# AI Visibility Rank Tracker — ChatGPT, Perplexity, Gemini (`muhammadafzal/ai-visibility-rank-tracker`) Actor Track brand ranking and share-of-voice across ChatGPT, Perplexity, Claude, and Gemini over time. Monitors prompt sets, detects citations, scores sentiment, compares competitors, and reports run-over-run position changes. Schedule it for daily AI-search visibility tracking. - **URL**: https://apify.com/muhammadafzal/ai-visibility-rank-tracker.md - **Developed by:** [Muhammad Afzal](https://apify.com/muhammadafzal) (community) - **Categories:** AI, SEO tools, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $60.00 / 1,000 prompt engine checks This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Visibility Rank Tracker — ChatGPT, Perplexity, Claude & Gemini Track how your brand ranks in AI answers over time. This actor queries **ChatGPT, Perplexity, Claude, and Gemini** with the prompts your customers actually ask, then measures whether your brand is mentioned, **where it ranks** versus competitors, its **share of voice**, whether your domain is **cited**, the **sentiment** of each mention — and how all of this **changes run over run**. Built for Generative Engine Optimization (GEO): the new discipline of getting your brand recommended by AI answer engines. Schedule it daily or weekly to build a visibility history and catch ranking drops before they cost you traffic. --- ### What it does For every **prompt × engine** combination, the actor returns a single clean row containing: - **mentioned** — was your brand named in the answer? - **position** — your rank by order of first appearance among all tracked brands (you + competitors) - **share_of_voice** — your % of total tracked-brand mentions in the answer - **cited / brand_cited_url** — was your domain referenced as a source? - **citations** — the source URLs the answer relied on - **sentiment** — positive / neutral / negative framing of your mention - **competitors_mentioned** — which rivals showed up alongside you - **position_change / visibility_status** — the delta versus the previous run (`new`, `gained`, `lost`, `improved`, `declined`, `unchanged`, `absent`) A run-level rollup (visibility score, average position, share of voice, gained/lost counts, per-engine breakdown) is saved to the key-value store under `SUMMARY`. Export results, run via API, schedule and monitor runs, or integrate with other tools. --- ### Why track AI visibility? Buyers increasingly ask ChatGPT, Perplexity, Claude, and Gemini "what's the best tool for X?" instead of scrolling Google. If your brand isn't named — or is ranked below competitors — you lose the recommendation. Traditional SEO rank trackers don't see this. This actor does, across four engines, with trend history. --- ### Input | Field | Type | Description | |-------|------|-------------| | `brandName` (required) | string | The brand/product to track. | | `brandDomain` | string | Your domain (e.g., `notion.so`) for citation detection. | | `industry` | string | Used to auto-generate prompts when you don't supply your own. | | `prompts` | string[] | The exact questions to monitor. Falls back to auto-generated prompts from `industry`. | | `platforms` | string[] | Any of `perplexity`, `chatgpt`, `claude`, `gemini`. | | `competitors` | string[] | Competitor brands for position & share-of-voice. | | `extractCitations` | boolean | Extract source URLs and detect domain citations. | | `analyzeSentiment` | boolean | Classify the sentiment of each brand mention. | | `trackingId` | string | Stable ID used to compare history across runs (defaults to a slug of the brand). | | `useWebSearch` | boolean | Enable OpenRouter's web plugin for non-native-search models. | | `webSearchResults` | integer | Web results per query for the plugin (1–10). | | `models` | object | Optional per-platform OpenRouter model-slug overrides. | | `maxPrompts` | integer | Cap on prompts per run (cost control). | #### Example input ```json { "brandName": "Notion", "brandDomain": "notion.so", "industry": "productivity software", "prompts": [ "What is the best note-taking app for teams?", "Recommend an all-in-one workspace tool." ], "platforms": ["perplexity", "chatgpt", "claude", "gemini"], "competitors": ["Evernote", "Obsidian", "Coda"], "extractCitations": true, "analyzeSentiment": true } ```` *** ### API key (required) — one key for all models All engines run through **[OpenRouter](https://openrouter.ai)**, so you only need a **single** API key. Set it as an environment variable on the actor (Console → Settings → Environment variables): ``` OPENROUTER_API_KEY = sk-or-... ``` If the key is missing, every check is reported with an `error` and **not charged**. | Platform | Default model | Web search | |----------|---------------|------------| | Perplexity | `perplexity/sonar` | native | | ChatGPT | `openai/gpt-4o-mini` | OpenRouter web plugin | | Claude | `anthropic/claude-sonnet-4` | OpenRouter web plugin | | Gemini | `google/gemini-2.5-flash` | OpenRouter web plugin | > **Note:** For non-Perplexity models, web grounding uses OpenRouter's web plugin (it adds live search results to any model) rather than each vendor's own search product — the right trade-off for one-key simplicity, and it still reflects current web data. You pay OpenRouter directly for model + web-plugin usage; the actor's PPE events cover the tracking/analysis layer. Override any model slug with the `models` input. *** ### Output Each row in the dataset: ```json { "brand": "Notion", "brand_domain": "notion.so", "engine": "perplexity", "model_used": "sonar", "prompt": "What is the best note-taking app for teams?", "mentioned": true, "position": 1, "share_of_voice": 60, "cited": true, "citations": ["https://notion.so/product", "https://example.com/review"], "brand_cited_url": "https://notion.so/product", "mention_count": 2, "mention_context": "Notion is one of the most popular all-in-one workspaces...", "sentiment": "positive", "competitors_mentioned": ["Evernote", "Obsidian"], "previous_mentioned": true, "previous_position": 3, "position_change": 2, "visibility_status": "improved", "error": null, "checked_at": "2026-06-07T12:00:00.000Z" } ``` *** ### Pricing (Pay Per Event) Charged events: | Event | When | |-------|------| | `actor-start` | Once per run. | | `prompt-engine-check` | Each successful prompt × engine check. | | `citation-extraction` | Each successful check when citation extraction is enabled. | | `sentiment-analysis` | Each mention scored when sentiment analysis is enabled. | Failed checks (e.g., a missing API key) are **never charged**. You pay your own LLM provider for the underlying API calls. *** ### Scheduling for trend tracking 1. Run once to seed a baseline (first run shows `visibility_status: new` / `absent`). 2. Create a Schedule (daily or weekly) with the same `trackingId`. 3. Each run compares against the previous snapshot and fills `position_change` and `visibility_status`. History is stored in a named key-value store (`ai-rank-history-`), so multiple brands can be tracked independently. *** ### Use cases - **GEO / AEO monitoring** — prove and improve how AI engines recommend you. - **Competitive intelligence** — see who AI names ahead of you, and where. - **Content ROI** — confirm whether new content gets you cited. - **Agency reporting** — deliver weekly AI-visibility reports to clients. *** ### Notes - Results depend on each engine's live web access and model behavior; answers vary between runs, which is exactly what trend tracking is for. - Keep prompts stable across runs for the cleanest deltas. # Actor input Schema ## `brandName` (type: `string`): The brand, company, or product to track in AI answers (e.g., 'Notion', 'Tesla'). This is the primary entity scored for visibility, position, and share-of-voice. ## `brandDomain` (type: `string`): Your brand's website domain (e.g., 'notion.so'). Used to detect when an AI engine cites your site as a source. Leave empty to skip citation-by-domain matching. ## `industry` (type: `string`): Your category (e.g., 'productivity software', 'electric vehicles'). Used ONLY to auto-generate a sensible prompt set when you do not supply your own prompts below. Ignored if 'prompts' is provided. ## `prompts` (type: `array`): The exact questions your buyers ask AI engines (e.g., 'What is the best note-taking app?'). Provide your own list for precise tracking. If left empty, prompts are auto-generated from 'industry'. Each prompt is checked on every selected platform. ## `platforms` (type: `array`): Which AI answer engines to query. All engines run through OpenRouter using a single OPENROUTER\_API\_KEY (set as an environment variable). Defaults: