# LLM Brand Share-of-Voice Monitor | GPT Claude Gemini (`eesti/llm-brand-monitor`) Actor Measure brand vs competitor mentions across GPT, Claude, and Gemini answers for buying-intent prompts. User-provided API keys. $0.10 per prompt×model + $0.02 start. - **URL**: https://apify.com/eesti/llm-brand-monitor.md - **Developed by:** [ando](https://apify.com/eesti) (community) - **Categories:** Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 prompt×model checkeds 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 Share-of-Voice Monitor Run buying-intent prompts against GPT, Claude, and/or Gemini and measure which brands are mentioned first — and with what sentiment. **Bring your own API keys.** Keys are used only for the run and never stored by this Actor beyond the run input. ### Input | Field | Required | Description | |---|---|---| | brand | yes | Primary brand | | competitor_brands | no | Competitors to track | | prompts | no | Custom prompts (defaults to 10 templates) | | models | yes | `gpt`, `claude`, and/or `gemini` | | openaiApiKey / anthropicApiKey / googleApiKey | for selected models | Provider keys | ### Output example ```json { "model": "gpt", "prompt": "What are the best tools for project management in 2026?", "brands_mentioned": [{ "brand": "Notion", "position": 1, "sentiment": "positive" }], "answer_excerpt": "...", "answered_at": "2026-07-23T12:00:00.000Z" } ```` ### Cost - Actor start: **$0.02** - Per prompt × model: **$0.10** - Typical 10 prompts × 1 model: **~$1.02** (plus your LLM API costs) ### Limitations - Mentions are string-matched; rebrands/aliases may be missed. - Sentiment is a lightweight keyword heuristic, not an NLU model. - LLM answers are non-deterministic across runs. ### Reviews If this Actor helped, leave a short Store review. # Actor input Schema ## `brand` (type: `string`): Primary brand name to track (e.g. Notion). ## `competitor_brands` (type: `array`): Competitor brand names to compare against. ## `prompts` (type: `array`): Custom prompts. If empty, 10 default buying-intent templates are used (with {brand} context). ## `models` (type: `array`): Models to query: gpt, claude, gemini. ## `openaiApiKey` (type: `string`): Required when models includes gpt. Stored only for this run. ## `anthropicApiKey` (type: `string`): Required when models includes claude. ## `googleApiKey` (type: `string`): Required when models includes gemini. ## Actor input object example ```json { "brand": "Notion", "competitor_brands": [ "Asana", "Monday.com", "ClickUp" ], "models": [ "gpt" ] } ``` # Actor output Schema ## `results` (type: `string`): No description ## `resultsCsv` (type: `string`): No description ## `run` (type: `string`): No description # 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", "competitor_brands": [ "Asana", "Monday.com", "ClickUp" ], "models": [ "gpt" ] }; // Run the Actor and wait for it to finish const run = await client.actor("eesti/llm-brand-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", "competitor_brands": [ "Asana", "Monday.com", "ClickUp", ], "models": ["gpt"], } # Run the Actor and wait for it to finish run = client.actor("eesti/llm-brand-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", "competitor_brands": [ "Asana", "Monday.com", "ClickUp" ], "models": [ "gpt" ] }' | apify call eesti/llm-brand-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=eesti/llm-brand-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LLM Brand Share-of-Voice Monitor | GPT Claude Gemini", "description": "Measure brand vs competitor mentions across GPT, Claude, and Gemini answers for buying-intent prompts. User-provided API keys. $0.10 per prompt×model + $0.02 start.", "version": "1.0", "x-build-id": "aIpRrNfAUoufOXiLd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/eesti~llm-brand-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-eesti-llm-brand-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/eesti~llm-brand-monitor/runs": { "post": { "operationId": "runs-sync-eesti-llm-brand-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/eesti~llm-brand-monitor/run-sync": { "post": { "operationId": "run-sync-eesti-llm-brand-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", "models" ], "properties": { "brand": { "title": "Brand", "type": "string", "description": "Primary brand name to track (e.g. Notion)." }, "competitor_brands": { "title": "Competitor brands", "type": "array", "description": "Competitor brand names to compare against.", "items": { "type": "string" } }, "prompts": { "title": "Prompts", "type": "array", "description": "Custom prompts. If empty, 10 default buying-intent templates are used (with {brand} context).", "items": { "type": "string" } }, "models": { "title": "Models", "type": "array", "description": "Models to query: gpt, claude, gemini.", "default": [ "gpt" ], "items": { "type": "string" } }, "openaiApiKey": { "title": "OpenAI API key", "type": "string", "description": "Required when models includes gpt. Stored only for this run." }, "anthropicApiKey": { "title": "Anthropic API key", "type": "string", "description": "Required when models includes claude." }, "googleApiKey": { "title": "Google AI API key", "type": "string", "description": "Required when models includes gemini." } } }, "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 } } } } } } } } } } ```