Interview with Jordan Parkes, Founder & AI Search Visibility Expert, ZeroClick Labs

This interview is with Jordan Parkes, Founder & AI Search Visibility Expert, ZeroClick Labs.

For readers meeting you for the first time, how do you describe what you do as the Founder & AI Search Visibility Expert at ZeroClick Labs and the kinds of problems you solve for clients?

I’m Jordan Parkes, the founder of ZeroClick Labs, an AI-first digital marketing agency dedicated to helping brands understand whether they are showing up in AI search, why they are or are not being recommended, and what to do about it.

I came to this after more than 15 years in SEO, so I’ve spent a long time helping companies become visible on Google. What has changed is where those searches are happening and how the answers are formed. People are now asking ChatGPT and Google’s AI Mode for advice before they ever visit a website. They ask who to trust, which products to compare, what solutions exist, and which companies are worth shortlisting.

The problem is that many brands have no real view of how they appear in those answers. They do not know if they are being mentioned, whether the information is accurate, or if competitors are being positioned as the obvious choice.

That is where our work starts. At ZeroClick Labs, we audit how AI platforms talk about a brand, identify missing trust signals, and then help build the content, authority, and third-party visibility needed to become a stronger source.

The goal is not to game AI systems. The goal is to make a brand easier to understand, easier to cite, and harder to leave out when buyers ask important questions.

What pivotal moment led you to launch ZeroClick Labs and focus on AI visibility, and how has that decision shaped the frameworks you use today?

The turning point was realizing that strong SEO did not automatically translate into strong AI visibility.

I was looking at brands that had done a lot of things right. They had rankings, solid content, backlinks, and proper technical foundations. But when you asked AI platforms the same questions their buyers were asking, these brands were often missing. In some cases, the AI was recommending competitors that would not have been serious threats in traditional search.

That forced me to rethink what visibility actually means now. It is no longer just about ranking well. It is about the whole conversation surrounding your brand, and the difficult part is that many of those conversations are now happening inside private AI chats.

That shaped how I work today. I focus on three practical questions:

1. Can AI systems understand and use your content?
2. Does your brand have real authority?
3. Are you showing up in the places AI tools are likely to reference when forming an answer?

At ZeroClick Labs, we are not chasing tricks or trying to reverse-engineer every model update. We are helping brands become clearer, more credible, and more visible across the places buyers and AI systems both rely on.

When you run an AI visibility audit, what are the first three signals you check across answer engines like Gemini and Perplexity, and why do those matter most?

1. The first thing I check is whether the brand shows up for the questions buyers are actually asking. Not branded searches — those are easy. I mean category-level and problem-led questions like “best tools for X,” “how do I solve Y,” or “which companies are leading in Z.” If you only appear when someone already knows your name, you are not really visible in AI search.

2. The second signal is how the brand is being described. Is the answer accurate? Is the positioning clear? Is the AI connecting the brand to the right services, products, markets, and use cases? This matters because AI answers can quietly shape perception. A weak or outdated description can be just as damaging as being left out.

3. The third signal is what sources are influencing the answer. I want to see where the citations are coming from, which competitors are being referenced, and whether the AI is leaning on your own content, third-party reviews, media mentions, comparison pages, forums, or industry sources. That tells us what the model seems to trust and where the brand needs to strengthen its footprint.

Those three checks give you a fast read on the real problem: Are you missing from the conversation? Are you being misunderstood? Or are competitors simply giving AI systems more to work with? Once you know that, the strategy becomes much clearer.

You’ve emphasized off-domain authority—walk us through your step-by-step playbook for earning LLM-trusted signals (e.g., Reddit, industry directories, niche editorials) in a way that’s authentic and sustainable.

The first rule is to stop treating off-domain authority like old-school link building. That playbook is tired. Thin guest posts, fake mentions, and directory spam might create activity, but they do not create trust.

Our process is more deliberate.

