Company Lookup – B2B Firmographic Data by Name or Domain

Used by sales reps enriching CRM exports before outreach, growth teams validating prospect firmographics at scale, and AI agents looking up company context mid-workflow.

Enrich a list of company names or domains with structured firmographic data in one run. Returns industry, employee count, founded year, HQ location, website, and social profiles (LinkedIn, Twitter, GitHub, Facebook) — sourced from Wikipedia/Wikidata via the free DuckDuckGo Instant Answer API, with domain resolution via Clearbit autocomplete. No API key required. Pay per result.

$1.50/1K records (Pay Per Event). Social profiles add-on +$0.50/1K (when LinkedIn/Twitter/GitHub found). First 10 results free. Failed lookups are never charged — you pay only for successful enrichments.

This is the fastest zero-cost path from a raw list of company names (from a CRM export, a scraped list, or a spreadsheet) to a fully enriched B2B dataset.

---

What you get per company

Field	Description
query	Original input (name or domain)
name	Canonical company name
domain	Primary domain (e.g. stripe.com)
description	Short company description from Wikipedia
industry	Industry label(s) (e.g. Financial services, Payment processor)
size_range	Employee band: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10000+
employees	Raw employee count (most recent figure available)
founded	Founding year as 4-digit string (e.g. 2010)
hq_city	HQ city
hq_country	HQ country
hq_address	Full address / location string if available
website	Canonical website URL
logo_url	Company logo image URL (free Clearbit logo CDN, by domain)
twitter	Twitter handle (without @)
linkedin	LinkedIn company slug
github	GitHub org/user handle
facebook	Facebook page slug or ID
company_type	Public / Private / Non-profit / etc.
registration_id	Stock ticker, ISIN, or other registration ID
status	Active for found companies
wikipedia_url	Link to Wikipedia article
wikidata_id	Wikidata Q-identifier (e.g. Q7624104)
sources	Which sources contributed to this record (ddg, wikidata, wikipedia, clearbit)
field_sources	Per-field provenance map — exactly which source supplied each value
parse_confidence	0–1 data quality score
warnings	Machine-readable quality codes (empty = clean)

---

Use cases

• Lead enrichment — bulk-enrich CRM exports with firmographics before outreach
• Domain verification — resolve company names to canonical domains at scale
• Market research — industry, size, and founding year across a sector
• Prospect scoring — segment by size band and company type for ABM targeting
• Competitive intelligence — gather social profiles and Wikipedia descriptions for a competitor list
• B2B outreach personalization — add HQ city + industry context to cold email sequences

---

How to use

By company name

{
  "companies": ["Stripe", "Shopify", "Databricks", "Figma Inc", "OpenAI"],
  "maxItems": 0
}

By domain

{
  "companies": ["stripe.com", "shopify.com", "vercel.com"],
  "maxItems": 0
}

Mixed names and domains (with quality filtering)

{
  "companies": ["Stripe", "shopify.com", "Databricks", "vercel.com"],
  "skipLowConfidence": true,
  "requestDelayMs": 500
}

---

Input parameters

Parameter	Type	Default	Description
companies	string[]	required	Company names and/or domains to look up
maxItems	integer	0	Max records to output. 0 = no limit
skipLowConfidence	boolean	false	Skip records with parse_confidence < 0.3
requestDelayMs	integer	500	Milliseconds between requests (increase to 1000–2000 for large batches)

---

Data sources — four free APIs, merged with redundancy

Every field is sourced from a layered set of free, no-key-required public endpoints, then merged with explicit per-field provenance. No single source is a point of failure: if one is down or has no match, the others still return a record.

1. DuckDuckGo Instant Answer API (primary) — Wikipedia/Wikidata-backed infoboxes: industry, employees, founded year, HQ, socials, company type, ISIN/ticker.

2. Wikidata (independent fallback + corroboration) — queried directly (entity search → entity claims → label resolution), not through DuckDuckGo. This means a DuckDuckGo outage or a change to its response shape does not break the lookup — Wikidata alone returns a full firmographic record. When DuckDuckGo does hit, Wikidata fills the gaps it left blank (e.g. exact HQ city, employee count, founding year) and corroborates the match.

3. Wikipedia REST summary (description fallback) — supplies a company description when neither DuckDuckGo nor Wikidata had one.

4. Clearbit Autocomplete — free name↔domain resolution. Confirms Stripe → stripe.com and resolves a canonical name from a bare domain.

The sources and field_sources fields in every record tell you exactly which API supplied each value — full data lineage, so you can trust (or filter) field-by-field.

---

Coverage

Company type	Coverage
Public companies (Fortune 500, NASDAQ/NYSE)	Excellent — full infobox + ISIN/ticker
Well-known private tech (Stripe, Vercel, Notion, Airbnb)	Very good — employees, founded, socials
Mid-tier B2B SaaS (Series B+, notable)	Good — description + domain; some fields may be missing
Small / niche / local companies	Low — Clearbit domain only, parse_confidence < 0.5
Unknown companies	Record with parse_confidence < 0.3, ddg_no_hit warning

---

Confidence scoring

• 1.0 — full DDG hit with description, industry, employees, founded
• 0.7–0.9 — good DDG hit, minor fields missing
• 0.5–0.7 — DDG hit but sparse infobox
• 0.3–0.5 — no DDG hit, Clearbit gave name/domain only
• < 0.3 — unrecognized company; query is echoed back

Enable skipLowConfidence: true to drop sparse records automatically.

