Built In Startup Scraper — Tech Stack, Industries, HQ 💻

foXLabs company-intelligence series: Company enrichment · Owler · Craft · Free lookup

🎉 Turn Built In company profiles into structured JSON — industries, tech stack, HQ, founded year, employee count, description, benefit categories and the number of jobs shown on the profile, one row per company. Built for tech-sector prospecting, recruiting research and technology targeting.

🔍 What is Built In Startup Scraper — and when should you use it?

Give this Actor company names or Built In URLs — or pick a sector preset — and it returns one record per company from the public company profiles on Built In, a directory of tech companies and startups. A preset starts from a list of anchor companies and can also page through the sector's Built In directory to find more. Every run reads the pages live.

Use it when you need: a prospect list of tech companies with HQ, size and industries; the technologies a company shows on its Built In profile; a sector sweep (SaaS, fintech, AI/ML, eCommerce, gaming, crypto) from Built In's directory.

Use something else when: a company has no Built In profile (its name then returns an HTTP 404 error row), or you need revenue, funding or competitors — Owler Scraper returns those.

🤖 Use with AI agents

Already on the Apify MCP server? Ask for this Actor by name: foxlabs/builtin-intelligence.

Your agent can pay for its own runs. This Actor is pay-per-event with agentic payments, so an agent can discover it, run it and settle the bill over x402 (USDC on Base) or Skyfire — no Apify account or API token of its own. Billing is the same either way: per delivered company record — rows that only report a failed lookup are free from 25 September 2026.

Otherwise paste this into Claude, ChatGPT, Cursor or any MCP-enabled assistant:

I want tech-company profiles from the Apify Actor `foxlabs/builtin-intelligence`.
Input: `companyNames` is a list of company names, each turned into a Built In URL (lowercased, spaces replaced by hyphens — no search); `companyUrls` takes exact Built In company URLs; or set `preset` to a sector (saas, fintech, ai-ml, ecommerce, gaming, crypto-web3), where `presetDepth` 0 keeps to its anchor companies and 1 or more also reads the sector's Built In directory. `maxResults` caps the rows.
Start with: {"companyNames":["Notion","Datadog","Asana"],"maxResults":100}
Ask me what to look up, run the Actor, then summarise the rows as a table.

The machine-readable API, MCP config and OpenAPI definition live at apify.com/foxlabs/builtin-intelligence.md.

📋 Overview

Everything you need to turn Built In profiles into structured data — in one Actor, with no login, cookies or API key.

Why teams pick this Actor:

• ✅ Names, URLs or a sector preset — names become Built In URLs directly; presets start from 12–23 anchor companies per sector.
• 🛠️ Tech stack — the technologies shown on the company's Built In profile (52 for Asana and 73 for Datadog in our test run).
• 🏢 Firmographics — HQ city, state, country and postal code, founded year, employee count, industry tags.
• 🧭 Directory discovery — with a preset, the sector's Built In directory pages are read to find companies beyond the anchors.
• 💰 Pay per record — $0.004 per company record, no subscription.
• 🤖 Agent-ready — MCP + x402 agentic payments.

✨ Features

• 🔍 Three ways in — companyNames, companyUrls (Built In company URLs or a .txt / .csv upload) and preset; they can be combined.
• 🧭 Six sector presets — anchor companies plus, with presetDepth 1–4, the companies listed on the sector's Built In directory pages (e.g. builtin.com/companies/type/fintech), kept when they match the sector's keywords.
• 🧾 List and text versions — industries / industriesText and techStack / techStackText, ready for CSV.
• 🚦 Misses are visible — a name or URL without a Built In page comes back as a row with error: HTTP 404.

🎬 Quick Start

curl -X POST "https://api.apify.com/v2/acts/foxlabs~builtin-intelligence/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"companyNames":["Notion","Datadog","Asana"],"maxResults":100}'

🚀 Getting Started (3 steps)

1. Choose companies — type names (they become Built In URLs), paste Built In URLs, or pick a sector preset.
2. Set the cap — maxResults limits the companies saved (default 100; 0 = no limit).
3. Run and export — download the dataset as JSON, CSV, Excel, HTML or XML.

