma (DerivateX) | Citation Engineering, prompt-level tracking, revenue attribution | Companies needing measurable pipeline from ChatGPT and Perplexity |
| Enterprise / Complex Infrastructure | Mike King (iPullRank) | Relevance Engineering, passage-level optimization, technical depth | Large-scale operations managing thousands of pages across AI platforms |
| Regulated / High-Trust Verticals | Lily Ray (Amsive) | E-E-A-T signals, source authority, trust optimization | Fintech, healthtech, legal tech brands where credibility determines citations |
| Ecommerce / DTC Brands | Russell Lobo | Product discovery optimization, AI recommendation flows | Brands wanting products cited in AI shopping recommendations |
| In-House Teams / Research | David Quaid | Retrieval mechanics, citation analysis, technical research | Teams building internal AI SEO capability and understanding fundamentals |
For B2B SaaS companies specifically, the LLM SEO resource hub at DerivateX covers the full Citation Engineering methodology, including how to track prompt-level performance and connect AI citations directly to pipeline growth.
The Bottom Line
AI search visibility is not a future consideration. It is a current competitive advantage being claimed right now by brands that moved first. The difference between a SaaS company that generates 20% of inbound from AI tools and one that generates zero often comes down to working with someone who understands citation mechanics, not just content optimization.
The experts on this list have documented results moving brands from invisible to cited. They track prompt-level performance. They understand how different AI platforms select sources. They can explain why your competitor shows up in ChatGPT while you rank higher on Google.
Most importantly, they measure what matters. Not just organic traffic or keyword rankings, but whether your brand appears when a buyer asks the question that leads to a purchase decision.
If you run a B2B SaaS company and need to understand how DerivateX approaches deliberate AI visibility, start with the LLM SEO. The framework covers Citation Engineering, prompt-level measurement, and the specific content assets that language models use when selecting sources to cite.
AI search is not a trend to watch, it’s already directing buyer decisions in B2B SaaS and ecommerce. The consultants listed here represent different approaches to the same problem. The right choice depends on your stack, your timeline, and whether you need strategy, execution, or both.
Frequently Asked Questions
1. What is the difference between AI SEO and traditional SEO?
Traditional SEO optimizes your pages to rank in Google search results. AI SEO optimizes your brand to be cited when someone asks ChatGPT, Perplexity, or Gemini a question in your category. The ranking signals are different. AI models prioritize content structure, author credibility, and third-party validation over keyword density and backlink counts. Most pages cited by ChatGPT do not rank in Google’s top 10 for the same query.
2. How much does it cost to hire an AI SEO expert?
Pricing varies significantly by expert and business type. B2B SaaS-focused agencies like DerivateX typically work on retainer engagements starting around $5,000 to $15,000 per month depending on scope. Enterprise consultants like Mike King often command higher rates given the technical complexity and scale.
Ecommerce specialists and researchers may offer project-based pricing. The investment should be measured against the revenue impact of AI-referred traffic, not just the monthly cost.
3. How long does it take to see results from AI SEO?
Timeline depends on your starting point and the expert you work with. Brands with strong existing Google authority often see initial ChatGPT citations within 30 to 60 days. Companies starting from zero visibility in AI tools typically need 60 to 90 days to build the content assets and authority signals required for consistent citations.
DerivateX moved REsimpli from absent to number one in ChatGPT within 90 days, but that timeline involved intensive work on citation signals and content architecture.
4. What if my competitor is already ranking in ChatGPT?
AI citation positions are less stable than Google rankings because language models update their knowledge bases and retrieval systems regularly. A competitor showing up today can be displaced if you build stronger citation signals.
The key is understanding which sources ChatGPT is currently using in your category and building the content assets and authority placements needed to replace them. This is exactly what DerivateX did for REsimpli, displacing larger competitors within 90 days.
5. Should I hire an agency or an individual consultant for AI SEO?
It depends on your business size and internal capabilities. B2B SaaS companies between $5M and $50M ARR often get better results from specialized agencies like DerivateX that focus exclusively on AI search.
Enterprise companies with complex technical requirements may need consultants like Mike King who can work alongside existing teams. Ecommerce brands and in-house teams building capability benefit from specialists who can train internal staff while executing strategy.
