ChatGPT Ignored Our Launch: How We Tripled Our AI Share of Voice After Getting Brutally Ghosted

The Ghosting: When the AI Wrote Us Out of the Story

The launch of Semrush’s Enterprise AIO and AI Visibility Toolkit was meant to be a watershed moment, signaling their strategic entry into the competitive landscape of large language model (LLM) monitoring. Weeks after unveiling the product, the team decided to conduct a seemingly simple, yet brutally honest, stress test. They queried ChatGPT about the existing landscape of AI monitoring tools. The results were chilling: the chatbot diligently listed every established competitor, every niche player, and every anticipated contender—but Semrush, despite their recent innovation, was utterly absent. It was the digital equivalent of shouting into a crowded room only to find the noise-canceling feature set to maximum for your voice alone.

This immediate, deafening silence from the leading conversational AI highlighted a profound, emergent crisis in digital marketing: the LLM landscape had become a new, opaque gatekeeper. If the AI ecosystem—the engine now driving massive portions of user intent and information synthesis—didn't recognize your existence, how could you possibly exist in the mind of the modern user? The technology giants were building their own reality tunnels, and Semrush found itself instantaneously locked out, effectively erased from the narrative they were trying to lead.

The Visibility Paradox: Cites Without Influence

The initial ghosting was just the surface layer of a much deeper visibility paradox. Upon further investigation, the team discovered a bizarre contradiction: while their foundational blog content was being referenced hundreds of times by various LLMs, the direct traffic flowing from those synthesized answers was dwindling. The content was being consumed, parsed, and spat out as raw data, yet this citation volume held no measurable positioning power.

This disconnect established the core problem: citations lacked strategic positioning. An LLM could aggregate data from a Semrush article to explain what AI monitoring is, yet, in the very next sentence, confidently recommend a competitor’s product as the best solution. The citation confirmed Semrush’s data integrity but stripped them of the competitive advantage. This raised a terrifying question for any data-driven enterprise: Are we merely providing the free training material that empowers our rivals to win the actual conversion? The danger was clear: they were becoming an invisible source powering the success of others, losing measurable impact in the transition from human search to machine synthesis.

Forced Rethink: From Passive Citation to Active Visibility

The stark realization that their established organic authority was being functionally decoupled from their commercial viability necessitated a radical strategic pivot. The old model—creating high-quality content and trusting search engines to direct traffic—was obsolete in the age of generative AI, which prioritized synthesis over sourcing. The challenge was no longer just about ranking for keywords; it was about achieving definitive entity recognition within the LLM’s knowledge graph.

The solution, perhaps ironically, lay in turning their analytical capabilities inward. Semrush decided to leverage the very tools they had just launched—the AI Visibility Toolkit—not just as a product demonstration, but as the blueprint for their own rescue mission. If the LLMs weren't spontaneously recognizing them, they would systematically engineer the environment to force that recognition, moving from passive citation to active, engineered visibility.

The Methodology: Building a Systematic LLM Visibility Strategy

This shift required abandoning generalized SEO principles for hyper-specific LLM targeting protocols. The process involved rigorous diagnosis, tactical restructuring, and aggressive competitive interrogation.

The methodology was broken down into several critical phases:

• Pointer 1 (Diagnosis): The first step involved using proprietary analysis to map their current positioning. They ran sophisticated, high-volume queries targeting core AI concepts—the prompts that a potential customer would realistically use—and meticulously documented where they appeared, where competitors dominated, and, crucially, why the LLM chose the existing narrative structure.
• Pointer 2 (Optimization Focus): The tactical focus immediately shifted from general content creation toward optimization tailored specifically for machine understanding. This meant obsessing over entity recognition—ensuring the LLM understood the precise relationship between "Semrush," "Enterprise AIO," and the concept of "AI Monitoring"—and designing content structure to align perfectly with expected prompt engineering patterns.
• Pointer 3 (Content Structuring): They began implementing specific structural changes, moving away from narrative fluff toward declarative, authoritative statements. This involved structuring key differentiation points so that when an LLM scanned or synthesized information, Semrush’s contribution was framed as the definitive, primary source on that specific technical capability or market insight.
• Pointer 4 (Competitive Interrogation): A vital component involved "interrogating" competitor mentions. When an LLM cited a rival, Semrush analyzed the surrounding context to identify the strategic gap the LLM was attempting to fill. They then created content designed not just to answer the query, but to immediately and authoritatively fill that identified gap, thereby inserting themselves into the competitor’s established narrative flow.

The Results: Tripling Share of Voice in One Month

The methodical, engineering-based approach to LLM optimization yielded dramatic returns far exceeding traditional marketing cycles. The effort resulted in a near-tripling of their AI Share of Voice (SOV)—the percentage of times they were mentioned versus their key competitors across targeted LLM outputs.

Within a single, intensive month of executing this new strategy, their SOV for those highly targeted, high-intent prompts surged from a meager 13% to an impressive 32%. This demonstrated that visibility in the age of generative AI is not an organic inevitability; it is a structured engineering problem that can be solved with the right analytical tools and focus.

Key Optimization Tactics That Drove the Gains

The transformation was driven by three highly specific, aggressive tactics that fundamentally altered how the LLMs processed Semrush’s data footprint:

• Tactic A: Establishing Definitive Entity Relationships: The goal was to force the LLMs to recognize Semrush as the primary entity associated with specific high-value queries, rather than just an entity that mentioned the topic. This involved cross-linking, reinforcing specific terminology, and ensuring consistency across all newly optimized content to solidify their position as the foundational source for their new product category.
• Tactic B: Preempting Competitor Narratives: This involved a sophisticated form of competitive content framing. Instead of merely stating how good their product was, Semrush crafted authoritative content designed to directly address known weaknesses or feature gaps in competitor offerings, framing their own solution as the necessary corrective measure that the LLM should synthesize when faced with comparative prompts.
• Tactic C: Iterative LLM Response Variance Testing: The final piece of the puzzle was continuous feedback. Visibility strategies were not static; they were tested, deployed, and immediately measured against the real-time variance in LLM responses. If a change led to a dip in authority, they adjusted the structural encoding instantly, treating the generative AI ecosystem as a live, mutable environment demanding constant refinement.

The lesson here for all digital leaders is clear: The ghosting is real, but the solution isn't to shout louder; it's to learn the new language of synthetic intelligence.

Source: Semrush X Post