# AI Brand Visibility Monitor - ChatGPT, Perplexity, Claude (`chazee/ai-visibility-monitor`) Actor Do AI assistants recommend your brand? Measure mention rates, share of voice, sentiment, and which domains ChatGPT, Perplexity, Gemini & Google AI Overviews cite for your category. No API keys needed. - **URL**: https://apify.com/chazee/ai-visibility-monitor.md - **Developed by:** [Chaz Eden](https://apify.com/chazee) (community) - **Categories:** AI, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $60.00 / 1,000 ai engine response analyzeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AI Brand Visibility Monitor — ChatGPT, Perplexity, Gemini & Google AI Overviews **Find out whether AI assistants mention your brand — and who they cite instead.** When buyers ask ChatGPT, Perplexity, Gemini, Claude, or Google's AI Overviews "what's the best X", someone gets recommended. This actor tells you how often it's you, how often it's your competitors, and which websites the AI engines cite — so you know exactly where to earn coverage. This is GEO/AEO (Generative Engine Optimization) monitoring without a $300–500/month SaaS subscription. ### Quick Start 1. Enter your **brand** (plus aliases like your domain) and your **competitors**. 2. Add the **prompts** your buyers actually ask ("best CRM for small business"), or list **topics** and let the actor generate ~10 buyer-intent prompts per topic. 3. Pick your **engines** and hit Start. No API keys needed — the actor brings its own. Run it on a weekly [schedule](https://docs.apify.com/platform/schedules) to track your visibility over time — enable **trendMode** to get deltas against the previous run. ### What you get **One dataset record per prompt × engine × sample:** ```json { "prompt": "best note taking app for students", "engine": "perplexity", "sampleIndex": 1, "brandMentioned": true, "mentionPosition": 2, "recommended": true, "sentiment": "positive", "competitorsMentioned": ["Obsidian", "Evernote"], "citations": [{ "url": "https://zapier.com/blog/best-note-taking-apps/", "domain": "zapier.com", "title": "The best note-taking apps" }], "responseText": "...", "model": "sonar", "runAt": "2026-07-12T15:00:00Z" } ```` **Plus an aggregated scorecard** in the run's OUTPUT record (free — you only pay for result items): a blended 0–100 visibility score, a `visibilityByEngine` breakdown (your score on each AI engine at a glance), plus per-engine average mention position, recommendation rate, share of voice vs competitors, and top 10 cited domains — with trend deltas when trendMode is on. Failed queries are listed there too and are never billed. ```json { "brand": "Notion", "visibilityScore": 87.5, "visibilityByEngine": { "perplexity": 100.0, "chatgpt": 50.0, "gemini": 100.0, "claude": 100.0 }, "shareOfVoice": { "Notion": 38.0, "Obsidian": 41.0, "Evernote": 21.0 } } ``` ### Why results vary — and why that's handled LLM answers are non-deterministic: the same question can name different products run to run. That's why this actor supports **statistical sampling** — set `samplesPerPrompt` to 3–5 and you get mention *rates*, not one-shot anecdotes. ### Pricing math You pay per analyzed response (`engine-query` event). A full weekly audit of 25 prompts × 4 engines × 3 samples = 300 queries ≈ **$6–12/run**. The prefilled demo input costs well under $0.50. ### Use cases - **Agencies** — run white-label AI visibility audits per client brand; schedule weekly and export the scorecard into your reports. - **In-house SEO / content teams** — the top-cited-domains list is your GEO backlog: those are the sites AI engines trust for your category. - **Founders** — check whether AI assistants know your product exists in your category, and track the needle as you publish. ### FAQ **Why do results vary between runs?** LLMs are non-deterministic and their web results change. Use `samplesPerPrompt: 3` or more for stable rates. **What is GEO/AEO?** Generative Engine Optimization / Answer Engine Optimization — the practice of getting your brand mentioned and cited by AI answer engines, the way SEO targets classic search results. **Which models are queried?** Perplexity `sonar`, OpenAI GPT-5 mini with web search, Gemini 2.5 Flash with Google Search grounding, Claude Haiku 4.5 with web search, and the live Google AI Overview block (via residential proxies). Not every Google query triggers an AI Overview — `aiOverviewPresent: false` records tell you that too. **Do I need my own API keys?** No. The actor uses its own keys; the cost is built into the per-query price. # Actor input Schema ## `brand` (type: `string`): The brand to track across AI answer engines. ## `brandAliases` (type: `array`): Alternative names, domains, or product names that count as a mention of your brand. ## `competitors` (type: `array`): Competitor brands to measure share of voice against. ## `prompts` (type: `array`): Buyer-intent questions to ask each AI engine. Add the questions your customers actually type. ## `topics` (type: `array`): Optional: each topic is expanded into ~10 buyer-intent prompts using an LLM. Combined with explicit prompts above. ## `engines` (type: `array`): Which AI answer engines to query. ## `samplesPerPrompt` (type: `integer`): How many times each prompt is asked per engine. LLM answers vary run to run — more samples give more stable mention rates. ## `country` (type: `string`): Two-letter country code. Controls the Google AI Overview proxy geo and hl/gl parameters. ## `language` (type: `string`): Two-letter language code for Google AI Overview queries. ## `sentiment` (type: `boolean`): Classify