I Asked ChatGPT to Recommend Consultancies
I Asked ChatGPT to Recommend Marketing Ops Consultancies. Here’s What It Revealed
*Experiment log. an operator’s notes on AI vendor discovery, entity signals, and machine-readable positioning*
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TL;DR
- I asked ChatGPT a simple category query: recommend marketing ops consultancies for mid-market B2B. My firm and several respected peers were absent from the results. - The firms that appeared shared a pattern: clear category labels, extractable claims, and structured third-party evidence. - This isn't an SEO problem. It's an entity and evidence problem. and it's already shaping how B2B buyers build shortlists.
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What I tested
I ran a straightforward experiment. I opened ChatGPT and entered this prompt:
*“Recommend three marketing operations consultancies that work with mid-market B2B companies.”*
This is a category query. the kind of question a VP of Marketing or a founder might ask when building a vendor shortlist. It's broad enough to surface patterns and specific enough to be useful.
I wanted to see who appeared, who did not, and what signals the model seemed to rely on.
A few caveats: AI outputs vary by model, session, and context. This is one experiment, not a controlled study. But the patterns were clear enough to be worth examining.
What I observed
Veriqo Studio was not mentioned. Neither were several firms I know personally. companies with strong track records, loyal clients, and genuine expertise.
This isn't a complaint. It's a useful diagnostic.
The firms that did appear were not necessarily larger or better known. But they had something in common: their positioning, claims, and evidence were structured in ways a machine could extract, compare, and cite.
The issue was not quality. The issue was machine-readable positioning.
What AI systems appear to reward
I reviewed the recommended firms and cross-referenced their web presence. Here is what stood out:
Clear category labels. Each firm had explicit language on core pages that matched how a buyer would describe the service. Not metaphors or clever taglines. Direct statements: who they serve, what they do, what category they operate in.
Specific, extractable claims. Concrete statements a machine can parse: “200+ mid-market B2B engagements” or “average 30% reduction in MQL-to-close time.” Vague language like “we deliver results” doesn't register.
Repeated phrasing across sources. The same positioning language appeared on their website, LinkedIn profiles, directory listings, and guest content. Consistency across surfaces reinforces entity identity.
Third-party corroboration. AI tools cross-reference multiple sources. A claim on your own site carries some weight. The same claim echoed on directories, podcasts, and industry profiles carries more.
Comparison-ready content. Structured case studies, process summaries, and FAQ-style answers. content formatted so a machine can extract it without interpretation.
Why this isn't a future trend
This is happening now.
Buyers are using AI tools to build shortlists before they ever visit your website. When someone asks ChatGPT or Perplexity “who should I hire for marketing ops,” the answer gets assembled from whatever the model can parse, compare, and trust.
Your analytics won't show these sessions. There is no referral, no click, no page view. The buyer arrives with a shortlist already formed. or they never arrive at all.
This isn't just an SEO problem. Traditional SEO focuses on ranking pages. AI tools do something different: they extract entities, compare claims, and assemble recommendations from structured evidence across the web.
AI visibility isn't a trick. It's an entity and evidence problem.
What this means if you already invest in SEO, AEO, or GEO
You aren't starting over. If you have invested in search visibility, structured content, or answer engine optimization, that work still matters.
What this experiment highlights is a refinement layer:
- Machine-readable clarity. Is your positioning expressed in direct, extractable language? Or does it require human interpretation of design cues and marketing copy? - Entity reinforcement. Does the same clear positioning appear consistently across your website, LinkedIn, directories, and third-party content? - Proof artifacts. Are your results, process descriptions, and expertise documented in formats a crawler can read? Or is your best evidence locked in slide decks and gated PDFs?
If your SEO and AEO foundations are solid, these refinements can be the difference between being found and being overlooked.
The 30-minute AI visibility audit
You don't need to hire anyone to run this diagnostic. Block 30 minutes and work through these steps:
1. Run three category prompts. Ask ChatGPT, Perplexity, and Claude to recommend firms in your category. Use buyer-style language, not industry jargon. Note who appears, who doesn't, and what language is cited.
