# AI Lead Qualifier — Score Any Domain List Against Your ICP (`midaslabs/lead-qualifier`) Actor Paste domains + describe your ideal customer. Get back fit scores 0-100 with reasons, plus full company profiles (industry, products, contacts, socials). Pay per qualified domain. - **URL**: https://apify.com/midaslabs/lead-qualifier.md - **Developed by:** [Midas Labs](https://apify.com/midaslabs) (community) - **Categories:** Lead generation, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $80.00 / 1,000 domain qualifieds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Qualifier — Score Any Domain List Against Your ICP Stop manually eyeballing lead lists. Paste company domains, describe your ideal customer in plain language, and get back a **0-100 fit score for every domain** — with concrete reasons and disqualifiers, plus a full company profile. No API keys, no scraping setup. ### How it works A frontier LLM reads each company's website and answers two questions: 1. **What is this company?** — name, description, industry, business model, products, contacts, socials 2. **Should you contact them?** — fit score against *your* ICP, with reasons grounded in what's actually on their site ### Input ```json { "domains": ["stripe.com", "notion.so", "some-small-agency.com"], "icp_description": "B2B SaaS companies, 10-200 employees, selling to sales or marketing teams, based in North America or Europe." } ```` Protocol prefixes and paths are stripped automatically; duplicates removed. Leave `icp_description` empty for enrichment only (no scoring). ### Output One dataset item per domain, sortable by `fit_score`: ```json { "domain": "notion.so", "ok": true, "fit_score": 78, "fit_reasons": [ "B2B SaaS product with team collaboration focus", "Sells to knowledge-work teams including marketing" ], "disqualifiers": [ "Larger than 200 employees" ], "company_name": "Notion", "description": "Notion provides a connected workspace for docs, wikis and projects...", "industry": "Software / productivity", "business_model": "b2b", "products_services": ["Docs", "Wikis", "Projects", "Notion AI"], "emails": [], "social_links": ["https://twitter.com/notionhq"], "tech_hints": [] } ``` Unreachable or broken sites produce `{ "domain": ..., "ok": false, "error": ... }` and are **not charged**. ### Pricing Pay per event: one `domain-qualified` charge per successfully processed domain. Failed domains are free. ### Use cases - Score inbound signups before routing to sales - Prioritize cold outreach lists — call the 80s first, skip the 20s - Clean bought lead lists before importing to CRM - Territory / market research with built-in qualification ### Why scores you can trust Scoring is grounded in site text only — the model must cite concrete `fit_reasons` and `disqualifiers` from the website, not guess. Companies whose sites say nothing relevant score low with an explanation, not a hallucinated high score. # Actor input Schema ## `domains` (type: `array`): Company website domains to qualify, e.g. stripe.com (no protocol needed). ## `icp_description` (type: `string`): Describe your ideal customer in plain language — industry, size, business model, tech, geography. Each domain gets a 0-100 fit score against this. Leave empty for enrichment only (no scoring). ## Actor input object example ```json { "domains": [ "anthropic.com" ], "icp_description": "B2B SaaS companies, 10-200 employees, selling to sales or marketing teams, based in North America or Europe." } ``` # 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 = { "domains": [ "anthropic.com" ], "icp_description": "B2B SaaS companies, 10-200 employees, selling to sales or marketing teams, based in North America or Europe." }; // Run the Actor and wait for it to finish const run = await client.actor("midaslabs/lead-qualifier").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 = { "domains": ["anthropic.com"], "icp_description": "B2B SaaS companies, 10-200 employees, selling to sales or marketing teams, based in North America or Europe.", } # Run the Actor and wait for it to finish run = client.actor("midaslabs/lead-qualifier").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 '{ "domains": [ "anthropic.com" ], "icp_description": "B2B SaaS companies, 10-200 employees, selling to sales or marketing teams, based in North America or Europe." }' | apify call midaslabs/lead-qualifier --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=midaslabs/lead-qualifier", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Lead Qualifier — Score Any Domain List Against Your ICP", "description": "Paste domains + describe your ideal customer. Get back fit scores 0-100 with reasons, plus full company profiles (industry, products, contacts, socials). Pay per qualified domain.", "version": "0.1", "x-build-id": "YEfg8OOeKThVnUPUE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/midaslabs~lead-qualifier/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-midaslabs-lead-qualifier", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/midaslabs~lead-qualifier/runs": { "post": { "operationId": "runs-sync-midaslabs-lead-qualifier", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/midaslabs~lead-qualifier/run-sync": { "post": { "operationId": "run-sync-midaslabs-lead-qualifier", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "domains" ], "properties": { "domains": { "title": "Domains", "type": "array", "description": "Company website domains to qualify, e.g. stripe.com (no protocol needed).", "items": { "type": "string" } }, "icp_description": { "title": "Ideal customer profile", "type": "string", "description": "Describe your ideal customer in plain language — industry, size, business model, tech, geography. Each domain gets a 0-100 fit score against this. Leave empty for enrichment only (no scoring)." } } }, "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 } } } } } } } } } } ```