The rules of digital discoverability are undergoing a massive transformation. As search engines evolve into answer engines driven by large language models, the strategies that once dominated traditional search engine optimization are being challenged. In a recent episode, digital marketing veteran and ZeroRank founder Besmir Bregasi joined host Jared to discuss what truly influences whether brands get cited in ChatGPT, Google AI Overviews, and other LLM-driven search experiences.
Drawing from extensive visibility data across multiple brands, locations, prompts, and platforms, Bregasi offered a front-row seat to how modern search operates and where companies should focus their marketing efforts. Rather than relying solely on traditional on-page optimization, modern brands must navigate a complex ecosystem where off-site sentiment, fresh discussions, and multi-channel engagement dictate visibility.
Bregasi’s journey to founding ZeroRank is rooted in a nearly two-decade career in digital marketing. Beginning with search engine optimization nearly twenty years ago, he spent roughly fifteen years heavily focused on paid advertising and affiliate marketing before returning to organic visibility through answer engine optimization, commonly known as AEO. This long trajectory gave him a unique perspective on the current technological shift. He believes the transition toward AI-driven discovery closely mirrors earlier periods in digital marketing history when a nascent channel presented massive opportunities alongside relatively low competition. When traffic from large language models began accelerating, Bregasi recognized an opening early enough to justify going all in, ultimately leading to the creation of ZeroRank.
The Off-Site Signals Driving LLM Visibility
One of the most critical takeaways from ZeroRank’s extensive data analysis is the heavy weight placed on off-site activity. According to Bregasi, LLM visibility is fundamentally shaped by the places where real people discuss, cite, review, or react to a brand across the digital landscape.
This dynamic represents a distinct shift from traditional optimization tactics. While a company can exercise near-total control over the content published on its own domain, third-party discussions provide an entirely different caliber of signal precisely because they originate from sources the brand cannot manipulate. Among the primary off-site signals monitored by ZeroRank are authentic customer discussions, third-party reviews, industry reactions, and independent citations.
For site owners and digital marketers who traditionally pour the vast majority of their energy and resources into their own domains, this data signals a necessary pivot. While on-page work retains a foundational role in technical accessibility, the larger growth opportunity lies in generating enough external attention that large language models repeatedly encounter the brand across trusted third-party sources.
The Freshness Advantage and Multi-Channel Impact
Among all the variables observed in ZeroRank’s data, Bregasi emphasized freshness above almost everything else. In their empirical observations, recent and valuable content frequently carries significantly more weight and influence than a massive historical archive of older brand mentions.
Brands with extensive online histories often find themselves losing traction in AI search results when their historical material becomes dated or low quality. Conversely, fresh content that commands immediate user interaction sends a powerful, distinct signal to language models. To illustrate this pattern, Bregasi pointed to an experiment ZeroRank conducted on the social media platform X. By strategically deploying fresh material and observing how initial exposure triggered rapid user reactions, the platform naturally amplified the content’s reach. The broader lesson was not merely that brands should pay for promotional posts, but rather that the sequence matters: fresh material gains initial exposure, attracts immediate genuine interaction, and triggers wider secondary reach across the platform.
When evaluating which specific platforms carry the most weight in client data, Bregasi consistently returned to three primary channels: Reddit, X, and YouTube. Each platform functions differently, meaning that simply blanketing all three with a brand name yields far less impact than earning genuine, meaningful exposure within relevant communities.
Reddit remains a dominant citation source in ZeroRank’s dataset, despite fluctuations from its historical prominence. Factors concerning thread quality, community relevance, and historical depth dictate how influential a discussion becomes, making authentic participation far more valuable than overt promotional spam. Meanwhile, X excels at generating rapid, real-time information flow and immediate user interaction. YouTube represents another massive pillar of influence, particularly regarding its heavy integration into Google AI Overviews. Fresh video content that successfully attracts comments, likes, and active engagement shortly after publication appears uniquely positioned to capture LLM visibility, effectively serving as a powerful third-party anchor connecting a company to a specific topic.
Beyond user-generated platforms, niche editorial mentions continue to play a vital role. LLMs routinely ingest information from specialized blogs, independent news sources, and industry-specific websites. A citation from a highly relevant niche publication carries substantially more weight and contextual meaning than a random mention on an unrelated domain. Interestingly, while platforms like LinkedIn remain valuable for traditional professional networking and B2B marketing, ZeroRank’s client data has not shown it carrying the same heavy citation weight in LLM search results as Reddit, X, YouTube, and targeted niche editorial sites.
