# AI Lead Enricher - LLM-Powered Company Intelligence (`lone_neutrino/ai-lead-enricher`) Actor Scrapes company websites and uses AI (Google Gemini) to classify industry, estimate size, detect tech stack, identify pain points, and score leads against your ICP. Turn raw URLs into sales-ready intelligence. - **URL**: https://apify.com/lone\_neutrino/ai-lead-enricher.md - **Developed by:** [caio gonçalves](https://apify.com/lone_neutrino) (community) - **Categories:** Automation, Lead generation, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Lead Enricher - LLM-Powered Company Intelligence Turn raw company URLs into **sales-ready intelligence** using AI. This actor scrapes company websites and uses **Google Gemini AI** to automatically classify, score, and enrich each lead with: - **Industry & sub-industry** classification - **Company size** estimation with reasoning - **Tech stack** detection - **Chatbot detection** (script-level + AI analysis) - **Pain points** identification - **Automation opportunities** specific to each company - **ICP scoring** (0-100) against YOUR ideal customer profile ### Why use this actor? | Feature | Traditional Scrapers | AI Lead Enricher | |---------|---------------------|------------------| | Company name | ✅ | ✅ | | Contact info | ✅ | ❌ (use with Leads Finder) | | Industry classification | ❌ | ✅ AI-powered | | Company size estimate | ❌ | ✅ with reasoning | | Tech stack detection | ❌ | ✅ | | Pain point analysis | ❌ | ✅ | | Custom ICP scoring | ❌ | ✅ 0-100 score | | Automation opportunities | ❌ | ✅ | ### Input - **Company URLs**: List of website URLs to analyze - **Gemini API Key**: Free at [Google AI Studio](https://aistudio.google.com/apikey) - **ICP Description**: Describe your ideal customer in plain English - **Max Pages Per Site**: 1-10 pages to crawl (default: 3) ### Output example ```json { "url": "https://acme-corp.com", "domain": "acme-corp.com", "companyName": "ACME Corporation", "industry": "SaaS", "subIndustry": "Project Management", "estimatedSize": "51-200", "description": "ACME builds project management tools for remote teams.", "products": ["TaskFlow", "TeamSync"], "techStack": ["React", "Node.js", "AWS", "Stripe"], "hasChatbot": false, "hasContactForm": true, "languages": ["English", "Dutch"], "targetMarket": "B2B", "painPoints": [ "Manual customer onboarding", "No automated support channel", "Limited self-service options" ], "automationOpportunities": [ "AI chatbot for customer support", "Automated onboarding workflows", "Smart lead qualification" ], "icpScore": 85, "icpScoreReasoning": "Strong B2B SaaS fit, right size range, no chatbot = high opportunity" } ```` ### Pricing \~$2 per 1,000 companies enriched (Apify platform costs + Gemini API). Gemini Flash is free for up to 15 requests/minute. For high volume, a paid Gemini key removes rate limits. ### Use cases 1. **Sales prospecting**: Enrich lead lists before outreach 2. **Market research**: Map an entire industry's tech adoption 3. **Competitive analysis**: Understand what tools companies use 4. **Agency sales**: Find companies that need your services (chatbot, automation, etc.) ### Combine with other actors 1. Use **Google Maps Scraper** to get company URLs 2. Run **AI Lead Enricher** to classify and score them 3. Use **Leads Finder** to get contact emails 4. Export to your CRM ### Cost optimization - Use `maxPagesPerSite: 1` for quick classification (~50% cheaper) - Use `maxPagesPerSite: 5` for deep analysis of high-value targets - Free Gemini API key handles ~200 companies/hour # Actor input Schema ## `urls` (type: `array`): List of company website URLs to enrich. ## `llmProvider` (type: `string`): Which AI model to use for enrichment. ## `apiKey` (type: `string`): API key for your chosen provider. Gemini: aistudio.google.com/apikey | OpenAI: platform.openai.com/api-keys | Anthropic: console.anthropic.com/settings/keys ## `llmModel` (type: `string`): Override the default model. Leave empty for defaults: gemini-2.0-flash / gpt-4o-mini / claude-sonnet-4-20250514 ## `icpDescription` (type: `string`): Describe your ideal customer in plain English. The AI scores each company 0-100 against this. ## `maxPagesPerSite` (type: `integer`): Pages to crawl per website (more = better data but slower) ## `proxyConfiguration` (type: `object`): Proxy settings for accessing websites ## Actor input object example ```json { "urls": [ "https://example.com" ], "llmProvider": "gemini", "icpDescription": "B2B SaaS companies with 10-200 employees in Europe that don't have a chatbot on their website", "maxPagesPerSite": 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 = { "urls": [ "https://example.com" ], "icpDescription": "B2B SaaS companies with 10-200 employees in Europe that don't have a chatbot on their website" }; // Run the Actor and wait for it to finish const run = await client.actor("lone_neutrino/ai-lead-enricher").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 = { "urls": ["https://example.com"], "icpDescription": "B2B SaaS companies with 10-200 employees in Europe that don't have a chatbot on their website", } # Run the Actor and wait for it to finish run = client.actor("lone_neutrino/ai-lead-enricher").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 '{ "urls": [ "https://example.com" ], "icpDescription": "B2B SaaS companies with 10-200 employees in Europe that don'\''t have a chatbot on their website" }' | apify call lone_neutrino/ai-lead-enricher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lone_neutrino/ai-lead-enricher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Lead Enricher - LLM-Powered Company Intelligence", "description": "Scrapes company websites and uses AI (Google Gemini) to classify industry, estimate size, detect tech stack, identify pain points, and score leads against your ICP. Turn raw URLs into sales-ready intelligence.", "version": "0.1", "x-build-id": "15ayZy4aXRKb4vrPJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lone_neutrino~ai-lead-enricher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lone_neutrino-ai-lead-enricher", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lone_neutrino~ai-lead-enricher/runs": { "post": { "operationId": "runs-sync-lone_neutrino-ai-lead-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lone_neutrino~ai-lead-enricher/run-sync": { "post": { "operationId": "run-sync-lone_neutrino-ai-lead-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls", "apiKey" ], "properties": { "urls": { "title": "Company URLs", "type": "array", "description": "List of company website URLs to enrich.", "items": { "type": "string" } }, "llmProvider": { "title": "AI Provider", "enum": [ "gemini", "openai", "anthropic" ], "type": "string", "description": "Which AI model to use for enrichment.", "default": "gemini" }, "apiKey": { "title": "AI API Key", "type": "string", "description": "API key for your chosen provider. Gemini: aistudio.google.com/apikey | OpenAI: platform.openai.com/api-keys | Anthropic: console.anthropic.com/settings/keys" }, "llmModel": { "title": "Model Override (optional)", "type": "string", "description": "Override the default model. Leave empty for defaults: gemini-2.0-flash / gpt-4o-mini / claude-sonnet-4-20250514" }, "icpDescription": { "title": "Ideal Customer Profile (ICP)", "type": "string", "description": "Describe your ideal customer in plain English. The AI scores each company 0-100 against this.", "default": "Any business that could benefit from automation or AI tools" }, "maxPagesPerSite": { "title": "Max Pages Per Site", "minimum": 1, "maximum": 10, "type": "integer", "description": "Pages to crawl per website (more = better data but slower)", "default": 3 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for accessing websites" } } }, "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 } } } } } } } } } } ```