perplexity→perplexity/sonar, chatgpt→openai/gpt-4o-mini, claude→anthropic/claude-sonnet-4, gemini→google/gemini-2.5-flash. Override slugs via the 'models' field. ## `competitors` (type: `array`): Competitor brand names to compare against (e.g., 'Evernote', 'Obsidian'). Used to compute your position (rank order) and share-of-voice within each AI answer. ## `extractCitations` (type: `boolean`): Enable to extract the source URLs each AI answer references and flag whether your domain is cited. Adds a per-check charge. ## `analyzeSentiment` (type: `boolean`): Enable to classify the sentiment (positive / neutral / negative) of how your brand is mentioned. Adds a per-mention charge. ## `trackingId` (type: `string`): Optional stable identifier used to store and compare history across scheduled runs. Defaults to a slug of the brand name. Use distinct IDs to track multiple brands separately. ## `useWebSearch` (type: `boolean`): Enable live web search for models that support it via OpenRouter's web plugin (Perplexity Sonar already searches natively). Keep on for accurate AI-search visibility; turn off to test the models' built-in knowledge only. ## `webSearchResults` (type: `integer`): How many web results the OpenRouter web plugin should fetch per query (more = richer answers but higher cost). Applies to non-native-search models. ## `models` (type: `object`): Optional. Map each platform to a specific OpenRouter model slug, e.g. {"chatgpt": "openai/gpt-4o", "claude": "anthropic/claude-sonnet-4"}. Any platform not listed uses its default model. ## `maxPrompts` (type: `integer`): Safety cap on how many prompts are checked per run (controls cost). Extra prompts beyond this limit are ignored. ## Actor input object example ```json { "brandName": "Notion", "brandDomain": "notion.so", "industry": "productivity software", "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams.", "Which all-in-one workspace tools are most popular?" ], "platforms": [ "perplexity", "chatgpt", "claude", "gemini" ], "competitors": [ "Evernote", "Obsidian", "Coda" ], "extractCitations": true, "analyzeSentiment": true, "trackingId": "", "useWebSearch": true, "webSearchResults": 3, "models": {}, "maxPrompts": 25 } ``` # Actor output Schema ## `results` (type: `string`): Link to the dataset containing all prompt x engine visibility tracking rows. # 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 = { "brandName": "Notion", "brandDomain": "notion.so", "industry": "productivity software", "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams.", "Which all-in-one workspace tools are most popular?" ], "competitors": [ "Evernote", "Obsidian", "Coda" ] }; // Run the Actor and wait for it to finish const run = await client.actor("muhammadafzal/ai-visibility-rank-tracker").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 = { "brandName": "Notion", "brandDomain": "notion.so", "industry": "productivity software", "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams.", "Which all-in-one workspace tools are most popular?", ], "competitors": [ "Evernote", "Obsidian", "Coda", ], } # Run the Actor and wait for it to finish run = client.actor("muhammadafzal/ai-visibility-rank-tracker").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 '{ "brandName": "Notion", "brandDomain": "notion.so", "industry": "productivity software", "prompts": [ "What is the best note-taking and productivity app?", "Recommend the top productivity software for teams.", "Which all-in-one workspace tools are most popular?" ], "competitors": [ "Evernote", "Obsidian", "Coda" ] }' | apify call muhammadafzal/ai-visibility-rank-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=muhammadafzal/ai-visibility-rank-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Visibility Rank Tracker — ChatGPT, Perplexity, Gemini", "description": "Track brand ranking and share-of-voice across ChatGPT, Perplexity, Claude, and Gemini over time. Monitors prompt sets, detects citations, scores sentiment, compares competitors, and reports run-over-run position changes. Schedule it for daily AI-search visibility