Company Data Enrichment: Firmographics from Public Registries

Turn a list of company domains, names, stock tickers or LEI codes into firmographics: industry, founding date, headquarters, employees, revenue, founders, CEO, parent and subsidiaries, tickers, LinkedIn/X/Crunchbase links, the official legal entity (GLEIF LEI registry) and SEC EDGAR filer data (CIK, SIC code, EIN, phone, business address, latest annual report).

A Crunchbase/Apollo-style company lookup built only on open, official data: Wikidata, the Global LEI Index (GLEIF) and the US SEC. No login, no cookies, no proxies, no scraping walls, so runs do not break or get blocked.

Pricing

• $3.00 per 1,000 companies found ($0.003 per company). No start fee.
• Inputs that match nothing are free, and duplicates are skipped (including two inputs that resolve to the same company).
• For comparison, Crunchbase scrapers in the Store charge $8–$15 per 1,000 companies and often get blocked.

Why this one

Problem with other lead/company Actors (from their public issues)	Here
"Duplicate 5,000 lead run" refund requests, "90% same leads"	Inputs deduplicated, and one row per real company (matched by ID)
"Max result doesn't work", "keeps using tokens after abort"	maxCompanies hard cap. Each lookup has a 90 s time limit
"Bad data", "70% of the leads are invalid", filters ignored	Every field comes from a named source (sources, matchMethod, wikidataUrl, gleifUrl, secUrl), so you can audit it
Crunchbase scrapers blocked or rated 1–1.5★	Open APIs only, nothing to block

Input (all optional)

{"companies": ["stripe.com", "Siemens", "NVDA", "Nubank", "529900ODI3047E2LIV03"], "language": "en"}

• Domain → matched on the official website registered in Wikidata (most precise).
• Ticker (1–5 capital letters) → stock ticker, then SEC.
• LEI (20 characters) → GLEIF registry.
• Name → best business match among Wikidata results (must look like a company), else GLEIF legal-name lookup.

Empty input runs 5 sample companies in a few seconds.

Output example (real run, 2026-10-21, input hubspot.com, shortened)

{
  "input": "hubspot.com",
  "name": "HubSpot",
  "matchMethod": "wikidata-official-website",
  "description": "American marketing software company",
  "domain": "hubspot.com",
  "industries": [
    "software industry",
    "software as a service",
    "marketing",
    "customer relationship management",
    "..."
  ],
  "foundedDate": "2006-01-01",
  "headquarters": [
    "Cambridge"
  ],
  "country": [
    "United States"
  ],
  "founders": [
    "Brian Halligan",
    "Dharmesh Shah"
  ],
  "ceo": [
    "Yamini Rangan"
  ],
  "employees": 8246,
  "employeesAsOf": "2024-01-01",
  "revenue": 2630000000,
  "revenueCurrency": "United States dollar",
  "revenueAsOf": "2024-01-01",
  "tickers": [
    "HUBS"
  ],
  "stockExchanges": [
    "New York Stock Exchange"
  ],
  "linkedinUrl": "https://www.linkedin.com/company/hubspot",
  "sources": [
    "wikidata",
    "gleif",
    "sec-edgar"
  ],
  "registry": {
    "lei": "529900BLNVEW0MVKJB37",
    "legalName": "HUBSPOT, INC.",
    "jurisdiction": "US-DE",
    "entityStatus": "ACTIVE",
    "registeredAs": "3950045",
    "headquartersAddress": "2nd Floor, 25 First Street, Cambridge, US-MA, 02141, US",
    "entityCreationDate": "2005-04-04"
  },
  "sec": {
    "cik": "0001404655",
    "sicDescription": "Services-Prepackaged Software",
    "ein": "202632791",
    "phone": "(888) 482-7768",
    "businessAddress": "2 CANAL PARK, CAMBRIDGE, MA, 02141",
    "latestAnnualReportDate": "2026-02-11"
  }
}

All fields are described in the dataset schema. Fields that a source does not publish are null.

Ready-made presets

Preset	Input
Public companies by ticker (adds SEC EDGAR + LEI)	{"companies": ["AAPL", "MSFT", "NVDA", "MELI", "GLOB"]}
Latin American companies by name, Spanish labels	{"companies": ["Bancolombia", "Falabella", "Rappi", "Corporación Favorita", "Totvs"], "language": "es", "includeNotFound": true}
Firmographics from domains (most precise)	{"companies": ["stripe.com", "shopify.com", "hubspot.com"]}

Workflow: domain → company + contacts (with Company Data Enrichment)

Website Contact Finder and Company Data Enrichment are built to be used together on the same list of domains. Join their outputs on domain:

Step	Actor	Gives you	Price
1	Company Data Enrichment	Who the company is: industry, HQ, employees, revenue, CEO, legal entity (LEI), SEC data	$3 / 1,000 companies found
2	Website Contact Finder	How to reach them: emails with MX check, phones, social profiles, tech stack	$2 / 1,000 sites with contacts

Real output for hubspot.com from both Actors (runs on 2026-10-21, shortened):

Contacts (Website Contact Finder):

{
  "domain": "hubspot.com",
  "companyName": "HubSpot",
  "primaryEmail": null,
  "primaryPhone": "+16178125820",
  "phonesTotal": 58,
  "linkedin": [
    "https://www.linkedin.com/company/hubspot",
    "... 17 more (company + people pages)"
  ],
  "twitter": [
    "https://x.com/HubSpot",
    "..."
  ],
  "facebook": [
    "https://www.facebook.com/hubspot"
  ],
  "techHints": [
    "Cloudflare",
    "Google Analytics",
    "HubSpot"
  ]
}

Company (Company Data Enrichment):

{
  "name": "HubSpot",
  "industries": [
    "software industry",
    "software as a service",
    "..."
  ],
  "foundedDate": "2006-01-01",
  "headquarters": [
    "Cambridge"
  ],
  "employees": 8246,
  "revenue": 2630000000,
  "revenueCurrency": "United States dollar",
  "ceo": [
    "Yamini Rangan"
  ],
  "tickers": [
    "HUBS"
  ],
  "registry": {
    "lei": "529900BLNVEW0MVKJB37",
    "legalName": "HUBSPOT, INC."
  },
  "sec": {
    "cik": "0001404655",
    "sicDescription": "Services-Prepackaged Software"
  }
}

No-code: run both on the same domain list (or chain them with an Apify integration / webhook on "run succeeded"), export both datasets and merge on domain.

API (Python):

from apify_client import ApifyClient
client = ApifyClient("<APIFY_TOKEN>")
domains = ["hubspot.com", "calendly.com", "stripe.com"]
companies = client.actor("datadesk-io/company-data-enrichment").call(run_input={"companies": domains})
contacts = client.actor("datadesk-io/website-contact-finder").call(run_input={"websites": domains})
by_domain = {r["domain"]: r for r in client.dataset(contacts["defaultDatasetId"]).iterate_items()}
for c in client.dataset(companies["defaultDatasetId"]).iterate_items():
    lead = {**c, "contacts": by_domain.get(c.get("domain"))}
    print(lead["name"], lead.get("employees"), (lead["contacts"] or {}).get("primaryEmail"))

AI agents (MCP): load both tools in one MCP server:

https://mcp.apify.com/?tools=datadesk-io/company-data-enrichment,datadesk-io/website-contact-finder

Then ask, for example: "For hubspot.com, calendly.com and stripe.com, get industry, employee count and CEO, then the best contact email (deliverable domains only) and LinkedIn page, and give me one table." The agent calls Company Data Enrichment with {"companies": [...]} and Website Contact Finder with {"websites": [...]}, then joins the results on domain. Use maxCompanies / maxWebsites to cap the cost of each call.

Limits

• Coverage is best for mid-size and large companies, public companies, banks and regulated entities (anything with a Wikidata item, an LEI or SEC filings). Very small local businesses are often not in these registries. They return "not found" and cost nothing. For small-business contacts, combine with Website Contact Finder.
• Employee counts and revenue are the latest values published in Wikidata, with their date (employeesAsOf, revenueAsOf).
• Name matching can pick the wrong company for ambiguous names. Use domains when you have them.

Use with AI agents (MCP)

Add it to Claude, ChatGPT, Cursor, VS Code or any MCP client through the Apify MCP server:

https://mcp.apify.com/?tools=datadesk-io/company-data-enrichment

Authenticate with OAuth or Authorization: Bearer <APIFY_TOKEN>. Local stdio config:

{"mcpServers": {"companies": {"command": "npx", "args": ["-y", "@apify/actors-mcp-server", "--tools", "datadesk-io/company-data-enrichment"], "env": {"APIFY_TOKEN": "<token>"}}}}

Example prompt: "Get industry, HQ, employee count, CEO and legal entity (LEI) for stripe.com, siemens.com and nubank.com.br."

The agent sees the input schema and the documented output fields. Use maxCompanies to cap the cost of one call. Plain REST: POST https://api.apify.com/v2/acts/datadesk-io~company-data-enrichment/run-sync-get-dataset-items?token=....

Data sources and licensing

Wikidata (CC0), GLEIF LEI data (CC0) and SEC EDGAR (US public domain), all accessed through their official public APIs with a descriptive User-Agent and within their rate limits.