1. We map where trust already exists. That includes Reddit threads, niche forums, review sites, industry directories, comparison pages, podcasts, newsletters, analyst content, and specialist publications. I want to know where real buyers go when they are trying to make sense of the market.

2. We benchmark the brand against competitors in those places. Who gets mentioned? Who gets recommended? What language do people use? Which sources keep appearing in AI answers? This usually exposes the uncomfortable truth very quickly. A brand may look strong on its own website, but be almost invisible everywhere else.

3. We fix the obvious credibility gaps. That means improving directory profiles, cleaning up inconsistent descriptions, strengthening review presence, updating third-party listings, and making sure the brand is represented accurately where people are already researching.

4. We create assets worth citing. This is not generic thought leadership—it’s useful, specific material: comparison pages, original data, category explainers, expert commentary, customer proof, and clear answers to buying questions. AI tools need evidence. Editors, community members, and buyers do too.

5. We engage in communities without trying to manipulate them. Reddit is a great example. You cannot fake your way into trust there. You show up, answer properly, disclose who you are when relevant, and contribute like a human being. If your strategy depends on pretending to be a customer, it is already broken.

6. We measure whether those signals are starting to change the answers. Are AI tools citing better sources? Is the brand being described more accurately? Is it appearing in more category-level recommendations? Are competitors losing ground? Over time, the goal is to make the brand easier to trust, easier to cite, and much harder to leave out.

For publishers and brands who want to be cited inside Perplexity Search and Gemini/AI Overviews, what specific content patterns and structured data elements have you seen move the needle fastest?

The fastest wins usually come from making important pages extremely clear.

AI tools need to understand what the page is about, who it is for, what question it answers, and why the source should be trusted. The content patterns I see work best include:

• Direct answer sections
• Comparison pages
• Buyer guides
• Original data
• Expert explainers
• Clean question-and-answer formats

Specific content elements that give AI systems something concrete to work with include:

• Specific use cases
• Pros and cons
• Limitations
• Pricing context
• Clear definitions

For publishers, focus on making trust obvious on the page, including:

• Clear expert bylines
• Author bios that show real credentials
• Visible sourcing
• Recent update dates
• Topic hubs that connect related articles

For brands, focus on:

• Strong product or service pages
• Comparison content
• Customer proof
• Third-party references
• Pages that answer the questions sales teams hear every day

Structured data helps, but it will not save weak content. I usually check the basics first:

• Organization
• Person
• Article

Then I add well-structured LocalBusiness schemas where appropriate and FAQPage where it genuinely fits.

In a zero-click world, what KPIs best capture brand presence and recommendation share inside AI answers, and how do you attribute that visibility back to revenue or pipeline?

In my work, the core KPIs for AI visibility are:

• AI share of voice
• brand mentions in relevant AI responses
• citation frequency
• sentiment

I want to know how often the brand appears for important buyer questions, whether it is framed as a serious option, and whether the description is accurate. High visibility is not always good visibility. If an AI tool mentions you but gets your positioning, pricing, or product wrong, that is a problem.

For revenue, you have to separate hard evidence from directional signals.

Hard evidence includes:

• AI referral traffic
• conversions from those visits
• cited URLs
• self-reported discovery from leads who say they found you through ChatGPT or AI overviews

Directional signals include:

• branded search lift
• direct traffic to high-intent pages
• demo requests
• pipeline movement after visibility improves

The key is not to pretend you can perfectly attribute every zero-click answer. You can’t. I would rather report a clear trend with honest confidence levels than give a client a made-up revenue number that looks impressive but falls apart under scrutiny.

From your perspective running an AI-focused agency, what agentic workflows and tool stack have proven most effective for monitoring, diagnosing, and improving AI answer share at scale?

From my perspective, the most effective setup is a mix of AI visibility tracking and agent-assisted analysis behind the scenes.

