# B2B Sales Lead Engine (`reflective_plagioclase/b2b-sales-engine`) Actor - **URL**: https://apify.com/reflective\_plagioclase/b2b-sales-engine.md - **Developed by:** [Matt](https://apify.com/reflective_plagioclase) (community) - **Categories:** Lead generation, Business, Automation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.50 / 1,000 lead generateds 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 ## B2B Sales Lead Engine — End-to-End Pipeline **Generate qualified B2B leads in minutes.** This actor runs a complete 5-phase pipeline: company discovery → website analysis → contact extraction → lead scoring → CRM-ready export. Perfect for sales teams, recruiters, and business developers who need structured lead lists without manually researching each prospect. ### How It Works The pipeline executes five phases automatically: 1. **Company Discovery** — Searches DuckDuckGo Lite and LinkedIn for companies matching your industry, location, and keywords 2. **Website Analysis** — Scrapes each company's homepage, about page, and contact page for metadata, description, tech stack, and team size 3. **Contact Extraction** — Finds emails (classified as named, info, sales, support, or jobs), phone numbers, and social media links (LinkedIn, Twitter, Facebook, Instagram, GitHub) 4. **Lead Scoring** — Scores each lead 1–100 based on data completeness, contact quality, and enrichment depth. Labeled HIGH / MEDIUM / LOW confidence 5. **CRM Export** — Output includes pre-mapped fields for HubSpot and Salesforce ### Input Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `targetIndustry` | string | ✅ | Industry to target (e.g., "SaaS", "manufacturing", "healthcare") | | `targetLocation` | string | ✅ | Geographic area (e.g., "New York", "Berlin", "remote") | | `targetKeywords` | string | — | Extra keywords to refine the search | | `maxLeads` | integer | — | Max leads to generate (default: 50, max: 500) | | `includeEmails` | boolean | — | Extract email addresses from company websites (default: true) | | `includePhones` | boolean | — | Extract phone numbers (default: true) | | `includeSocialMedia` | boolean | — | Extract social media profile links (default: true) | | `proxy` | object | — | Proxy configuration (default: Apify proxy) | ### Output Example Each lead is a structured JSON object: ```json { "company_name": "Acme Corp", "domain": "acme.com", "linkedin_url": "https://linkedin.com/company/acme", "industry": "SaaS", "description": "Acme Corp provides cloud-based project management...", "location": "San Francisco", "team_size": "50-200", "emails": [ {"address": "john@acme.com", "type": "named", "context": "/about"}, {"address": "sales@acme.com", "type": "sales", "context": "/contact"} ], "phones": ["+1-555-0123"], "social_links": {"linkedin": "...", "twitter": "..."}, "tech_stack": ["React", "AWS", "Stripe"], "lead_score": 85, "lead_confidence": "HIGH", "_hubspot_fields": {"company": "Acme Corp", "website": "acme.com", ...}, "_salesforce_fields": {"Name": "Acme Corp", "Website": "acme.com", ...} } ```` ### Use Cases - **Sales prospecting** — Build targeted account lists for outbound campaigns - **Market research** — Profile competitors or identify companies in a new market - **Event prep** — Generate attendee / sponsor lists for conferences - **Partnership sourcing** — Find integration partners by tech stack and industry - **Recruiting** — Identify companies hiring specific roles in a region ### Pricing Pay-per-event: **$0.0045 per lead generated** ($4.50 per 1,000 leads). You only pay for leads that are successfully generated and pushed to the output dataset. ### Tips - Be specific with industry + location for best results (e.g., "SaaS" + "Berlin" yields more targeted leads than broad terms) - Use `targetKeywords` to filter by tech stack ("React"), business model ("B2B"), or company stage ("Series A") - Leads with named contacts (email names, not info@) score higher automatically - The output includes `_hubspot_fields` and `_salesforce_fields` — ready for direct CRM import # Actor input Schema ## `targetIndustry` (type: `string`): Industry to find companies in (e.g., 'SaaS', 'manufacturing', 'healthcare') ## `targetLocation` (type: `string`): Location to search (e.g., 'New York', 'Berlin', 'remote') ## `targetKeywords` (type: `string`): Extra keywords to refine search (optional) ## `maxLeads` (type: `integer`): Maximum number of leads to generate ## `includeEmails` (type: `boolean`): Extract email addresses from company websites ## `includePhones` (type: `boolean`): Extract phone numbers from company websites ## `includeSocialMedia` (type: `boolean`): Extract social media profile links ## `verifyEmails` (type: `boolean`): Perform basic email domain verification ## `proxy` (type: `object`): Proxy settings for scraping ## Actor input object example ```json { "targetIndustry": "SaaS", "targetLocation": "New York", "maxLeads": 50, "includeEmails": true, "includePhones": true, "includeSocialMedia": true, "verifyEmails": false, "proxy": { "useApifyProxy": true } } ``` # 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 = { "targetIndustry": "SaaS", "targetLocation": "New York", "targetKeywords": "" }; // Run the Actor and wait for it to finish const run = await client.actor("reflective_plagioclase/b2b-sales-engine").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 = { "targetIndustry": "SaaS", "targetLocation": "New York", "targetKeywords": "", } # Run the Actor and wait for it to finish run = client.actor("reflective_plagioclase/b2b-sales-engine").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 '{ "targetIndustry": "SaaS", "targetLocation": "New York", "targetKeywords": "" }' | apify call reflective_plagioclase/b2b-sales-engine --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=reflective_plagioclase/b2b-sales-engine", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "B2B Sales Lead Engine", "version": "0.1", "x-build-id": "mQRW8Ji0OUoQCZGfN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/reflective_plagioclase~b2b-sales-engine/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-reflective_plagioclase-b2b-sales-engine", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/reflective_plagioclase~b2b-sales-engine/runs": { "post": { "operationId": "runs-sync-reflective_plagioclase-b2b-sales-engine", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/reflective_plagioclase~b2b-sales-engine/run-sync": { "post": { "operationId": "run-sync-reflective_plagioclase-b2b-sales-engine", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "targetIndustry", "targetLocation" ], "properties": { "targetIndustry": { "title": "Target Industry", "type": "string", "description": "Industry to find companies in (e.g., 'SaaS', 'manufacturing', 'healthcare')" }, "targetLocation": { "title": "Target Location", "type": "string", "description": "Location to search (e.g., 'New York', 'Berlin', 'remote')" }, "targetKeywords": { "title": "Additional Keywords", "type": "string", "description": "Extra keywords to refine search (optional)" }, "maxLeads": { "title": "Max Leads", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of leads to generate", "default": 50 }, "includeEmails": { "title": "Include Emails", "type": "boolean", "description": "Extract email addresses from company websites", "default": true }, "includePhones": { "title": "Include Phone Numbers", "type": "boolean", "description": "Extract phone numbers from company websites", "default": true }, "includeSocialMedia": { "title": "Include Social Media", "type": "boolean", "description": "Extract social media profile links", "default": true }, "verifyEmails": { "title": "Verify Email Domains", "type": "boolean", "description": "Perform basic email domain verification", "default": false }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings for scraping", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```