# Lead Finder (`jungle_synthesizer/lead-finder`) Actor Find B2B leads by job title, location, and industry. Searches public sources for LinkedIn profiles and returns full name, job title, company, LinkedIn URL, and guessed email. No API keys required. - **URL**: https://apify.com/jungle\_synthesizer/lead-finder.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## Lead Finder Find B2B leads by job title, location, and industry. Searches DuckDuckGo for public LinkedIn profiles matching your criteria and returns full name, job title, company, LinkedIn URL, and guessed email address. No API keys or credentials required. ### What it does For each combination of job title and location you provide, the actor searches DuckDuckGo for LinkedIn profiles matching that combination. It extracts profile details directly from the search snippets and constructs a best-guess email address based on the person's name and company domain. **Output per lead:** - Full name (split into first/last) - Job title - Company name - LinkedIn profile URL - Guessed email address (`firstname.lastname@company.com` pattern) - Source search URL - Timestamp ### Pricing You are only charged for leads where an email address was successfully guessed. Leads with no guessable email are returned free. ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `jobTitles` | Array of strings | Yes | Job titles to search for (e.g. `"CEO"`, `"Software Engineer"`) | | `locations` | Array of strings | Yes | Cities or regions to search in (e.g. `"San Francisco"`, `"New York"`) | | `industries` | Array of strings | No | Industries to filter by (e.g. `"Technology"`, `"Finance"`) | | `maxItems` | Integer | No | Maximum leads to return (default: 100) | ### Example input ```json { "jobTitles": ["CEO", "CTO"], "locations": ["San Francisco", "New York"], "industries": ["Technology"], "maxItems": 50 } ```` ### Example output ```json [ { "full_name": "Evan Conrad", "first_name": "Evan", "last_name": "Conrad", "title": "CEO", "company": "San Francisco Compute", "linkedin_url": "https://www.linkedin.com/in/evan-conrad", "email_guess": "evan.conrad@sanfranciscocompute.com", "source_url": "https://duckduckgo.com/?q=site%3Alinkedin.com%2Fin+%22CEO%22+%22San+Francisco%22", "scraped_at": "2026-06-02T12:06:22.453Z" } ] ``` ### Notes - Email addresses are guesses based on the most common pattern (`firstname.lastname@companydomain.com`) and are **not verified**. Use an email verification service before sending. - Search results come from DuckDuckGo's `site:linkedin.com/in` index. Coverage depends on how many LinkedIn profiles are indexed by DuckDuckGo. - Each (job title × location) combination generates one search query. With 2 titles and 3 locations, the actor makes 6 queries. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `jobTitles` (type: `array`): List of job titles to search for (e.g. "CEO", "Software Engineer", "Marketing Manager") ## `locations` (type: `array`): List of locations/cities to search in (e.g. "San Francisco", "New York", "London") ## `industries` (type: `array`): List of industries to filter by (e.g. "Technology", "Finance", "Healthcare"). Leave empty to search all industries. ## `maxItems` (type: `integer`): Maximum number of leads to return. Only leads with a guessed email are billed. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "jobTitles": [ "CEO", "CTO" ], "locations": [ "San Francisco", "New York" ], "industries": [], "maxItems": 10 } ``` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "jobTitles": [ "CEO", "CTO" ], "locations": [ "San Francisco", "New York" ], "industries": [], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/lead-finder").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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "jobTitles": [ "CEO", "CTO", ], "locations": [ "San Francisco", "New York", ], "industries": [], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/lead-finder").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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "jobTitles": [ "CEO", "CTO" ], "locations": [ "San Francisco", "New York" ], "industries": [], "maxItems": 10 }' | apify call jungle_synthesizer/lead-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/lead-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lead Finder", "description": "Find B2B leads by job title, location, and industry. Searches public sources for LinkedIn profiles and returns full name, job title, company, LinkedIn URL, and guessed email. No API keys required.", "version": "0.1", "x-build-id": "Pn4vQjjnmBLzHVsJE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~lead-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-lead-finder", "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/jungle_synthesizer~lead-finder/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-lead-finder", "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/jungle_synthesizer~lead-finder/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-lead-finder", "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": [ "jobTitles", "locations" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "jobTitles": { "title": "Job Titles", "type": "array", "description": "List of job titles to search for (e.g. \"CEO\", \"Software Engineer\", \"Marketing Manager\")", "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "List of locations/cities to search in (e.g. \"San Francisco\", \"New York\", \"London\")", "items": { "type": "string" } }, "industries": { "title": "Industries", "type": "array", "description": "List of industries to filter by (e.g. \"Technology\", \"Finance\", \"Healthcare\"). Leave empty to search all industries.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of leads to return. Only leads with a guessed email are billed.", "default": 100 } } }, "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 } } } } } } } } } } ```