Brand Visibility in the Age of LLMs: Navla’s Answer
Talk around generative AI is growing louder by the day. The use of Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity is skyrocketing—and with them, a radical shift is underway in how people search for information. These tools are now woven into everyday life, both personal and professional. Users increasingly rely on LLMs to ask questions, compare options, evaluate products, explore solutions, make decisions, and even get support on complex tasks. LLMs have effectively become a new, crucial touchpoint in the customer journey—complementing (and in many cases replacing) traditional search engines.
This evolution makes one thing clear: brands must begin to assess their visibility within LLMs. It’s no longer just about being discoverable through traditional channels, but about understanding how your brand appears in AI-generated answers—and how this influences site traffic, brand perception, and ultimately, decision-making. Yes, Google remains the top driver of website traffic. But for years now, we’ve seen a rise in zero-click searches, where the user finds the information they need without ever visiting a website. With LLMs, this behavior is set to increase exponentially.
So what does that mean for brand visibility?
It means visibility can no longer be measured solely in terms of clicks and visits. What matters today is being part of the answer—because that’s where opinions are shaped, knowledge is built, and increasingly, decisions are made. In other words, brands need to become the source. This new context offered by LLMs opens up a strategic frontier: if a user receives a complete, trusted answer from an AI model, having your brand present within that response can be a powerful competitive advantage—even if it doesn’t generate direct traffic. Being visible within LLM responses, in a consistent and credible way, means entering the decision-making process before any comparison or active search even begins.
To help brands navigate and measure this shift, the Navla team has developed an advanced dashboard designed to systematically analyze LLM behavior in response to strategic brand-related questions.
Our goal is to answer three essential questions:
1. How often is the brand mentioned, and how is it positioned in relation to key prompts?
2. How does the brand rank compared to its competitors?
3. What sources are LLMs referencing to generate their responses?
Our Approach
While generative language models are reshaping how users search for, discover, and engage with information online, there’s still a lack of clarity around how—and why—brands appear in LLM-generated responses. Unlike search engines, LLMs don’t yet provide visibility into how often a brand is mentioned or how it’s positioned across different prompts. There’s no equivalent to tools like Google Search Console that offer this kind of transparency. At Navla, we saw this gap not as a limitation, but as an opportunity. That’s why we developed a structured and innovative approach to map brand presence within LLM-generated content—one that turns opaque systems into actionable insights.
Our first goal? To define a new generation of strategic KPIs and help brands build a tangible, long-term competitive advantage. Using a methodology developed by our Search and Data teams, we identify the most common and relevant user queries related to a client’s business—real questions LLMs are likely to receive. We then programmatically submit these prompts to the leading LLMs, such as ChatGPT, Gemini, and Perplexity, and analyze the responses at scale. All the resulting data is funneled into an automated workflow that structures and centralizes the information into clean, queryable databases—fully integrated into customizable dashboards. The interface we designed is both intuitive and in-depth, offering clear visibility into how your brand is positioned in AI-generated conversations, and how it stacks up against competitors.
New KPIs and Competitive Analysis
Our approach goes beyond data collection—it paves the way for a new generation of KPIs designed specifically to measure brand visibility in the AI landscape.
Two indicators are particularly impactful:
- The number of brand mentions across LLM responses to key user queries;
- The average positioning of your brand relative to competitors, broken down by question and model.
These metrics offer a radically new perspective.
They don’t just show if your brand is mentioned—they show how relevant it is and where it stands within the hierarchy of AI-generated responses. Tracking this over time enables truly data-driven visibility strategies, helping optimize content and digital assets, while positioning your brand within the conversations that actually drive customer decisions. One of the most powerful aspects of our methodology is the ability to run detailed comparative analysis between your brand and direct competitors. For each query, we’re able to assess not only whether your brand appears, but which other brands are present—and how your positioning compares. This unlocks a new form of competitive benchmarking—not based on traffic or market share, but on presence within AI-generated content, at the exact moments when users are researching, evaluating, and deciding.
Understanding whether your brand is perceived as a leader, an alternative, or not mentioned at all helps uncover strategic opportunities: areas to improve, new topics to own, and spaces where competitors are gaining ground. In a landscape where attention is shifting from traditional search to AI dialogue, being part of the first answers—and being better positioned than your competitors—becomes a measurable, mission-critical goal for holistic digital visibility.
Understanding the Sources: The Real Engine Behind LLM Responses
A key component of our analysis is mapping the sources that LLMs rely on to generate their responses. In addition to tracking brand presence and positioning, we identify which websites, publishers, and platforms are most frequently referenced as the informational backbone of AI-generated content. This level of insight is critical. It helps uncover where LLMs draw their knowledge from, which domains hold the most influence on specific topics, and which sources are actively shaping your brand’s visibility—or invisibility—within AI conversations. This is where a new strategic KPI comes into play: source analysis. Understanding which sources are most frequently cited not only allows brands to monitor their exposure, but also to take targeted action—whether by strengthening relationships with high-authority domains, optimizing content on influential platforms, or filling gaps in underrepresented areas.
From Visibility to Action: Turning Insights into Strategic Moves
With our solution, companies can not only monitor their visibility within LLMs, but—more importantly—access actionable, relevant insights for marketing, communication, and digital strategy teams. It’s not just about knowing if your brand is mentioned—it’s about understanding how, where, and in what competitive context. You might discover, for instance, that a key competitor dominates responses on a strategic topic, or uncover frequent user questions that no brand has addressed yet. The data, grounded in real evidence, becomes the foundation for targeted strategic takeaways—fueling action plans that may include:
- Improving visibility on authoritative domains through Link Building or Digital PR
- Activating editorial or marketing partnerships
- Optimizing on-site content
- Identifying new or emerging competitors
In a world where visibility is no longer driven solely by clicks—but by the credibility assigned by AI models—these insights offer a tangible lever to build a real competitive edge.
What Makes Our Solution Stand Out
In today’s landscape, many solutions claim to help brands understand their positioning within answer engines. But the real differentiator lies in the depth of analysis and the ability to turn data into strategic action. Most tools on the market focus on offering a high-level overview of brand visibility compared to competitors. While useful, these features often provide a static snapshot of the context—lacking real control over the data or the ability to tailor the analysis to the client’s specific goals.
Our solution is designed to go much further. We don’t just observe what happens—we start from the most important input: the user’s question. We directly query the most popular generative models with strategically crafted prompts, then collect and structure each response. We analyze not only which brands are mentioned, but also which sources the LLMs rely on to generate their answers—a crucial element in understanding perceived credibility and authority. All this data is delivered through a fully customizable dashboard built in Looker Studio, designed for intuitive and detailed exploration. Most importantly, these insights become strategic and actionable, providing brands with the tools to understand:
- Why they appear (or don’t),
- In relation to whom,
- And what to do next to improve visibility.
Conclusion
In an ecosystem where artificial intelligence is becoming a key touchpoint between supply and demand, being visible within LLM responses means earning authority, trust, and attention—right where and when users are actively seeking information. Today, it’s no longer just about showing up on Google. It’s about being part of the answer. With our solution, you gain the tools to monitor where and how your brand is mentioned, compare your presence to competitors, and take focused action to improve your positioning across this new generation of AI-driven touchpoints.