Built In Tech Companies Scraper

Extract structured company profiles from Built In — the leading tech company directory. Get company name, HQ location, industries, tech stack, employee count, website, and more — all in one clean JSON dataset.

🏢 What does it do?

This actor crawls Built In's company directory and profile pages to extract comprehensive company data. For each company, it returns:

• Company identity: name, slug, profile URL, website, logo
• Location data: HQ city, state, country, street address, zip code
• Headcount: total employee count
• Industries: all industry tags (Cloud, Software, FinTech, etc.)
• Tech stack: every technology listed on the company profile (programming languages, frameworks, tools, SaaS products)
• Jobs link: direct URL to the company's open positions on Built In

You can scrape the full global directory (1,000+ companies), filter by city (Austin, Boston, Chicago, NYC, SF, Seattle, and 80+ other locations), or supply a list of specific company URLs.

👥 Who is it for?

B2B Sales & Outreach Teams — Build targeted prospect lists filtered by tech stack, location, and headcount. Know which CRM a company uses before your first call.

Technical Recruiters — Find companies using the specific technologies your candidates know. Filter Austin-based companies with 200–500 employees using React and AWS.

Market Intelligence Analysts — Track tech adoption across the industry. Which frameworks are gaining share in NYC fintech? Built In's tech stack data answers that.

Competitive Intelligence — Monitor industry peers, benchmark headcount trends, and map the technology landscape of a vertical.

Data Enrichment Pipelines — Augment your CRM or data warehouse with fresh company profiles and tech stacks at scale.

✅ Why use this actor?

Built In is a curated, high-quality directory of tech companies that self-report their stack and culture. Unlike LinkedIn or Crunchbase:

• Tech stack is explicit: Companies list their actual technologies, not inferred from job postings.
• Location is precise: Built In tracks city-level presence across 80+ US metros and international hubs.
• Data is self-maintained: Companies update their profiles to attract talent, so data is current.
• No login required: All data is publicly accessible.

📦 Data extracted per company

Field	Type	Description
name	string	Company name
slug	string	URL slug (e.g. salesforce)
profileUrl	string	Full Built In profile URL
websiteUrl	string	Company's own website
logoUrl	string	Company logo image URL
description	string	Company mission/about text
city	string	HQ city
state	string	HQ state/region
country	string	HQ country code
streetAddress	string	Street address
zipCode	string	Postal code
totalEmployees	number	Employee headcount
industries	array	Industry tags (Cloud, Software, FinTech, etc.)
techStack	array	Technology names used by the company
jobsUrl	string	Link to open jobs on Built In

💰 How much does it cost to scrape Built In company profiles?

This actor uses Pay-Per-Event pricing. You are charged:

• $0.005 per run (one-time start fee)
• $0.003 per company profile (BRONZE tier — see volume discounts below)

Cost examples:

Companies scraped	Estimated cost
20 (test run)	~$0.07
100	~$0.31
500	~$1.51
1,000	~$3.01
5,000	~$9.01 (GOLD discount)

Volume discounts are applied automatically based on your Apify subscription:

Tier	Per-company price
FREE	$0.00345
BRONZE	$0.003
SILVER	$0.00234
GOLD	$0.0018
PLATINUM	$0.0012
DIAMOND	$0.00084

🚀 How to use it

Option 1: Scrape all companies in a city

1. Open the actor on Apify
2. Set Location to a city slug (e.g. austin, boston, chicago, new-york, san-francisco, seattle, los-angeles, denver, atlanta)
3. Set Max companies to your desired limit
4. Click Start

Option 2: Scrape specific company profiles

1. Add company profile URLs to Company or directory URLs (e.g. https://builtin.com/company/stripe)
2. Set Max companies to the number of profiles to scrape
3. Click Start

Option 3: Scrape from a directory page

1. Add a Built In directory URL to Company or directory URLs (e.g. https://builtin.com/companies/location/austin)
2. The actor will paginate through all results automatically

Option 4: Global company list

1. Leave Location and Company or directory URLs empty
2. Set Max companies to your desired limit
3. The actor will crawl the global directory

📥 Input parameters

Parameter	Type	Default	Description
startUrls	array	[]	Company profile or directory URLs to scrape
location	string	""	Built In city slug (e.g. austin)
maxCompanies	number	100	Max company profiles to extract
maxRequestRetries	number	3	Retries for failed requests

Location slugs (examples): albuquerque, atlanta, austin, baltimore, boston, charlotte, chicago, colorado, dallas, dc, denver, detroit, houston, indianapolis, los-angeles, miami, minneapolis, nashville, new-york, orlando, philadelphia, pittsburgh, portland, raleigh, sacramento, san-antonio, san-diego, san-francisco, seattle, st-louis, tampa, tucson

📤 Output example

{
  "name": "Stripe",
  "slug": "stripe",
  "profileUrl": "https://builtin.com/company/stripe",
  "websiteUrl": "https://stripe.com",
  "logoUrl": "https://builtin.com/sites/www.builtin.com/files/2021-10/stripe.jpeg",
  "description": "Stripe is a technology company that builds economic infrastructure for the internet...",
  "city": "Dublin",
  "state": "Dublin",
  "country": "IE",
  "streetAddress": "Grand Canal Street Lower",
  "zipCode": null,
  "totalEmployees": 5360,
  "industries": ["Payments", "Software"],
  "techStack": ["React", "Ruby", "Python", "Go", "AWS", "Kafka", "PostgreSQL"],
  "jobsUrl": "https://builtin.com/jobs?companyId=84805&allLocations=true"
}

💡 Tips & best practices

Filter by tech stack after export: The actor returns the full tech stack array. Use a spreadsheet or Apify's dataset filter to find companies using React, Kubernetes, Salesforce, etc.

