Monitor prompts across OpenAI, Perplexity, and ChatGPT web actor, log results to Google Sheets, and run sentiment and brand-hierarchy analysis to produce an auditable brand-visibility report.
This AI agent audits your brand's visibility across multiple AI models and logs findings to Google Sheets. It sends the same prompt to OpenAI, Perplexity, and optionally a ChatGPT web actor, normalizes responses, and extracts citations. An LLM-powered sentiment pass categorizes polarity, emotion, and brand hierarchy, creating a repeatable AI-visibility report for marketing and comms teams.
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Send the same prompt to OpenAI, Perplexity, and optional ChatGPT web actor.
Normalize responses into a unified schema.
Extract and attach citations where available.
Run sentiment and emotion analysis to classify responses.
Identify and map the brand hierarchy across responses.
Append the consolidated record to the Output Google Sheet.
before → Inconsistent brand mentions across models; scattered data with no normalization; manual, error-prone log processes in sheets; missing or inconsistent citations; uncertain sentiment and brand-hierarchy signals. after → Consistent brand mentions across tools; unified, normalized data with citations; automated logging to Google Sheets; clear polarity, emotion, and brand hierarchy; auditable, repeatable AI visibility reports.
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A Manual Trigger starts the AI agent; prompts are read from Google Sheets (or a manual input path).
The same prompt is sent to OpenAI (API), Perplexity (API), and optionally the ChatGPT web actor.
Responses are normalized into a unified schema, analyzed for sentiment and brand hierarchy, and appended to the Output sheet.
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Scenario: A marketing team runs a weekly audit of brand visibility across OpenAI and Perplexity with 20 prompts. Time: about 30 minutes. Outcome: a Google Sheet with per-prompt LLM, response, brand mentioned flag, brand hierarchy, polarity, emotion, and sources.
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Needs consistent, auditable data on how the brand appears across AI tools.
Wants clear sentiment signals and brand-hierarchy context to guide messaging.
Requires comparable responses to craft external statements.
Needs structured data for dashboards and reporting.
Need alignment on how different models represent the brand.
Wants repeatable audits to track impact of campaigns on AI visibility.
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Stores prompts and results; provides input and output tabs for the agent.
Fetches baseline model answers for each prompt.
Retrieves model answers with citations for comparison.
Optionally scrapes a ChatGPT web response for inclusion.
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The agent logs the Prompt, LLM used, Response text, a Brand mentioned flag, Brand Hierarchy, Basic Polarity, Emotion Category, and Source citations when available. This data is appended to the Output Google Sheet and can be re-exported. The log is designed to be auditable and repeatable for ongoing audits.
A dedicated LLM-powered sentiment pass analyzes each response to assign polarity (Positive/Neutral/Negative) and an emotion category (Joy, Sadness, Anger, Fear, Disgust, Surprise). It also extracts a brand-hierarchy sequence where present. The results are stored alongside the original response for context.
OpenAI API and Perplexity API are included by default. You may optionally enable a ChatGPT web actor path. You can replace or expand models by duplicating the request→map→append pattern within the workflow.
Disable the APIfy path in the agent configuration. The workflow will continue to fetch results from OpenAI and Perplexity, with citations and sentiment analysis applied to those responses only.
API keys are stored and accessed through the connected integration setup within your n8n environment. Follow your organization’s security policies for key management. The agent does not expose keys beyond the configured integrations, and access is governed by your Google Sheets and n8n permissions.
Yes. The agent supports replacing the Manual Trigger with a webhook or schedule trigger to run audits automatically. This enables regular, hands-off visibility reporting.
The hierarchy is derived from the brand mentions detected in responses, mapped as a directed path such as Brand > Sub-brand > Line. The path is captured when available and stored for review alongside sentiment and source data.
Monitor prompts across OpenAI, Perplexity, and ChatGPT web actor, log results to Google Sheets, and run sentiment and brand-hierarchy analysis to produce an auditable brand-visibility report.
