We build the prompt set in three stages. First, we map the client's category and service or product lines to the questions their target audience asks at each stage of the buying journey: awareness (what is X), consideration (best X for Y), and decision (X agency or provider near me or in my industry). Second, we cross-reference those question patterns against the actual language used in search queries using Semrush keyword data and People Also Ask clusters. Third, we adapt the prompts to the conversational format that AI models receive, which differs from keyword-format search queries. The result is a 50-prompt set that mirrors real customer intent across the full funnel.

We run the same 50-prompt set at the start of every month across ChatGPT, Perplexity, Gemini, and Google AI Overviews. We record every citation, mention, and recommendation for the client and their top 3 competitors. The monthly report shows: current Share of Answer percentage, delta from previous month, delta from baseline, competitor citation frequency by platform, and which specific prompts moved in or out of the client's favor. We include real screenshots of AI responses so the data is verifiable, not estimated.

Measure establishes where you are before any work begins. We run the full prompt set, document the Share of Answer baseline, map competitor citation patterns, and identify the specific structural reasons your brand is absent from the responses where it should appear. Build implements the fixes: schema overhaul, entity disambiguation, content extractability rewrites, AI crawler access validation, and quick-win citable content assets. Grow compounds the foundation: ongoing citable content production, digital PR for citation building, monthly measurement, and schema iteration as AI model behavior evolves. Every engagement starts with Measure regardless of scope.

Incorrect AI brand mentions have three root causes: outdated content on your own site that AI crawlers indexed, conflicting entity information across multiple sources (your LinkedIn says one thing, your website says another, a directory says a third), or third-party sources citing inaccurate information that AI models weighted heavily. The fix sequence is: audit all entity references for consistency, update your own content to be unambiguous and current, add explicit correction schema where applicable, and for third-party source errors, pursue direct correction with the source publisher or submit structured feedback where AI platforms provide that mechanism. Results take 30 to 90 days depending on how frequently the relevant AI model re-indexes your content.

The core stack is Semrush for keyword and entity research, Profound or Peec for AI citation tracking across multiple models, Google Search Console for AI Overview appearance data, and manual prompt testing with documented screenshots for verification. We do not rely on any single tool because no tool currently covers all four major AI surfaces (ChatGPT, Perplexity, Gemini, AI Overviews) with equal accuracy. The manual prompt layer is what makes our measurement verifiable rather than estimated.

Brands without a physical location have fewer local entity signals to build on but stronger opportunity in category-level and expertise-level citation. The strategy focuses on building topical authority (consistent, well-structured content in your specific domain), third-party mentions in publications that AI models treat as authoritative, and explicit schema declarations of your service areas and expertise. For fully remote or digital businesses, the entity disambiguation work focuses on what you do and for whom rather than where you are located.