AI Brand Perception Audit (QA)

AI Brand Perception Audit (QA)

1. 01

   Define the client's core brand terms and competitor names for AI monitoring

   Compile a list of exact brand names, common misspellings, product lines, and key executives. Include top competitors to benchmark against.

2. 02

   Run a baseline query across major AI platforms

   Use a tool like LLM Pulse to capture how ChatGPT, Perplexity, Gemini, and Google AI Overviews currently describe the client. Record the raw responses and note any citations or sources referenced.

3. 03

   Score each response for accuracy, sentiment, and citation quality

   Create a simple rubric: 1-5 for factual correctness, 1-5 for sentiment (negative to positive), and note whether the AI cites the client's own site or third-party sources. Flag any hallucinations or outdated information.

4. 04

   Compare findings against the client's intended positioning and key messages

   Review the client's brand guidelines, value proposition, and recent PR or content. Identify gaps where the AI's portrayal diverges from the desired narrative.

5. 05

   Document the audit in a client-facing report

   Summarize the top issues, including specific examples of misrepresentation or missing citations. Prioritize fixes by potential impact on trust and conversion.

6. 06

   Recommend a remediation plan with content and citation-building actions

   Suggest creating or updating pages that directly answer common questions, earning mentions from authoritative sites, and monitoring changes over time. For crisis-level issues, consider a service like Reputation Rhino for suppression and positive content placement.

7. 07

   Schedule a re-audit in 30 to 60 days to track progress

   AI models update frequently, so a single audit is a snapshot. Regular re-checks show whether remediation efforts are shifting the AI's perception.