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I am going to paste our brand positioning document below. Once you have read it, use the Peec MCP to pull our brand sentiment report broken down by AI model, and use get_chat on our lowest-scoring conversations to identify the topics and language patterns AI associates with our brand. Then do a structured comparison: for each positioning claim we make, check whether the sentiment data and retrieved topics support it, contradict it, or simply do not reflect it at all. Return a prioritized gap list showing: (1) attributes we claim but AI does not reflect, (2) topics where AI sentiment directly conflicts with our positioning, and (3) topics we own in our positioning but where we are invisible in AI search. [PASTE POSITIONING DOCUMENT HERE] Save the full gap analysis to Notion under [brand name] Positioning. Post the top 3 gaps to [Slack channel] for the messaging team.
Your brand positioning is what you want to be true. What AI says is what buyers actually hear when they ask about you. The gap between the two is not visible in a standard brand tracker or a share of voice report. It lives in the specific language AI uses to describe you, the attributes it emphasizes, and the ones it never mentions. This comparison surfaces that gap in structured, prioritized form: not a vague sense that the narrative is off, but a concrete list of the claims AI supports, the ones it contradicts, and the ones it ignores entirely.
That list is your PR brief, your content strategy, and your message architecture fix list in one document.
