Diving into the rapidly evolving world of digital marketing, I had the privilege of sitting down with Dominic Jainy, a seasoned IT professional whose expertise in artificial intelligence and machine learning has positioned him as a leading voice in the realm of LLM (Large Language Model) rank tracking. With conversational AI platforms reshaping how consumers discover brands and make decisions, Dominic offers unparalleled insights into the tools and strategies that are helping businesses stay visible in this new search landscape. Our discussion touches on the transition from traditional SEO to AI-driven visibility, the unique challenges faced by enterprises and small businesses alike, and the innovative tools that are defining this space. We also explore the future of LLM visibility and how businesses can strategically adapt to this transformative shift.
How has the shift from traditional SEO to LLM visibility reshaped the way businesses approach their online presence, and can you share a story of a brand that successfully adapted?
That shift has been nothing short of a tectonic plate moving under the digital marketing landscape. Traditional SEO was all about keywords, backlinks, and ranking on a static list of results, but with LLMs, it’s about being part of a dynamic, conversational answer that might not even reference a website. Businesses now have to think about how they’re perceived and recommended by AI engines like ChatGPT or Gemini, which prioritize trusted data over traditional signals. I recall working with a local chain of dental clinics in a major city a couple of years ago. They were killing it on Google with top rankings for “best dentist near me,” but when AI chatbots started gaining traction, they noticed a drop in inquiries despite their SEO success. We used a tool similar to AI Rank Checker to track their visibility across over 20 AI engines, and the data showed they were barely mentioned in conversational responses. By optimizing their content for natural language queries and ensuring consistent brand mentions across trusted sources, they saw a 30% uptick in AI-driven recommendations within three months. That translated to real foot traffic, and they felt this visceral relief—like they’d dodged becoming invisible in a world they hadn’t even fully understood yet.
What specific challenges do large organizations face when tracking AI visibility compared to smaller businesses, and can you recount a case where compliance played a critical role?
Large organizations have a completely different beast to tame compared to smaller outfits. They’re dealing with massive data sets, multiple brands or product lines, and often a global footprint, which means tracking AI visibility isn’t just about being seen—it’s about ensuring accuracy and alignment with internal policies. Compliance and governance become huge hurdles because a single AI hallucination or incorrect recommendation can spiral into a PR disaster or legal issue. I worked with a multinational healthcare company that needed to ensure their AI mentions adhered to strict regulatory standards. Using a tool with features akin to Profound’s hallucination monitoring, we identified instances where an AI engine was inaccurately suggesting their product for unapproved uses. We had to set up a rigorous monitoring system, integrate it with their compliance dashboards, and work with their legal team to issue corrections to data sources feeding the AI. It was a tense process—imagine the weight of knowing one wrong answer could cost millions in fines—but by establishing these protocols, they not only safeguarded their reputation but also built a framework for proactive AI governance that gave their executives peace of mind.
Why do simple tools appeal to freelancers or small business owners, and can you share an example of how one leveraged such a tool to enhance their visibility?
Simplicity is gold for freelancers and small business owners because they often wear a dozen hats and don’t have the time or budget for complex platforms. Tools that offer straightforward checks, like basic ChatGPT visibility tracking, cut through the noise and give them actionable insights without a steep learning curve. It’s about getting a quick pulse on whether their brand is being recommended in AI conversations without drowning in data. I remember a freelance graphic designer who was struggling to get noticed in a saturated market. She started using a lightweight tool similar to ScrunchAI to see if her name popped up in AI responses for design queries. The tool showed she wasn’t being mentioned at all, so she focused on getting featured in niche design forums and local business directories that AI systems often pull from. Within a few weeks, she noticed her name appearing in basic recommendations, and soon after, she landed two high-value clients who mentioned finding her through a chatbot suggestion. That moment of seeing her hard work pay off was electric for her—it was like finally being heard in a crowded room.
How does combining AI visibility tracking with broader marketing tools benefit users, and can you walk me through a scenario where this integration proved valuable?
Combining AI visibility with broader marketing tools creates a synergy that standalone options can’t match. It’s about seeing the full picture—how your AI presence ties into website traffic, funnel conversions, and overall campaign performance. Instead of siloed data, you get a cohesive strategy where each piece informs the other, saving time and reducing guesswork. I worked with a mid-sized e-commerce brand that adopted a multi-purpose suite akin to XFunnel, which paired LLM tracking with funnel analytics. They noticed their AI visibility was low for key product queries, so we analyzed their sales funnel data within the same platform to see where customers were dropping off. By tweaking their content to better align with conversational queries and linking it to targeted landing pages, they saw AI mentions increase alongside a 15% bump in conversion rates over two months. The integration meant they didn’t have to jump between tools—they could adjust on the fly, and the team felt this seamless flow of insights was like having a co-pilot guiding their every move.
What potential do you see in newer, still-evolving tools in the LLM tracking space, and can you share an early adopter’s experience with such a platform?
