# LLM Brand Visibility Tracker (`zinin/llm-brand-visibility`) Actor For each query that matters, check whether AI assistants recommend YOUR brand — and which competitors they cite instead. Grounded answers from Perplexity Sonar, GPT and Gemini via your own OpenRouter key. This is GEO: the SEO of the AI era. - **URL**: https://apify.com/zinin/llm-brand-visibility.md - **Developed by:** [Tim Zinin](https://apify.com/zinin) (community) - **Categories:** AI, Marketing - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 brand probes 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## LLM Brand Visibility — Are You Cited by ChatGPT, Perplexity & Gemini? When a buyer asks an AI assistant *"what's the best note-taking app?"*, does it name **you** — or your competitors? This Actor answers that, query by query, model by model. ### The problem Search is moving into AI answers. If ChatGPT and Perplexity recommend your competitor and never mention you, you're losing demand you can't even see in Google Analytics. GEO (Generative Engine Optimization) is the new SEO — but the SaaS tools that track it (Ahrefs Brand Radar, Profound) cost hundreds a month. This does the core job per-query, pay-as-you-go. ### What it does For every **query × model** you give it, it runs a *grounded* LLM answer and reports: - **mentioned** — did the model name your brand? - **position** — how early (rank among the brands it named)? - **competitorsCited** — who it recommended instead. - **snippet** — the relevant part of the answer. ### Bring your own OpenRouter key You supply your own [OpenRouter API key](https://openrouter.ai/keys). LLM token cost is billed to **your** account — this Actor only orchestrates, parses and scores, so there's no marked-up middle-man LLM fee. One key reaches multiple grounded engines: | Model ID | Engine | |----------|--------| | `perplexity/sonar` | Perplexity (built-in web search) | | `openai/gpt-4o-mini:online` | GPT + web plugin | | `google/gemini-2.0-flash-001:online` | Gemini + web plugin | ### Input ```json { "brand": "Notion", "queries": ["best note-taking app", "Notion alternatives"], "models": ["perplexity/sonar"], "lang": "en", "openrouterApiKey": "sk-or-...", "maxConcurrency": 3 } ```` ### Output One row per query × model: ```json { "brand": "Notion", "query": "best note-taking app", "model": "perplexity/sonar", "mentioned": true, "position": 1, "competitorsCited": ["Obsidian", "Microsoft OneNote", "Evernote"], "snippet": "...", "summary": "perplexity/sonar on \"best note-taking app\": Notion MENTIONED (#1); competitors: Obsidian, Microsoft OneNote, Evernote." } ``` ### Pricing Pay-per-event: **$0.01 per run + $0.05 per query×model row**. You also pay your own LLM tokens (~$0.005/row on Sonar). No monthly seat. ### FAQ **Why my own key?** So you pay LLM cost at cost, and there's no volume cap from us. **Which languages?** Any — set `lang` (e.g. `ru`, `tr`). Grounded models answer natively. **Can an agent call it?** Yes — standard Apify Actor, callable via API or the Apify MCP server. *** Built by [zinin](https://apify.com/zinin). Questions? Telegram @timzinin. # Actor input Schema ## `brand` (type: `string`): The brand, product or company you want to track in AI answers. ## `queries` (type: `array`): The prompts a real user would ask (e.g. `best note-taking app`, `Notion alternatives`). Each query is run against EVERY model in `models` below, and you are charged per query × model pair, not per query — e.g. 10 queries × 2 models = 20 billed rows. With the limits on both fields, the maximum possible is 20 queries × 5 models = 100 rows. ## `models` (type: `array`): OpenRouter model IDs to test. Grounded options: `perplexity/sonar`, `openai/gpt-4o-mini:online`, `google/gemini-2.0-flash-001:online`. Every model you add here multiplies the number of billed rows: rows = number of queries × number of models (see `queries`). ## `lang` (type: `string`): Answer language (e.g. en, ru, tr). ## `openrouterApiKey` (type: `string`): Your OpenRouter API key (https://openrouter.ai/keys). LLM token cost is billed to YOUR account; this Actor only orchestrates and scores. Optional to start the Actor: without it the run finishes cleanly and explains setup, and nothing is charged beyond the Actor start. ## `maxConcurrency` (type: `integer`): Parallel LLM calls (kept low to respect rate limits). ## Actor input object example ```json { "brand": "Notion", "queries": [ "best note-taking app", "best project management tool for startups" ], "models": [ "perplexity/sonar" ], "lang": "en", "maxConcurrency": 3 } ``` # 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", "queries": [ "best note-taking app", "best project management tool for startups" ], "models": [ "perplexity/sonar" ] }; // Run the Actor and wait for it to finish const run = await client.actor("zinin/llm-brand-visibility").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", "queries": [ "best note-taking app", "best project management tool for startups", ], "models": ["perplexity/sonar"], } # Run the Actor and wait for it to finish run = client.actor("zinin/llm-brand-visibility").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", "queries": [ "best note-taking app", "best project management tool for startups" ], "models": [ "perplexity/sonar" ] }' | apify call zinin/llm-brand-visibility --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=zinin/llm-brand-visibility", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LLM Brand Visibility Tracker", "description": "For each query that matters, check whether AI assistants recommend YOUR brand — and which competitors they cite instead. Grounded answers from Perplexity Sonar, GPT and Gemini via your own OpenRouter key. This is GEO: the SEO of the AI era.", "version": "0.1", "x-build-id": "8PjAx1WBVJPU31iVY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/zinin~llm-brand-visibility/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-zinin-llm-brand-visibility", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/zinin~llm-brand-visibility/runs": { "post": { "operationId": "runs-sync-zinin-llm-brand-visibility", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/zinin~llm-brand-visibility/run-sync": { "post": { "operationId": "run-sync-zinin-llm-brand-visibility", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "queries" ], "properties": { "brand": { "title": "Brand / product name", "type": "string", "description": "The brand, product or company you want to track in AI answers." }, "queries": { "title": "Queries", "maxItems": 20, "type": "array", "description": "The prompts a real user would ask (e.g. `best note-taking app`, `Notion alternatives`). Each query is run against EVERY model in `models` below, and you are charged per query × model pair, not per query — e.g. 10 queries × 2 models = 20 billed rows. With the limits on both fields, the maximum possible is 20 queries × 5 models = 100 rows.", "items": { "type": "string" } }, "models": { "title": "Models", "maxItems": 5, "type": "array", "description": "OpenRouter model IDs to test. Grounded options: `perplexity/sonar`, `openai/gpt-4o-mini:online`, `google/gemini-2.0-flash-001:online`. Every model you add here multiplies the number of billed rows: rows = number of queries × number of models (see `queries`).", "items": { "type": "string" } }, "lang": { "title": "Language", "type": "string", "description": "Answer language (e.g. en, ru, tr).", "default": "en" }, "openrouterApiKey": { "title": "OpenRouter API key", "type": "string", "description": "Your OpenRouter API key (https://openrouter.ai/keys). LLM token cost is billed to YOUR account; this Actor only orchestrates and scores. Optional to start the Actor: without it the run finishes cleanly and explains setup, and nothing is charged beyond the Actor start." }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "Parallel LLM calls (kept low to respect rate limits).", "default": 3 } } }, "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 } } } } } } } } } } ```