We use tools like Peec AI to monitor how often a brand shows up across priority prompts, which competitors are being mentioned, what sources are being cited, and how answers change over time. That gives us the starting point, but the dashboard itself is only part of the workflow.

Where agents have become especially useful is in the analysis that follows. We use them to:

• pull visibility data;
• group prompts by buyer-journey stage;
• compare how a brand is being described versus competitors;
• run sentiment analysis;
• flag patterns that would take much longer to find manually, such as weak brand descriptions, recurring citation sources, missing product positioning, outdated information, or prompts where competitors consistently appear instead.

For example, if a client is not showing up in “best X for Y” prompts, we can use agents to review the answers, identify which competitors are being favored, analyze the cited sources, and summarize what seems to be influencing the result. From there, we can decide whether the fix is:

1. a content update;
2. a new comparison article;
3. third-party validation; or
4. PR outreach.

We also use agents to speed up recurring tasks like turning findings into content briefs, checking whether key brand messages are reflected accurately, mapping gaps across different prompt categories, and monitoring shifts in sentiment or positioning over time.

At scale, the workflow that works best is using agents to make the repetitive analysis faster and more consistent, while still relying on human judgment for strategy and execution.

Could you share a recent anonymized case where a client went from invisible to frequently cited in Gemini or Perplexity—what levers made the biggest difference and what surprised you in the process?

A recent SaaS client had a strong product but very little presence in AI answers. Competitors were being recommended more often, even when the client was a better fit for the query.

What moved the needle most was one high-quality roundup we built around the exact questions buyers were asking. We made it genuinely useful, clear, and well-structured, and more helpful than the existing sources AI platforms had available. That single piece became the top-cited source across our prompt set, and it pushed the brand into top recommendations in AI Overviews and ChatGPT.

The article is still referenced as the top source months after we published it.

What surprised me was that AI platforms did not always mention the brand directly, even when they used the article as a source. That was a reminder that authority in AI search is not one-dimensional. A single great asset can move the needle, but the real gains come when the brand, the content, and the broader web presence all reinforce each other.

Looking 12 months ahead, what strategic bet are you making about Gemini, Perplexity Search, and AI-driven discovery that most marketers are underestimating—and what should they start doing now to be ready?

My bet is that AI chats will become the place where purchases start and end.

I’m already seeing the first half of that. People are asking ChatGPT questions like, “Which tools should I compare?” “Who is best for this use case?” “What should I watch out for?” “Which company would you recommend?” This is the start of the purchase journey moving into AI answers.

I think the logical next step is the end of the journey moving there too. In e-commerce especially, AI shopping experiences will make it easier to research, compare, and buy without following the old path of search results, website visits, product pages, and checkouts. The AI agent will be the guide, the filter, and eventually the transaction point.

This changes the stakes for marketers. You are no longer just trying to win a click. You are trying to become the brand an AI system understands, trusts, and is willing to recommend when a buyer is ready to decide.

The move now is to clean up your content, strengthen your presence in credible third-party sources, create useful comparison and decision-stage assets, and track how your brand appears across the questions buyers are asking.

My advice is simple: Do not wait until this becomes easy to measure. By then, the companies that acted early will already be the ones getting recommended.

Thanks for sharing your knowledge and expertise. Is there anything else you'd like to add?

The main thing I’d add is that AI visibility is no longer a future problem; it is already shaping how people research, compare, and choose brands.

Many companies are waiting for perfect data. I think that’s a mistake. The brands that win will be the ones that have already started learning and monitoring how they appear, where they are trusted, where they are missing, and what AI systems are using to form their answers.

Just as companies gained a strong advantage by mastering search engines in the early 2000s, brands that master AI visibility will similarly benefit now.

If you want to go deeper into how the digital marketing landscape is evolving, we publish original research and analysis on AI search over at our Lab. And if you prefer the highlights, our bi-weekly newsletter distills our latest findings and predictions into something you can actually act on.