sentiment and recommendation strength for each response that mentions your brand (one cheap LLM call per response). ## `trendMode` (type: `boolean`): Compare this run's scorecard against the previous run for the same brand + prompts and report deltas. ## `confirmLargeRun` (type: `boolean`): Required safety switch when prompts × engines × samples exceeds 2000 queries. ## Actor input object example ```json { "brand": "Notion", "brandAliases": [ "notion.so", "Notion AI" ], "competitors": [ "Obsidian", "Coda", "Evernote" ], "prompts": [ "best note taking app for students", "Notion vs Obsidian which should I use", "what is the best tool to organize my life" ], "engines": [ "perplexity", "chatgpt" ], "samplesPerPrompt": 1, "country": "US", "language": "en", "sentiment": true, "trendMode": false, "confirmLargeRun": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "brand": "Notion", "brandAliases": [ "notion.so", "Notion AI" ], "competitors": [ "Obsidian", "Coda", "Evernote" ], "prompts": [ "best note taking app for students", "Notion vs Obsidian which should I use", "what is the best tool to organize my life" ], "engines": [ "perplexity", "chatgpt" ], "samplesPerPrompt": 1 }; // Run the Actor and wait for it to finish const run = await client.actor("chazee/ai-visibility-monitor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "brand": "Notion", "brandAliases": [ "notion.so", "Notion AI", ], "competitors": [ "Obsidian", "Coda", "Evernote", ], "prompts": [ "best note taking app for students", "Notion vs Obsidian which should I use", "what is the best tool to organize my life", ], "engines": [ "perplexity", "chatgpt", ], "samplesPerPrompt": 1, } # Run the Actor and wait for it to finish run = client.actor("chazee/ai-visibility-monitor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "brand": "Notion", "brandAliases": [ "notion.so", "Notion AI" ], "competitors": [ "Obsidian", "Coda", "Evernote" ], "prompts": [ "best note taking app for students", "Notion vs Obsidian which should I use", "what is the best tool to organize my life" ], "engines": [ "perplexity", "chatgpt" ], "samplesPerPrompt": 1 }' | apify call chazee/ai-visibility-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=chazee/ai-visibility-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Brand Visibility Monitor - ChatGPT, Perplexity, Claude", "description": "Do AI assistants recommend your brand? Measure mention rates, share of voice, sentiment, and which domains ChatGPT, Perplexity, Gemini & Google AI Overviews cite for your category. No API keys needed.", "version": "0.0", "x-build-id": "tfgJTh2WLeIhyfHOw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/chazee~ai-visibility-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-chazee-ai-visibility-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/chazee~ai-visibility-monitor/runs": { "post": { "operationId": "runs-sync-chazee-ai-visibility-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/chazee~ai-visibility-monitor/run-sync": { "post": { "operationId": "run-sync-chazee-ai-visibility-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "brand" ], "properties": { "brand": { "title": "Brand name", "type": "string", "description": "The brand to track across AI answer engines." }, "brandAliases": { "title": "Brand aliases", "type": "array", "description": "Alternative names, domains, or product names that count as a mention of your brand.", "items": { "type": "string" } }, "competitors": { "title": "Competitors", "type": "array", "description": "Competitor brands to measure share of voice against.", "items": { "type": "string" } }, "prompts": { "title": "Prompts", "type": "array", "description": "Buyer-intent questions to ask each AI engine. Add the questions your customers actually type.", "items": { "type": "string" } }, "topics": { "title": "Topics (auto-expand)", "type": "array", "description": "Optional: each topic is expanded into ~10 buyer-intent prompts using an LLM. Combined with explicit prompts above.", "items": { "type": "string" } }, "engines": { "title": "Engines", "type": "array", "description": "Which AI answer engines to query.", "items": { "type": "string", "enum": [ "perplexity", "chatgpt", "gemini", "claude", "aiOverview" ], "enumTitles": [ "Perplexity", "ChatGPT (web search)", "Gemini (grounded)", "Claude (web search)", "Google AI Overviews" ] } }, "samplesPerPrompt": { "title": "Samples per prompt", "minimum": 1, "maximum": 5, "type": "integer", "description": "How many times each prompt is asked per engine. LLM answers vary run to run — more samples give more stable mention rates.", "default": 1 }, "country": { "title": "Country", "type": "string", "description": "Two-letter country code. Controls the Google AI Overview proxy geo and hl/gl parameters.", "default": "US" }, "language": { "title": "Language", "type": "string", "description": "Two-letter language code for Google AI Overview queries.", "default": "en" }, "sentiment": { "title": "Sentiment analysis", "type": "boolean", "description": "Classify sentiment and recommendation strength for each response that mentions your brand (one cheap LLM call per response).", "default": true }, "trendMode": { "title": "Trend mode", "type": "boolean", "description": "Compare this run's scorecard against the previous run for the same brand + prompts and report deltas.", "default": false }, "confirmLargeRun": { "title": "Confirm large run", "type": "boolean", "description": "Required safety switch when prompts × engines × samples exceeds 2000 queries.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```