Build a monthly scorecard of your brand's visibility in AI answers and compare it with last month

Build this month's scorecard for the visibility of my brand [brand name] and my site [domain] in AI answers. Market: [country], language: [language code, e.g. en]. My competitors: [up to 3 competitor brand names]. My fixed question set: [5 to 6 questions that do not name my brand, phrased the way a real customer would ask]. Last month's scorecard: [paste last month's table, or write "first month"]. This is read-only. Keep the question set identical every month, otherwise the months cannot be compared. If I gave no questions, propose 6 (2 of the "best [service] in [city]" type, 2 comparisons, 1 on price, 1 how-to), get my approval and say that the set will not change. 1. Credit check. Use seo_history to find queries run recently with these arguments. Call the reusable ones again with the same arguments, they return at no cost. Tell me how many new calls are needed. 2. Share in AI chats. Run seo_geo_tracker with brand=[brand name], brand_domain=[domain], the competitors and the question set, engines='chat_gpt,perplexity' and repeats=2. The tool runs at most 24 language model queries per call (questions × engines × repeats) and lowers the repeats itself when that limit is exceeded, so report prompts_used and calls from the reply to keep the months on the same scale. Take the brand mention rate, the number of answers citing my domain, the rate per engine, each competitor's rate and the most cited source domains. This tool does not return the answer text, only rates and source domains. 3. Google's AI answers. Run the 3 questions with the strongest commercial intent through seo_ai_overview: target=[domain], location=[country name in English, e.g. United Kingdom], language=[language code]. For each, record whether an AI answer appears, whether my domain is cited, and which domains are cited instead. Careful: the references list can come back empty while the sources sit only as links inside the ai_answer text; in that case do not trust target_cited, count the domains of the links in the text. ai_answer is cut at 1,200 characters, so write "not cited" as "not in the visible part", not as a certainty. 4. Overall mentions. Run seo_geo_citations with brand=[brand name] and take the mention counts in Google AI answers and ChatGPT. If my brand name is made of common words (for example two everyday words), this number can be inflated by unrelated text; say so and use this row for the trend, not as an exact figure. 5. Real visits. In my GA4 property [GA4 property], use ga4_report with dimension='source' to pull sessions and key events for the last 30 days and the previous 30 days. Add up chatgpt.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, claude.ai and any other source that clearly belongs to an AI assistant. Leave out paid traffic tagged with utm parameters (for example an ad source like chatgpt_ads), because that is not organic visibility. 6. Brand searches. In my Search Console property, use get_report with platform='search_console', breakdown='query', for the last 28 days and the previous 28 days, and add up impressions and clicks for queries that contain my brand name. Being mentioned in AI answers often lifts brand searches, so this row is an indirect signal. Format the output like this. First a four-sentence summary. Then the scorecard table: metric, this month, last month, change, note. Rows: mention rate in ChatGPT, mention rate in Perplexity, answers citing my domain, each competitor's mention rate, questions where Google's AI answer cites me, seo_geo_citations mention counts, sessions and key events from AI assistants, brand search impressions. Then a per-question table: question, mentioned in ChatGPT, mentioned in Perplexity, cited by Google, most cited competitor source. Then the 10 most cited source domains and how many of them are my site, a competitor's site or a third party. Decision rules: with two repeats, label any rate change below 10 points as "within noise" and do not read it as a real change. If this is the first month, leave the change column empty and record this month as the baseline. End with one recommendation for next month, and give the table in a form I can copy and paste back next month.