📥 Input

{"companyNames":["Notion","Datadog","Asana"],"maxResults":100}

Field	Type	Description
preset	string	Start from a sector's anchor companies: saas (23), fintech (15), ai-ml (13), ecommerce (15), gaming (12), crypto-web3 (13). Combined with names and URLs when both are given.
presetDepth	integer	Preset only. 0 = only the anchor companies; 1–4 = also read the sector's Built In directory pages and add the companies listed there whose industries, tagline, description or HQ match the sector's keywords (every value from 1 to 4 behaves the same). Default 2.
companyNames	array	Company names, one per line. Each becomes https://builtin.com/company/<name> — lowercased, spaces replaced by hyphens, no search. A name without a Built In page returns an error: HTTP 404 row.
companyUrls	array	Built In company URLs (https://builtin.com/company/<slug>), or a .txt / .csv upload. URLs from other sites are ignored.
maxResults	integer	Cap on companies saved (default 100; 0 = no limit). Without a preset only the first maxResults companies are fetched; with a preset, pages already in progress still finish, so the count can end slightly above the cap.
maxConcurrency	integer	Pages fetched in parallel (default 10, max 50).
maxListingPages	integer	Discovery only — pages of each sector directory to read (default 30, max 100). Also limited to ⌈maxResults ÷ 18⌉ + 1 pages, and to 30 pages when maxResults is 0.
proxyConfiguration	object	Apify proxy settings (default: Apify proxy, automatic selection).

More recipes

• Sector anchors only: {"preset":"fintech","presetDepth":0,"maxResults":2} — returned Stripe and Plaid (the first two fintech anchors) in our test run.
• Names without a Built In page stay visible: {"companyNames":["Notion","zzqx-no-such-company-2026"]} — Notion plus a row with error: HTTP 404.

📤 Output

One row per company. A name or URL without a Built In page comes back as a row with only url, error (e.g. HTTP 404) and scrapedAt; so does a page on which no company name was found, or one that could not be read. A page that still fails after its retries — blocked or server errors — is only logged, not written. From 25 September 2026 rows with error are not charged.

Field	Description
url / slug	Built In profile URL and its slug
name	Company name
description	The company's own description from Built In's structured company data (not the page's SEO text)
tagline	The one-line pitch under the industry tags; null when the profile has none
logo / website	Logo image URL and company website
headquarters	HQ as City, State, Country
headquartersCity / headquartersState / headquartersCountry / headquartersPostalCode	HQ in parts
yearFounded	Founding year
employees	Employee count Built In publishes for the company
industries / industriesText	Built In industry tags, as a list and comma-joined
techStack / techStackText	Technologies shown on the profile, as a list and comma-joined; empty when the profile shows none
perks	The benefit categories shown on the profile (e.g. Healthcare Benefits) — category headings, not individual benefits
openPositions	Job cards on the profile page — not the company's total number of openings (see FAQ); 0 when the profile shows none
scrapedAt	Timestamp of the scrape
error	Miss rows only — why the page could not be read

In our test runs (Asana, Datadog, Notion, Plaid, Stripe) every field was filled for all five companies except tagline and techStack / techStackText (3 of 5 each; Plaid and Stripe had neither).

Sample — Notion, from our test run on 2026-09-10 (lists trimmed to their first three entries):

{
  "url": "https://builtin.com/company/notion",
  "slug": "notion",
  "name": "Notion",
  "description": "Notion is a collaborative AI workspace. Teams use it to store knowledge, run projects, take meeting notes, and build AI workflows — all in one place, so AI always has the context it needs to be useful. We're building the layer where AI stops being a chat window and starts being part of how work gets done. Knowledge, decisions, and action in one connected system.",
  "tagline": "Notion is the AI workspace where teams and AI think together. Come build work that lasts.",
  "logo": "https://builtin.com/sites/www.builtin.com/files/2021-12/notion logo.jpeg",
  "website": "https://www.notion.so/product",
  "headquarters": "San Francisco, CA, US",
  "headquartersCity": "San Francisco",
  "headquartersState": "CA",
  "headquartersCountry": "US",
  "headquartersPostalCode": "94105",
  "yearFounded": 2016,
  "employees": 1000,
  "industries": [
    "Artificial Intelligence",
    "Productivity",
    "Software"
  ],
  "industriesText": "Artificial Intelligence, Productivity, Software",
  "techStack": [
    "Go",
    "JavaScript",
    "MySQL"
  ],
  "techStackText": "Go, JavaScript, MySQL, Perl, Python, Scala, Snowflake, SQL, TypeScript, Figma, Tableau, Salesforce, Slack, Zoom",
  "perks": [
    "Compensation + Total Rewards",
    "Diversity, Equity + Inclusion",
    "Employee Satisfaction + Recognition"
  ],
  "openPositions": 9,
  "scrapedAt": "2026-09-10T14:52:38.189Z"
}

💼 Use cases

1. Tech-sector prospecting — a target list without having a list. Input: a sector preset. Output: companies with industries, HQ, employee count and website. Use: a prospect list for outreach.

