# Tool: Contact Enrichment (`scrapers_lat/contact-enrichment-tool`) Actor
Enrich B2B contacts with public work email, job title, seniority, LinkedIn, location and company domain using premium AI research. Paid Apify plans only.
- **URL**: https://apify.com/scrapers\_lat/contact-enrichment-tool.md
- **Developed by:** [Scrapers Lat](https://apify.com/scrapers_lat) (community)
- **Categories:** Lead generation, Business, AI
- **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $40.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
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
[](https://console.apify.com/actors/contact-enrichment-tool)
## Tool: Contact Enrichment
> Turn a name and a company into a complete B2B contact. Find public work emails and enrich each contact with job title, seniority, LinkedIn, location and company domain using premium AI research.
Email + status verified, likely, guessed
Enrichment title, seniority, LinkedIn
JSON / CSV / Excel output formats
### What it does
Give it a list of contacts with a company name (and a person name or domain when you have them). For each contact it runs deep AI research over public sources and returns a clean, structured record:
- **email** and **emailStatus** (verified, likely, guessed, not_found)
- **jobTitle** and **seniority**
- **linkedinUrl**
- **location** and **companyDomain**
- **source** URL and a **confidence** level for every match
Feed the results straight into a CRM, a cold email tool or a spreadsheet. Export to JSON, CSV or Excel, or pull it through the Apify API.
### Paid plans only
This tool runs on the latest, most reliable premium AI models. Because each run has a real model cost, it is available to paid Apify accounts only. A run started from a free account stops immediately with an upgrade message. Upgrade your Apify subscription to a paid plan and run it again.
### Who is it for
| Use case | Who benefits |
|---|---|
| Outbound sales | SDRs building targeted contact lists |
| Recruiting | Recruiters reaching candidates and hiring managers |
| Agencies | Teams enriching client prospect lists at scale |
| RevOps | Filling gaps in CRM records with fresh public data |
### Input
- **contacts**: a list of items, each with a `company` (and optionally `name` and `domain`).
- **findFields**: which data points to research (email, job title, seniority, LinkedIn, location, company domain).
- **maxItems**: optional cap on how many contacts to enrich this run.
### Notes
- Only publicly available business data is researched. Use the results in line with applicable laws and each source's terms.
- Email status tells you how confident the match is, so you can verify before sending.
### Example use cases
Ready-to-run example tasks, each preconfigured for a common scenario. Open one and press run, or use it as a template:
- [Enrich company contacts for Stripe](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-stripe): Enrich Stripe with company contact data including email formats, seniority, location and LinkedIn links as JSON.
- [Enrich company contacts for Ramp](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-ramp): Enrich Ramp company records with email patterns, job titles, seniority and location fields returned as clean JSON.
- [Enrich company contacts for Notion](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-notion): Enrich Notion with contact details such as email, title, location and LinkedIn links exported as structured JSON.
- [Enrich company contacts for Figma](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-figma): Enrich Figma company data with email formats, seniority and job titles from its domain, delivered as JSON records.
- [Enrich company contacts for Airtable](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-airtable): Enrich Airtable with email patterns, titles, location and company domain fields returned in clean structured JSON.
- [Enrich company contacts for Vercel](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-vercel): Enrich Vercel company records with email, seniority, LinkedIn and location fields exported as structured JSON.
- [Enrich company contacts for Linear](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-linear): Enrich Linear with email formats, job titles and seniority pulled from its company domain and returned as JSON.
- [Enrich company contacts for Datadog](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-datadog): Enrich Datadog company data with email, title, location and LinkedIn fields from its domain, delivered as JSON.
- [Enrich company contacts for Snowflake](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-snowflake): Enrich Snowflake with email patterns, seniority and company domain fields returned as clean and consistent structured JSON.
- [Enrich company contacts for Brex](https://apify.com/scrapers_lat/contact-enrichment-tool/examples/cenr-enrich-brex): Enrich Brex company records with email, title, seniority, location and LinkedIn fields exported as structured JSON.
### Export, API and AI agents (x402 + MCP)
Export the scraped data to **JSON, CSV or Excel**, pull it as a **dataset** through the Apify **API**, or wire it into your app with **no code**. This web scraper and data extractor also works for bulk data extraction and scheduled runs.
For AI agents: this Actor is available on **x402**, Apify's agentic payment standard built with Coinbase. An AI agent can discover, pay for and run it on its own with a funded wallet and a single HTTP request: no account, no subscription, no API key and no human in the loop. It also runs as an **MCP** tool inside Claude, Cursor and other AI clients out of the box. Learn more about [x402 agentic payments on Apify](https://docs.apify.com/platform/integrations/x402).
Built by [scrapers.lat](https://scrapers.lat).
# Actor input Schema
## `contacts` (type: `array`):
A list of contacts to research. Each item can include a person name, a company name and a company domain. The more you provide, the better the match.
## `findFields` (type: `array`):
Which public data points to research for each contact.
## `maxItems` (type: `integer`):
Cap how many contacts to enrich this run. Optional.
## Actor input object example
```json
{
"contacts": [
{
"name": "",
"company": "Stripe",
"domain": "stripe.com"
}
],
"findFields": [
"email",
"jobTitle",
"seniority",
"linkedinUrl",
"location",
"companyDomain"
]
}
````
# Actor output Schema
## `results` (type: `string`):
No description
# 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 = {
"contacts": [
{
"name": "",
"company": "Stripe",
"domain": "stripe.com"
}
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapers_lat/contact-enrichment-tool").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 = { "contacts": [{
"name": "",
"company": "Stripe",
"domain": "stripe.com",
}] }
# Run the Actor and wait for it to finish
run = client.actor("scrapers_lat/contact-enrichment-tool").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 '{
"contacts": [
{
"name": "",
"company": "Stripe",
"domain": "stripe.com"
}
]
}' |
apify call scrapers_lat/contact-enrichment-tool --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapers_lat/contact-enrichment-tool",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Tool: Contact Enrichment",
"description": "Enrich B2B contacts with public work email, job title, seniority, LinkedIn, location and company domain using premium AI research. Paid Apify plans only.",
"version": "0.1",
"x-build-id": "cX9JhuCtCnXCabTDQ"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapers_lat~contact-enrichment-tool/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapers_lat-contact-enrichment-tool",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapers_lat~contact-enrichment-tool/runs": {
"post": {
"operationId": "runs-sync-scrapers_lat-contact-enrichment-tool",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapers_lat~contact-enrichment-tool/run-sync": {
"post": {
"operationId": "run-sync-scrapers_lat-contact-enrichment-tool",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"contacts"
],
"properties": {
"contacts": {
"title": "Contacts to enrich",
"type": "array",
"description": "A list of contacts to research. Each item can include a person name, a company name and a company domain. The more you provide, the better the match."
},
"findFields": {
"title": "Fields to find",
"type": "array",
"description": "Which public data points to research for each contact.",
"items": {
"type": "string",
"enum": [
"email",
"jobTitle",
"seniority",
"linkedinUrl",
"location",
"companyDomain"
],
"enumTitles": [
"Email",
"Job title",
"Seniority",
"LinkedIn URL",
"Location",
"Company domain"
]
},
"default": [
"email",
"jobTitle",
"seniority",
"linkedinUrl",
"location",
"companyDomain"
]
},
"maxItems": {
"title": "Max contacts",
"minimum": 1,
"maximum": 100000,
"type": "integer",
"description": "Cap how many contacts to enrich this run. Optional."
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```