Combine location + max companies: Start with a city like new-york and maxCompanies: 500 to get a rich B2B prospect list.

Enrich existing lists: Use startUrls to re-scrape specific companies for regular data refresh.

Rate limiting: The actor uses Built In's residential proxy and respects rate limits with delays between requests. Large runs (1,000+ companies) may take 30-60 minutes.

Empty fields: Some companies don't list all data. techStack may be empty for non-tech companies. streetAddress and zipCode are optional.

🔗 Integrations

Outreach sequence automation: Export the dataset to Google Sheets → enrich with Clay → push to HubSpot sequences for tech-stack-targeted outreach.

Talent sourcing pipeline: Combine with Built In Jobs Scraper to correlate company profiles with open roles — recruit at companies where Python + ML engineers are hiring.

Market map tracking: Schedule weekly runs for a list of competitor URLs. Track headcount changes over time in Airtable or a data warehouse.

Sales intelligence enrichment: Import into Salesforce or HubSpot → match against existing accounts → fill missing techStack and totalEmployees fields on existing CRM records.

CRM data refresh: Use the Apify REST API to trigger runs on-demand when your SDR team needs fresh company data before a campaign launch.

🖥️ API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('automation-lab/builtin-companies-scraper').call({
    location: 'austin',
    maxCompanies: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);
// { name: 'SailPoint', city: 'Austin', techStack: ['Angular', 'AWS', ...], ... }

Python

from apify_client import ApifyClient

client = ApifyClient(token='YOUR_APIFY_TOKEN')

run = client.actor('automation-lab/builtin-companies-scraper').call(run_input={
    'location': 'boston',
    'maxCompanies': 200,
})

dataset = client.dataset(run['defaultDatasetId'])
for item in dataset.iterate_items():
    print(item['name'], item.get('techStack', []))

cURL

curl -s -X POST \
  "https://api.apify.com/v2/acts/automation-lab~builtin-companies-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"location": "chicago", "maxCompanies": 50}' | jq '.data.id'

🤖 MCP (Claude / AI Assistant Integration)

Use this actor directly inside Claude Code, Claude Desktop, or any MCP-compatible AI tool.

Claude Code:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/builtin-companies-scraper"

Claude Desktop / Cursor / VS Code — add to your MCP config:

{
  "mcpServers": {
    "apify": {
      "type": "http",
      "url": "https://mcp.apify.com?tools=automation-lab/builtin-companies-scraper",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Example prompts once connected:

• "Scrape the top 50 tech companies in Austin and export to a spreadsheet"
• "Find all Built In companies using Kubernetes and Terraform"
• "Get company profiles for these 10 Built In URLs: [list]"
• "What's the average headcount of San Francisco tech companies on Built In?"

⚖️ Legal & compliance

Built In's company profiles are publicly accessible without authentication. This actor:

• Accesses only public pages available to any anonymous visitor
• Does not bypass login, paywalls, or any access controls
• Does not collect personal data about individuals
• Respects robots.txt and uses reasonable request delays
• Is intended for commercial B2B data use cases (market research, sales intelligence, talent sourcing)

Users are responsible for ensuring their use of scraped data complies with applicable laws (GDPR, CCPA) and their end-use terms. For personal data processing at scale, consult your legal team.

❓ FAQ

Q: How many companies are on Built In?

Built In lists thousands of tech companies globally. The main directory has 100+ pages of 20 companies each, and location-specific pages add depth. The Austin directory alone has 141 pages. With maxCompanies set high, you can collect thousands of profiles.

Q: Is the tech stack data reliable?

Companies self-report their tech stacks to attract talent. Data quality is high for companies that actively maintain their Built In profile, but some smaller or less-active companies may have empty tech stacks.

Q: Why are some companies missing location data?

Built In displays location data based on what companies have entered in their profiles. Some companies (especially those with remote-first or international structures) may not specify a primary HQ address.

Q: The run succeeded but I got fewer companies than expected. Why?

This can happen when the location has fewer companies than your maxCompanies limit, or when some profile pages return temporary errors. Check the actor logs for any skipped URLs.

Q: Can I scrape a specific Built In city page like /companies/location/chicago?

Yes — paste https://builtin.com/companies/location/chicago into the Company or directory URLs field, and the actor will paginate through all companies in that city directory.

Q: How often should I re-run for fresh data?

For prospect list refreshes, monthly runs are usually sufficient. For headcount tracking or competitive intelligence, weekly runs give better signal.

🔗 Related scrapers

• Built In Jobs Scraper — scrape open job listings from Built In
• LinkedIn Company Scraper — LinkedIn company profiles
• Crunchbase Scraper — startup funding & investor data