2. Capture the exact phrasing AI uses. What claims does the model repeat? What proof does it reference? This tells you what “extractable evidence” looks like in your space.
3. Compare your positioning language. Open your homepage and about page. Does your language match how buyers ask the question. or does it require translation? Buyers ask “who can fix our lead handoff process,” not “revenue operations transformation partner.”
4. Audit your third-party presence. Check LinkedIn, industry directories, guest articles, and podcast bios. Is your positioning consistent across these surfaces? Are your category labels and claims the same everywhere?
5. Identify missing proof artifacts. Do you have public case studies with named outcomes? Process descriptions a machine can index? Or is your strongest evidence buried in conversations and proposals?
6. Rewrite one key page. Pick your homepage or services page. Rewrite the first 200 words using direct category language, specific claims, and clear statements of who you serve and what you deliver.
Common failure modes
A few patterns that consistently reduce AI visibility:
- Vague positioning that requires human interpretation (“innovative solutions for modern challenges”) - Generic “full-service” language that fits every firm and describes none of them - No third-party corroboration. claims exist only on your own website - Weak category naming. no clear answer to “what type of firm is this?” - Proof hidden in PDFs, slide decks, or gated downloads that crawlers can't access - Inconsistent entity identity across web properties
What I am doing about it
I am treating this as a project. Veriqo Studio’s work is real and documented. The gap isn't in quality. It's in how that quality is expressed for machine consumption.
In practice, that means:
- Rewriting core page positioning with direct category language and extractable claims - Restructuring engagement examples into public, structured proof artifacts - Building consistent entity signals across LinkedIn, directories, and third-party surfaces - Publishing content like this that demonstrates thinking in formats AI tools can parse
This is the same approach I would recommend to any B2B firm facing the same gap. Start with clarity. Build structured proof. Make it easy for both humans and machines to understand what you do and why it matters.
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Want a quick AI visibility readout?
I help teams audit how they show up across search, AI answers, and buyer-facing content systems. then fix the clarity gaps without bloated marketing plans.
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A question worth considering
Ask an AI tool to describe what your company does. Then compare the answer to what you actually want to be known for.
The gap between those two answers is your extractability gap. Closing it might be the most important marketing project you run this quarter.
What did AI get right. or wrong. about your category?
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*Educational content only. Observations are based on a single experiment conducted in February 2026 and may not reflect current AI model behavior. AI outputs vary by model, session, and context. Not legal, compliance, or guaranteed-outcome advice. Your results will depend on your specific market, content, and competitive landscape.*
Frequently Asked Questions
What is AI visibility? AI visibility refers to whether your company, expertise, and claims appear when buyers use AI tools like ChatGPT, Perplexity, or Google AI Overviews to research vendors or compare services. It depends on machine-readable positioning, entity signals, and structured evidence across the web.
How is AI visibility different from SEO? Traditional SEO focuses on ranking web pages in search results. AI visibility is about whether your entity. your company, your claims, your proof. gets extracted and cited when AI tools assemble recommendations. It requires clarity, consistency, and third-party corroboration, not just on-page optimization.
How do I test if my company appears in AI answers? Open ChatGPT, Perplexity, or Claude and ask a category query a buyer would use: “Recommend firms that do [what you do] for [your target market].” Run the query across multiple tools and note who appears, what language is cited, and what evidence is referenced.
What should B2B consultancies fix first? Start with category positioning language on your homepage and about page. Make sure a machine can extract a clear statement of what you do, who you serve, and what category you belong to. Then build public proof artifacts. case studies, process descriptions, and FAQ content. and ensure your positioning is consistent across third-party platforms.
Will this hurt my existing SEO or AEO work? No. Clear positioning, structured content, and specific claims improve both traditional search and AI visibility. They are complementary layers, not competing strategies.
Should I try to manipulate AI recommendations? No. The patterns I observed were not about gaming a system. They were about clarity, specificity, and structured evidence. Focus on making your real expertise legible to machines. That's a durable approach. Trying to trick specific models isn't.