Connecting Traditional SEO to Modern Answer Engine Optimization
Despite the explosive rise of AI search, traditional SEO is far from obsolete. Bregasi views conventional search engines and traditional websites as a critical foundational information layer that LLM systems continue to draw from when formulating complex responses.
To conceptualize this relationship, Bregasi offered a rough mental model in which user-generated content and third-party discussions might account for a significant majority of the overall influence, while the remaining portion stems from traditional websites and conventional search structures. Rather than a rigid mathematical formula, this approximation highlights the deep overlap between standard search optimization and answer engine optimization. For site owners and publishers, abandoning SEO entirely to chase AI citations would be an extreme and counterproductive reaction. The two channels are deeply intertwined, even if off-site discussions have undeniably captured a greater share of visibility influence.
This interplay is particularly evident in local search and niche markets. In one compelling case study highlighted during the interview, a vacation rental company struggling against heavily entrenched competitors in conventional Google search managed to break through by shifting its strategy. Instead of fighting traditional on-page battles, the business focused intensely on localized conversations taking place in the target language and geographic community. By actively participating in local Reddit threads and regional discussions on X, the company successfully influenced how LLMs perceived its local relevance. This demonstrates that in lightly contested or geographically specific markets, a relatively modest amount of fresh, localized activity can yield a disproportionately large impact on AI-driven visibility.
The Technical Foundation and Measurement Challenges
While off-site signals drive growth, a solid on-site foundation remains an essential baseline requirement. Bregasi described technical on-site optimization not as the primary growth lever, but as a prerequisite that ensures LLM systems can easily access, crawl, and interpret information. Ensuring clean site architecture, clear metadata, and seamless technical accessibility removes friction for automated crawlers. However, once those technical baselines are established, pouring endless hours into minor on-page tweaks often yields diminishing returns compared to earning valuable mentions elsewhere. Furthermore, because off-site signals are inherently difficult for a business to manipulate directly, third-party references serve as a trusted quality signal for automated systems determining brand authority.
Tracking visibility across LLM-driven experiences introduces a unique logistical hurdle: personalization. Because two users can enter identical prompts and receive entirely different responses influenced by geographic location, browsing history, device type, and account variables, treating any single response as definitive is unreliable. ZeroRank solves this measurement challenge through scale. By running continuous queries across massive numbers of accounts, locations, and variable combinations, the platform identifies recurring patterns and establishes an average visibility baseline. This data-driven approach allows brands to implement strategic changes, repeat measurements, and observe whether their baseline visibility trends upward, transforming AI tracking into a reliable framework for ongoing experimentation.
Rapid Product Development and Strategic Customer Acquisition
The journey of building ZeroRank itself reflects an aggressive, execution-driven timeline. After committing to the business opportunity, Bregasi and his co-founder Kelvin Lobanaj established a strict three-month deadline to develop and launch the initial version of the software. Lobanaj, stepping away from a lucrative role as chief technology officer at a prominent cryptocurrency firm, dedicated himself to the project full-time while Bregasi redirected his focus from previous ventures.
To rapidly validate the software and gather critical user feedback, ZeroRank leveraged AppSumo as an early customer acquisition channel. The response exceeded expectations, propelling the platform past one thousand users in rapid succession, establishing it as one of AppSumo’s top-selling products during its launch month, and securing stellar reviews. However, Bregasi approaches such aggressive launch strategies with pragmatic realism, noting the inherent tradeoffs and financial margins involved in heavy discount campaigns. Rather than treating the AppSumo campaign as an immediate profit center, ZeroRank positioned the pricing around break-even, absorbing minor customer acquisition costs as a strategic marketing investment. The true payoff manifested in rapid user acquisition, invaluable product feedback, heightened market awareness, and an organic referral engine that ultimately fuels recurring customer growth.
In just over a year of operation—with its major customer acquisition push beginning only months prior—ZeroRank successfully scaled to mid-six figures in annual recurring revenue. As the company enters its next growth phase, Bregasi plans to diversify acquisition channels through traditional SEO, targeted paid advertising, podcast appearances, editorial outreach, case studies, and structured affiliate partnerships. Additionally, the company has implemented creative referral incentives, such as offering free AI credits to existing users who successfully invite peers to the platform.
Ultimately, the evolving landscape of AI search underscores that brand visibility is no longer constrained by traditional on-page optimization alone. While a technically sound and structured website remains necessary, the primary catalysts for LLM citations originate from authentic conversations, active community participation, and fresh engagement happening across the broader digital ecosystem. For publishers, marketers, and business owners navigating this transition, treating AI visibility as an ongoing, data-driven experiment is essential for long-term success.
