# Domain Lead Scoring and ICP Enrichment (`iammienta/agent-website-lead-score-enricher`) Actor Score company websites against an ideal customer profile and return structured lead qualification data. Built for AI sales agents: fit score, fit band, reasons, disqualifiers, evidence, company profile, and next action. - **URL**: https://apify.com/iammienta/agent-website-lead-score-enricher.md - **Developed by:** [Pete Mientkiewicz](https://apify.com/iammienta) (community) - **Categories:** AI, Agents, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 ## Domain Lead Scoring and ICP Enrichment Domain Lead Scoring and ICP Enrichment scores company websites against your ideal customer profile and returns agent-ready lead qualification data. It is built for AI sales agents, prospecting workflows, CRM enrichment, and outbound prioritisation when you have domains but need fit scores, reasons, disqualifiers, website evidence, and a recommended next action. ### Use cases - Score a list of domains before importing leads into a CRM. - Prioritise outbound lists by ICP fit. - Give an AI sales agent structured lead qualification context. - Add evidence-backed fit reasons and disqualifiers to prospect records. - Build a lightweight enrichment step before deeper paid enrichment. ### Use this Actor when Use this Actor when you already have one or more company domains and a short ICP description, and you want a first-pass lead score with transparent reasons. ### Do not use this Actor when Do not use it as the sole source of truth for buying intent, revenue, headcount, funding, or private company facts. It scores public website signals only. ### Input ```json { "domains": [ "https://scaleupsystems.co" ], "icpDescription": "B2B companies selling services or software, with clear growth or lead generation needs.", "maxPagesPerDomain": 2 } ```` ### Output The Actor writes one record per domain to the default dataset. Typical fields include: - `domainOrUrl` - `fitScore` - `fitBand` - `reasons` - `disqualifiers` - `suggestedNextAction` - `profile` - `profile.evidencePages` - `profile.contactPaths` - `profile.confidence` ### Agent workflow 1. Pass a small batch of domains and an ICP description. 2. Read scored records from the default dataset. 3. Route `high` fit leads to outreach, `medium` fit leads to manual review, and `low` fit leads to deprioritisation. 4. Use `reasons` and `disqualifiers` to explain the decision in downstream automations. ### Limitations - Scoring is based on website text and visible contact/conversion paths. - Thin or blocked websites can produce low-confidence results. - The score should guide prioritisation, not replace human sales judgement. ### Pricing and runtime This MVP uses bounded website fetches and compact JSON output. Keep batches small for agent workflows; increase only after validating result quality. # Actor input Schema ## `domains` (type: `array`): Domains or URLs to score against the ICP. ## `icpDescription` (type: `string`): Short description of the ideal customer profile to score against. ## `maxPagesPerDomain` (type: `integer`): Maximum pages to fetch per domain. ## Actor input object example ```json { "domains": [ "https://scaleupsystems.co" ], "icpDescription": "B2B companies selling services or software, with clear growth or lead generation needs.", "maxPagesPerDomain": 2 } ``` # Actor output Schema ## `results` (type: `string`): Structured results in the default dataset. # 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": [ "https://scaleupsystems.co" ], "icpDescription": "B2B companies selling services or software, with clear growth or lead generation needs." }; // Run the Actor and wait for it to finish const run = await client.actor("iammienta/agent-website-lead-score-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 = { "domains": ["https://scaleupsystems.co"], "icpDescription": "B2B companies selling services or software, with clear growth or lead generation needs.", } # Run the Actor and wait for it to finish run = client.actor("iammienta/agent-website-lead-score-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 '{ "domains": [ "https://scaleupsystems.co" ], "icpDescription": "B2B companies selling services or software, with clear growth or lead generation needs." }' | apify call iammienta/agent-website-lead-score-enricher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iammienta/agent-website-lead-score-enricher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Domain Lead Scoring and ICP Enrichment", "description": "Score company websites against an ideal customer profile and return structured lead qualification data. Built for AI sales agents: fit score, fit band, reasons, disqualifiers, evidence, company profile, and next action.", "version": "0.1", "x-build-id": "oAhabWikgdGbmeHPi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iammienta~agent-website-lead-score-enricher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iammienta-agent-website-lead-score-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/iammienta~agent-website-lead-score-enricher/runs": { "post": { "operationId": "runs-sync-iammienta-agent-website-lead-score-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/iammienta~agent-website-lead-score-enricher/run-sync": { "post": { "operationId": "run-sync-iammienta-agent-website-lead-score-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": [ "domains", "icpDescription" ], "properties": { "domains": { "title": "Domains or URLs", "minItems": 1, "maxItems": 25, "type": "array", "description": "Domains or URLs to score against the ICP.", "items": { "type": "string" } }, "icpDescription": { "title": "ICP description", "type": "string", "description": "Short description of the ideal customer profile to score against." }, "maxPagesPerDomain": { "title": "Max pages per domain", "minimum": 1, "maximum": 5, "type": "integer", "description": "Maximum pages to fetch per domain.", "default": 2 } } }, "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 } } } } } } } } } } ```