# AI Lead Enrichment — Firmographics, ICP Score & Outreach (`muhammadafzal/ai-lead-enrichment`) Actor Enrich a list of companies, domains, or people with firmographics, role data, an ICP-fit score, and a personalized outreach line — powered by an LLM with live web search. Turn raw lists into sales-ready leads. - **URL**: https://apify.com/muhammadafzal/ai-lead-enrichment.md - **Developed by:** [Muhammad Afzal](https://apify.com/muhammadafzal) (community) - **Categories:** AI, Lead generation, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 lead enricheds 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 ## AI Lead Enrichment — Firmographics, ICP Score & Outreach Turn a raw list of companies, domains, or people into **sales-ready leads**. Give the actor your list and it uses an LLM with **live web search** to fill in firmographics (industry, size, HQ, founded year), role/seniority, a concise summary, an **ICP-fit score** against your ideal customer profile, and a **personalized outreach opener** for each lead. Export results, run via API, schedule and monitor runs, or integrate with your CRM and outbound tools. --- ### What you get per lead | Field | Example | |---|---| | `company_name`, `domain` | Apify, apify.com | | `industry`, `company_size`, `hq_location`, `founded_year` | Web scraping & automation, 51-200, Prague, 2015 | | `company_description` | Full-stack web scraping and automation platform. | | `person_name`, `role_title`, `seniority` | Jane Doe, VP of Sales, VP | | `summary` | Mid-size scraping platform; strong fit for proxy cost savings. | | `icp_fit_score` (0-100) + `icp_fit_reasons` | 85 — matches industry, headcount in range | | `personalized_opener` | "Saw Apify scaled its proxy fleet—we cut similar costs 40% for data teams." | | `citations`, `confidence` | source URLs, high/medium/low | --- ### Input | Field | Type | Description | |---|---|---| | `leads` | array | Objects with any of `company`, `domain`, `fullName`, `role`, `location`, `notes`. | | `icpDescription` | string | Your ideal customer profile — used to score each lead's fit. | | `offerContext` | string | What you're selling — used to write the personalized opener. | | `model` | string | OpenRouter model slug (default `openai/gpt-4o-mini`). | | `useWebSearch` | boolean | Ground enrichment in live web results (recommended). | | `webSearchResults` | integer | Web results per lead (1–10). | | `maxLeads` | integer | Cap on leads per run (cost control). | #### Example input ```json { "leads": [ { "company": "Apify", "domain": "apify.com" }, { "fullName": "Jane Doe", "company": "Acme", "role": "VP Sales" } ], "icpDescription": "B2B SaaS, 50-1000 employees, data/AI teams", "offerContext": "Managed proxy + data pipeline that cuts scraping costs 40%", "model": "openai/gpt-4o-mini", "useWebSearch": true } ```` *** ### API key (required) Enrichment runs through **[OpenRouter](https://openrouter.ai)** — set a single environment variable on the actor (Console → Settings → Environment variables, mark as **Secret**): ``` OPENROUTER_API_KEY = sk-or-... ``` Pick any model via `model`. You pay OpenRouter directly for model + web-search usage; the actor's PPE events cover the enrichment layer. *** ### Pricing (Pay Per Event) | Event | When | |---|---| | `actor-start` | Once per run | | `lead-enriched` | Each lead successfully enriched | Leads that fail (missing key, model error) are **not charged** — but still appear in the dataset with an `error` so you know which ones to retry. *** ### Use cases - **Outbound sales** — enrich a prospect list and get tailored openers in one run. - **CRM hygiene** — fill missing firmographics on existing records. - **Lead scoring / routing** — rank inbound leads by ICP fit before they hit a rep. - **Agent pipelines** — call via the Apify MCP server to enrich leads on demand. *** ### Tips - More input context (domain + role + notes) → better, higher-confidence enrichment. - Write a specific `icpDescription` — vague profiles produce vague scores. - For large lists, raise `maxLeads` and schedule runs; keep `webSearchResults` low to control cost. # Actor input Schema ## `leads` (type: `array`): The leads to enrich. Each item is an object with any of: company, domain, fullName, role, location, notes. Provide whatever you have — the more context, the better the enrichment. Example: \[{"company":"Apify","domain":"apify.com"},{"fullName":"Jane Doe","company":"Acme","role":"VP Sales"}]. ## `icpDescription` (type: `string`): Describe your ideal customer (industry, size, role, region, pain points). The actor scores each lead's fit against this and explains why. Leave empty to skip ICP scoring. ## `offerContext` (type: `string`): What you're selling or why you're reaching out. Used to write a short, personalized outreach opener tailored to each lead. Leave empty to skip opener generation. ## `model` (type: `string`): Internal: OpenRouter model used for enrichment. Managed by the actor; not user-editable. ## `useWebSearch` (type: `boolean`): Ground enrichment in live web results via OpenRouter's web plugin. Strongly recommended for accurate firmographics. Turn off to rely on the model's built-in knowledge only (cheaper, less current). ## `webSearchResults` (type: `integer`): How many web results to fetch per lead when web search is on (more = better data, higher cost). ## `maxLeads` (type: `integer`): Safety cap on how many leads to enrich per run (cost control). Extra leads beyond this limit are ignored. ## Actor input object example ```json { "leads": [ { "company": "Apify", "domain": "apify.com" }, { "company": "Vercel", "domain": "vercel.com" } ], "icpDescription": "B2B SaaS companies with 50-1000 employees that do web scraping, data extraction, or AI automation; decision makers in engineering or data roles.", "offerContext": "We provide a managed proxy and data-pipeline platform that cuts scraping infrastructure costs by 40%.", "model": "openai/gpt-4o-mini", "useWebSearch": true, "webSearchResults": 3, "maxLeads": 50 } ``` # Actor output Schema ## `results` (type: `string`): Link to the dataset containing all enriched leads. # 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 = { "leads": [ { "company": "Apify", "domain": "apify.com" }, { "company": "Vercel", "domain": "vercel.com" } ], "icpDescription": "B2B SaaS companies with 50-1000 employees that do web scraping, data extraction, or AI automation; decision makers in engineering or data roles.", "offerContext": "We provide a managed proxy and data-pipeline platform that cuts scraping infrastructure costs by 40%." }; // Run the Actor and wait for it to finish const run = await client.actor("muhammadafzal/ai-lead-enrichment").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 = { "leads": [ { "company": "Apify", "domain": "apify.com", }, { "company": "Vercel", "domain": "vercel.com", }, ], "icpDescription": "B2B SaaS companies with 50-1000 employees that do web scraping, data extraction, or AI automation; decision makers in engineering or data roles.", "offerContext": "We provide a managed proxy and data-pipeline platform that cuts scraping infrastructure costs by 40%.", } # Run the Actor and wait for it to finish run = client.actor("muhammadafzal/ai-lead-enrichment").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 '{ "leads": [ { "company": "Apify", "domain": "apify.com" }, { "company": "Vercel", "domain": "vercel.com" } ], "icpDescription": "B2B SaaS companies with 50-1000 employees that do web scraping, data extraction, or AI automation; decision makers in engineering or data roles.", "offerContext": "We provide a managed proxy and data-pipeline platform that cuts scraping infrastructure costs by 40%." }' | apify call muhammadafzal/ai-lead-enrichment --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=muhammadafzal/ai-lead-enrichment", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Lead Enrichment — Firmographics, ICP Score & Outreach", "description": "Enrich a list of companies, domains, or people with firmographics, role data, an ICP-fit score, and a personalized outreach line — powered by an LLM with live web search. Turn raw lists into sales-ready leads.", "version": "1.0", "x-build-id": "GphrLrV1fAPURmwnC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/muhammadafzal~ai-lead-enrichment/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-muhammadafzal-ai-lead-enrichment", "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/muhammadafzal~ai-lead-enrichment/runs": { "post": { "operationId": "runs-sync-muhammadafzal-ai-lead-enrichment", "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/muhammadafzal~ai-lead-enrichment/run-sync": { "post": { "operationId": "run-sync-muhammadafzal-ai-lead-enrichment", "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", "properties": { "leads": { "title": "Leads to Enrich", "type": "array", "description": "The leads to enrich. Each item is an object with any of: company, domain, fullName, role, location, notes. Provide whatever you have — the more context, the better the enrichment. Example: [{\"company\":\"Apify\",\"domain\":\"apify.com\"},{\"fullName\":\"Jane Doe\",\"company\":\"Acme\",\"role\":\"VP Sales\"}].", "default": [ { "company": "Apify", "domain": "apify.com" }, { "company": "Vercel", "domain": "vercel.com" } ] }, "icpDescription": { "title": "Ideal Customer Profile (ICP)", "type": "string", "description": "Describe your ideal customer (industry, size, role, region, pain points). The actor scores each lead's fit against this and explains why. Leave empty to skip ICP scoring.", "default": "B2B SaaS companies with 50-1000 employees that do web scraping, data extraction, or AI automation; decision makers in engineering or data roles." }, "offerContext": { "title": "Your Offer / Outreach Context", "type": "string", "description": "What you're selling or why you're reaching out. Used to write a short, personalized outreach opener tailored to each lead. Leave empty to skip opener generation.", "default": "We provide a managed proxy and data-pipeline platform that cuts scraping infrastructure costs by 40%." }, "model": { "title": "Model", "type": "string", "description": "Internal: OpenRouter model used for enrichment. Managed by the actor; not user-editable.", "default": "openai/gpt-4o-mini" }, "useWebSearch": { "title": "Use Web Search", "type": "boolean", "description": "Ground enrichment in live web results via OpenRouter's web plugin. Strongly recommended for accurate firmographics. Turn off to rely on the model's built-in knowledge only (cheaper, less current).", "default": true }, "webSearchResults": { "title": "Web Search Results", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many web results to fetch per lead when web search is on (more = better data, higher cost).", "default": 3 }, "maxLeads": { "title": "Max Leads", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Safety cap on how many leads to enrich per run (cost control). Extra leads beyond this limit are ignored.", "default": 50 } } }, "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 } } } } } } } } } } ```