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Audit report: how ChatGPT, Gemini, Claude, and Perplexity perceive and recommend your brand. What LLMs know about your business and how to change the narrative.
I conduct a systematic audit of your brand's visibility across major AI models (ChatGPT-4, Gemini 1.5, Claude 3.5, Perplexity). I test 100+ industry queries, document how LLMs describe your company, products, and services, benchmark against your competitors, and deliver a prioritized action plan for improving your position in AI-generated responses.
Report from 100+ query tests in ChatGPT, Gemini, Claude, and Perplexity — a complete map of how AI perceives your brand today.
Comparative analysis against Top 5 competitors — who is cited more frequently than you, what content and signals they use to achieve that position.
Identification of incorrect or outdated information about your company in LLM responses, with a recommendation on how to correct it.
GEO gap map: industry topics where your brand should be the cited authority but isn't — a ready list of content opportunities.
Prioritized action plan with difficulty estimates and potential impact on AI visibility for each recommended action.
I create a set of 100+ queries covering brand queries (company name), category queries (industry, services), informational queries, and comparison queries — tailored to your market.
I systematically test all queries in ChatGPT-4, Gemini 1.5, Claude 3.5 Sonnet, and Perplexity, document each response, and record your brand's mention frequency.
I analyze results vs. competitors, identify patterns in content and signals that cause more frequent citation, and document factual errors in brand descriptions.
I deliver a detailed PDF report with findings, root cause analysis, and a prioritized action list with ROI estimates for each GEO recommendation.
It depends on the model. Perplexity and SearchGPT have access to a live web index. GPT-4 and Claude have a training cutoff date (typically 6–12 months in the past). The audit shows what each model knows separately.
Direct correction of LLM data is impossible — but it can be fixed through external sources. Updating Wikidata entries, Wikipedia (if you qualify), industry media, and your own content influences future training cycles and RAG.
I recommend quarterly — models update, new versions have different knowledge and behaviors. For companies actively building GEO, monthly monitoring is standard practice.
Initiate protocol. Establish connection. Let's build something loud.