2. Recruiting and employer research — what a tech employer shows on Built In. Input: company names. Output: description, tagline, benefit categories, jobs shown on the profile, HQ. Use: brief candidates or clients.

3. Technology targeting — who shows a given technology. Input: a list of companies. Output: techStack. Use: filter for companies that show, say, Snowflake.

🔗 Integration

JavaScript / Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('foxlabs/builtin-intelligence').call({"companyNames":["Notion","Datadog","Asana"],"maxResults":100});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')
run = client.actor('foxlabs/builtin-intelligence').call(run_input={"companyNames": ["Notion", "Datadog", "Asana"], "maxResults": 100})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

Automation (n8n / Zapier / Make): schedule or webhook → HTTP request to the Actor API with your companyNames or preset → handle the JSON dataset → push to a sheet, CRM or dashboard.

📊 Pricing

Pay-per-event: $0.004 per delivered company record (≈ $4 per 1,000 companies), plus Apify's run-start event ($0.00005 per GB of run memory). With a preset, pages already in progress when maxResults is reached still finish, so a preset run can save — and charge for — slightly more companies than maxResults. From 25 September 2026, rows that only report a failed lookup (error) are free. Until then Apify still counts them as dataset items. Current pricing.

❓ FAQ

Where does the data come from? From Built In's public company profile pages (builtin.com/company/…): the structured company data embedded in each page plus the page itself. Presets with discovery also read Built In's sector directory pages (builtin.com/companies/type/…). No login is needed.

How are names matched? Without search: the name is lowercased and spaces become hyphens (Notion → builtin.com/company/notion). If a company's Built In address differs, paste its URL; check name and website in the output to confirm the page is the company you meant.

What does openPositions count? The job cards on the company's profile page — 9 for Datadog, Notion and Stripe and 0 for Asana and Plaid in our test runs. On Datadog's profile the full list sits behind "View all jobs", so the number is not the company's total openings.

What are perks? The benefit categories shown on the profile (for example Healthcare Benefits) — category headings, not individual benefits. Datadog and Notion had the same six categories in our test.

Can I export to CSV / Excel / JSON? Yes — directly from the Apify dataset.

🐛 Troubleshooting

• A name returns error: HTTP 404 — the company's Built In address differs from its name, or it has no profile; find it on builtin.com and paste the URL.
• The run fails with "No companies to scrape" — no names, no Built In URLs and no preset were given.
• techStack is empty — the profile shows no technologies (Stripe and Plaid in our test).
• A preset run returns fewer companies than expected — presetDepth 0 gives only the anchors; discovered companies are filtered by the sector's keywords, and maxListingPages and maxResults limit the directory pages read.

⚖️ Is it legal to scrape this data?

The Actor reads publicly accessible company profile pages and returns company information only — no personal profiles. Check that your use fits the source website's terms. Built In is a trademark of its owner; this Actor is not affiliated with Built In.

🤝 Support & contact

• 🌐 Website: data.foxlabs.com.tr
• 📧 Email: info@foxlabs.com.tr
• 🐛 Issues: open a ticket in the Actor’s Issues tab
• 🧰 More clean B2B data actors: Fox Labs on Apify

Changelog

0.1.29 — 2026-09-10 — failed lookups become free (per-record billing) + README corrected

• You will pay only for delivered company records. The Actor now charges a new company-record event for each delivered company, and rows that only report a failed lookup (error) are pushed without a charge.
• Timing: Apify applies pricing changes after a 14-day notice. From 25 September 2026 the pricing switches from the per-dataset-item event to company-record at the same price ($0.004). Until then billing is exactly as before; the code detects the switch by itself.
• No output field changed.
• README corrected. The 2026-09-07 README documented inputs this Actor does not have (queries, maxResultsPerQuery, includeRaw) with a placeholder (undefined) example, promised name search and registry-ID lookup (names are turned into Built In URLs; there is no search), called employees a registered count, and promised status / legal form / formation date, "formation / status monitoring", "whole dataset in one call", "every row carries query" and "empty/failed lookups never billed"; it also left out the sector presets and maxListingPages. This README describes the real inputs, output and billing.

0.1 — 2026-09-07

• AI-agent payments (x402) enabled and the README replaced with a generated template; that README described inputs this Actor does not have and was replaced in 0.1.29.

0.0

• Initial release.