tracking.", "version": "1.0", "x-build-id": "0GR7NVCyynIaHRdRo" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/muhammadafzal~ai-visibility-rank-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-muhammadafzal-ai-visibility-rank-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/muhammadafzal~ai-visibility-rank-tracker/runs": { "post": { "operationId": "runs-sync-muhammadafzal-ai-visibility-rank-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/muhammadafzal~ai-visibility-rank-tracker/run-sync": { "post": { "operationId": "run-sync-muhammadafzal-ai-visibility-rank-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "brandName" ], "properties": { "brandName": { "title": "Brand Name", "type": "string", "description": "The brand, company, or product to track in AI answers (e.g., 'Notion', 'Tesla'). This is the primary entity scored for visibility, position, and share-of-voice.", "default": "Notion" }, "brandDomain": { "title": "Brand Domain", "type": "string", "description": "Your brand's website domain (e.g., 'notion.so'). Used to detect when an AI engine cites your site as a source. Leave empty to skip citation-by-domain matching.", "default": "notion.so" }, "industry": { "title": "Industry / Category", "type": "string", "description": "Your category (e.g., 'productivity software', 'electric vehicles'). Used ONLY to auto-generate a sensible prompt set when you do not supply your own prompts below. Ignored if 'prompts' is provided.", "default": "productivity software" }, "prompts": { "title": "Tracked Prompts", "type": "array", "description": "The exact questions your buyers ask AI engines (e.g., 'What is the best note-taking app?'). Provide your own list for precise tracking. If left empty, prompts are auto-generated from 'industry'. Each prompt is checked on every selected platform.", "items": { "type": "string" }, "default": [] }, "platforms": { "title": "AI Platforms to Track", "type": "array", "description": "Which AI answer engines to query. All engines run through OpenRouter using a single OPENROUTER_API_KEY (set as an environment variable). Defaults: perplexity→perplexity/sonar, chatgpt→openai/gpt-4o-mini, claude→anthropic/claude-sonnet-4, gemini→google/gemini-2.5-flash. Override slugs via the 'models' field.", "items": { "type": "string" }, "default": [ "perplexity", "chatgpt", "claude", "gemini" ] }, "competitors": { "title": "Competitor Brands", "type": "array", "description": "Competitor brand names to compare against (e.g., 'Evernote', 'Obsidian'). Used to compute your position (rank order) and share-of-voice within each AI answer.", "items": { "type": "string" }, "default": [] }, "extractCitations": { "title": "Add-on: Extract Citations", "type": "boolean", "description": "Enable to extract the source URLs each AI answer references and flag whether your domain is cited. Adds a per-check charge.", "default": true }, "analyzeSentiment": { "title": "Add-on: Analyze Sentiment", "type": "boolean", "description": "Enable to classify the sentiment (positive / neutral / negative) of how your brand is mentioned. Adds a per-mention charge.", "default": true }, "trackingId": { "title": "Tracking ID", "type": "string", "description": "Optional stable identifier used to store and compare history across scheduled runs. Defaults to a slug of the brand name. Use distinct IDs to track multiple brands separately.", "default": "" }, "useWebSearch": { "title": "Use Web Search", "type": "boolean", "description": "Enable live web search for models that support it via OpenRouter's web plugin (Perplexity Sonar already searches natively). Keep on for accurate AI-search visibility; turn off to test the models' built-in knowledge only.", "default": true }, "webSearchResults": { "title": "Web Search Results", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many web results the OpenRouter web plugin should fetch per query (more = richer answers but higher cost). Applies to non-native-search models.", "default": 3 }, "models": { "title": "Model Overrides", "type": "object", "description": "Optional. Map each platform to a specific OpenRouter model slug, e.g. {\"chatgpt\": \"openai/gpt-4o\", \"claude\": \"anthropic/claude-sonnet-4\"}. Any platform not listed uses its default model.", "default": {} }, "maxPrompts": { "title": "Max Prompts", "minimum": 1, "maximum": 200, "type": "integer", "description": "Safety cap on how many prompts are checked per run (controls cost). Extra prompts beyond this limit are ignored.", "default": 25 } } }, "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 } } } } } } } } } } ```