---

Why this beats the alternatives

The Apify Store company-enrichment field splits into two camps — and this actor sits in the gap between them. Real incumbents (researched 2026-06): nexgendata/company-enrichment-tool (from $12/1k), andok/company-enrichment (from $10/1k), fortunate_favorite/company-intelligence, scrapepilot Company Research Scraper.

• Rich-but-opaque enrichers (nexgendata) return a deep record (tech_stack, funding_stage, revenue_range, naics/sic, …) but ship no per-field provenance and no confidence score — a value with no way to know where it came from or how far to trust it.
• Provenance-aware enrichers (andok) ship an "evidence" array with a source URL per data point — so we don't claim per-field lineage is uniquely ours. But andok has no parse-confidence / trust score, uses website-HTML + VIES + SSL + Wayback (no Wikidata/Wikipedia firmographics), and bills from $10/1k.

We deliberately don't chase tech_stack / funding / revenue — those need live per-company crawls. Our lane is the fast, zero-cost firmographic baseline with full lineage and an honest trust score: the layer you run first on a raw list before paying anyone for tech/funding depth.

Feature	This Actor	nexgendata	andok	Clearbit / Apollo API
Data source	DDG + Wikidata (direct) + Wikipedia + Clearbit	proprietary enrichment	website HTML + VIES + SSL + Wayback	paid vendor DB
No API key required	Yes	Yes	Yes	No ($99–499/mo)
Multi-source redundancy (any can fail)	Yes — 4 sources	No	Partial	Single
Per-field data lineage (field_sources)	Yes	No	Yes (evidence URLs)	No
parse_confidence on every record	Yes	No	No	No
Lineage and trust score together	Yes — unique	No	No (lineage only)	No
Failed lookups billed	Never	Sometimes	Sometimes	Sometimes
Logo URL included	Yes (Clearbit CDN)	Varies	Yes	Yes (paid)
Base cost / 1k	$1.50	$12	$10	$50–200+

The one-sentence edge: the firmographics lookup that gives you per-field source lineage and a trust score on every record (the combination nobody else ships), survives any single source going down (4 redundant free APIs), and never charges a failed lookup — at $1.50/1k vs the $10–$12/1k incumbents. Full breakdown in ./COMPETITIVE_EDGE.md.

---

FAQ

Can it look up private companies? Yes, as long as they have a Wikipedia article. Many notable private tech companies are covered. Truly niche or local companies return sparse records with parse_confidence < 0.5.

Can I input URLs instead of domains? Yes. https://stripe.com/ and www.stripe.com are both automatically normalized to stripe.com.

Does it need proxies? No. All four sources are free, unauthenticated APIs — no proxies, no keys needed.

What if a name is ambiguous (e.g. "Linear")? The actor retries DuckDuckGo with suffixes ("Linear Inc", "Linear company"), and Wikidata's entity search rejects non-company matches (it filters out disambiguation noise like a pattern or a fictional character). The first organization-type hit is returned.

What happens if DuckDuckGo is down or changes its format? The lookup continues. Wikidata is queried independently (not through DuckDuckGo) and can return a complete firmographic record on its own. This redundancy is the core design of the actor — see Data sources above.

How do I know which source a value came from? Read the field_sources map on each record — it maps every populated field to the API that supplied it (ddg, wikidata, wikipedia, or clearbit). The sources array lists every API that contributed.

---

Output sample

A successful lookup for "Stripe" returns:

{
  "query": "Stripe",
  "name": "Stripe, Inc.",
  "domain": "stripe.com",
  "description": "American financial infrastructure platform for businesses",
  "industry": "Financial services, Payment processor",
  "size_range": "1001-5000",
  "employees": 4000,
  "founded": "2010",
  "hq_city": "San Francisco",
  "hq_country": "United States",
  "website": "https://stripe.com",
  "logo_url": "https://logo.clearbit.com/stripe.com",
  "twitter": "stripe",
  "linkedin": "stripe",
  "github": "stripe",
  "sources": ["ddg", "wikidata", "clearbit"],
  "parse_confidence": 0.95,
  "warnings": []
}

---

Use with AI agents (MCP)

An agent calls this tool to look up company firmographics mid-conversation — e.g. "What industry is Stripe in?", "How many employees does Databricks have?", or "Get LinkedIn and Twitter for these 50 companies."

Point your MCP client at this tool:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.apify.com/?tools=bovi/company-lookup-scraper",
        "--header",
        "Authorization: Bearer <YOUR_APIFY_TOKEN>"
      ]
    }
  }
}

Minimal agent input (name or domain lookup):

{ "companies": ["Stripe", "Anthropic", "stripe.com"] }

No API key needed inside the tool — auth is your Apify token in the client config above. The field_sources map on every record tells the agent exactly which source supplied each value.

Pricing

Pay-per-result (PPE):

Event	Rate	Trigger
company-result (base)	$1.50/1K	Every company record pushed
social-enriched (add-on)	$0.50/1K	Records where LinkedIn, Twitter, or GitHub is present

Social premium fires only when at least one social profile was found — failed or empty lookups are never billed. Total cost example: 1,000 records with 60% social hit rate = $1.80 ($1.50 base + $0.30 social add-on).

Integrations

Built for sales-ops and growth teams enriching CRM exports with firmographic data by company name or domain — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Legal

Data sourced from DuckDuckGo Instant Answers (Wikipedia/Wikidata) and Clearbit Autocomplete — both free, public, unauthenticated endpoints. Use responsibly.