Newer tools in the LLM tracking space are like raw diamonds—they might lack polish, but their potential is immense. They often bring fresh perspectives or niche focuses that bigger players haven’t tapped into yet, and as they grow, they can adapt quickly to user feedback. The risk is there, with limited features or engine support, but so is the opportunity for early adopters to shape the tool’s direction. I know a small tech startup that jumped on a beta version of a tool much like HallAI, which was still rough around the edges with only basic ChatGPT monitoring. They faced challenges like inconsistent UI updates and gaps in data, which was frustrating—imagine waiting for a report only to find half the metrics missing. But they stuck with it, providing feedback to the developers, and discovered an unexpected win: the tool’s early volatility analysis helped them spot fluctuations in AI answers before competitors did. They capitalized on this by rapidly updating their content, gaining a slight edge in visibility. That gritty, hands-on experience felt like being part of a garage-band project that just might hit the big time.
What trends are fueling the prediction that LLM visibility could rival SEO’s importance by 2026, and can you highlight an industry already feeling this shift?
The prediction that LLM visibility could match SEO’s importance by 2026 isn’t just hype—it’s rooted in how consumer behavior is evolving. People are moving away from traditional search to conversational AI for quick, curated answers, especially for local recommendations or product decisions. This isn’t a slow trickle; it’s a wave, driven by the millions of daily interactions on platforms like ChatGPT and Perplexity that bypass Google entirely. The retail industry is already deep in this shift—I’ve seen firsthand how e-commerce brands are scrambling to ensure they’re mentioned in AI-driven shopping suggestions. For instance, a fashion retailer I advised noticed that AI engines were recommending competitors for queries like “best summer dresses” despite their solid SEO rankings. By focusing on structured data and authentic reviews to boost their AI trustworthiness, they started appearing in chatbot responses, driving a noticeable spike in referral traffic. It’s a stark reminder that if you’re not in the AI conversation, you’re out of the customer’s mind, no matter how high you rank on a search engine results page.
How does a flexible pricing model impact small businesses or agencies compared to traditional subscriptions, and can you share a client’s success story with this approach?
A flexible pricing model, like a pay-as-you-go system where credits never expire, is a game-changer for small businesses and agencies. Unlike subscriptions that lock you into monthly fees regardless of usage, this approach lets users pay only for what they need, which is a lifeline for tight budgets or seasonal workloads. It also removes the pressure to “use it or lose it,” giving them freedom to experiment without financial guilt. I had a client, a boutique marketing agency, that switched to a tool with a credit-based system similar to AI Rank Checker. They were previously tied to a costly subscription they couldn’t justify during slow months, but with the new model, they saved nearly 40% in annual costs by only running checks for active campaigns. They redirected those savings into content optimization, which boosted their clients’ AI visibility scores significantly. Seeing their financial stress lift while still delivering results was like watching a weight come off their shoulders—they could finally focus on strategy over survival.
With LLMs increasingly influencing purchases and recommendations, how should businesses prioritize AI visibility in their marketing plans, and can you outline a step-by-step approach a business took to improve their presence?
AI visibility should be at the core of any modern marketing plan, not an afterthought. It’s no longer enough to dominate Google rankings; if you’re not in the AI conversation, you’re missing out on direct consumer trust and decision-making moments. Businesses need to treat this as a parallel track to SEO, allocating budget and resources to monitor and optimize their presence across conversational platforms. I worked with a local restaurant group that took this to heart. First, they used a comprehensive tool to track their visibility across multiple AI engines, discovering they were rarely recommended for casual dining queries. Next, they updated their online profiles with consistent, detailed information—think hours, menu highlights, and customer stories—to feed AI systems better data. Then, they encouraged authentic reviews on platforms AI often scrapes, which helped build credibility. Finally, they ran regular checks to tweak their content based on query trends. Within a few months, they saw a marked increase in chatbot mentions, leading to more reservations, especially from younger customers relying on AI for quick picks. That success felt like striking a match in the dark—they illuminated a path they didn’t even know existed.
What is your forecast for the future of LLM visibility and its impact on digital marketing over the next few years?
I’m incredibly bullish on LLM visibility becoming a cornerstone of digital marketing by the end of this decade. We’re already seeing AI engines redefine how trust and recommendations work, and as more consumers lean on these platforms for everything from product choices to local services, businesses will have no choice but to adapt or fade into obscurity. I predict we’ll see tools become even more granular, offering real-time sentiment analysis and predictive modeling for AI responses, which will push marketers to be hyper-responsive. The impact will be profound—imagine a world where your marketing isn’t just about ranking but about shaping an AI’s perception of your brand through every data point it touches. It’s both thrilling and a bit daunting to think about the speed of this change, and I’m curious to see how businesses will balance creativity with the precision these tools demand. What do you think—will marketers embrace this fully, or will there be resistance to such a paradigm shift?