# Company Lookup – B2B Firmographic Enrichment | $1.50/1K (`bovi/company-lookup-scraper`) Actor

**Enrich company names or domains** with firmographic data: industry, **employee count**, founded year, HQ location, website, logo, and **social profiles** (LinkedIn, Twitter, GitHub).

- **URL**: https://apify.com/bovi/company-lookup-scraper.md
- **Developed by:** [Vitalii Bondarev](https://apify.com/bovi) (community)
- **Categories:** Other, MCP servers
- **Features:** Limited permissions
- **Stats:** 106 total users, 33 monthly users, 59.6% runs succeeded, 5 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.45 / 1,000 companies

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 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

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

#### By domain

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

#### Mixed names and domains (with quality filtering)

```json
{
  "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`](./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:

```json
{
  "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:

```json
{
  "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):

```json
{ "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](https://docs.apify.com/platform/integrations/n8n), [Make](https://docs.apify.com/platform/integrations/make), [Zapier](https://docs.apify.com/platform/integrations/zapier).
- **Webhooks** — fire your own endpoint the moment a run finishes, to push results straight into your pipeline ([docs](https://docs.apify.com/platform/integrations/webhooks)).
- **MCP server** — expose this actor as a tool to Claude, Cursor, or any [MCP client](https://mcp.apify.com) so an AI agent can pull this data mid-conversation ([guide](https://blog.apify.com/how-to-use-mcp/)).
- **API & SDKs** — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all [Apify integrations](https://apify.com/integrations).

### Legal

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

### More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

- [Email Finder](https://apify.com/bovi/email-finder)
- [Epa Data Scraper](https://apify.com/bovi/epa-data-scraper)
- [Firmy Cz Scraper](https://apify.com/bovi/firmy-cz-scraper)
- [Gelbeseiten Directory](https://apify.com/bovi/gelbeseiten-directory)
- [Google Maps Leads](https://apify.com/bovi/google-maps-leads)
- [Google Maps Scraper](https://apify.com/bovi/google-maps-scraper)

Chain any of them together from the **Integrations** tab (the *Run succeeded* trigger) to build a multi-step workflow — one actor's output feeds the next.

### Rate this Actor

If Company Lookup saved you time, please rate it on the Apify Store. A rating takes a few seconds and helps other people find a working scraper. If something did not work, open an issue on the Actor page and we will fix it.

### Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as `USAGE_STATS` in your own Apify key-value store, so you can see the exact record created for your run.

Set `disableUsageStats` to `true` in the input to opt out. Nothing is sent then; your `USAGE_STATS` record only says that statistics were disabled.

Only these fields are recorded:

- schema version, Actor name and build number;
- UTC start and finish hour (not a precise timestamp);
- run duration, number of results and time to the first result, each as a coarse range;
- whether the result was empty, the end status, and an error type from a fixed list;
- memory setting and counts of charged events;
- names of the input fields you set, never their values;
- the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

#### Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same `disableUsageStats: true` input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

#### Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

- the result limit you asked for (a range, when the input has one) and what share of it was delivered;
- results delivered per input item you listed (a range);
- output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
- how the run was started (console, API, schedule, webhook, another Actor);
- how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
- if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
- a short code made from the names of the input fields you set, never their values.

#### Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and `disableUsageStats: true` turns it off.

# Actor input Schema

## `companies` (type: `array`):

List of company names (e.g. 'Stripe', 'Shopify') or domains (e.g. 'stripe.com', 'shopify.com') to look up. You may mix names and domains in the same list.

## `maxItems` (type: `integer`):

Maximum total company records to output. 0 = no limit. Useful for cost control in large runs. Default: 0.

## `skipLowConfidence` (type: `boolean`):

If true, skip results with parse_confidence below 0.3 (companies where only a name/domain was found with no enrichment). If false, always output a record even if it is sparse. Default: false.

## `requestDelayMs` (type: `integer`):

Milliseconds to wait between requests. Increase to 1000–2000 for very large batches to reduce rate-limiting risk. Default: 150.

## `disableUsageStats` (type: `boolean`):

See the README Usage statistics section for details.

## Actor input object example

```json
{
  "companies": [
    "Stripe",
    "Shopify",
    "vercel.com"
  ],
  "maxItems": 0,
  "skipLowConfidence": false,
  "requestDelayMs": 150,
  "disableUsageStats": false
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset containing Company Lookup Scraper records (name, domain, industry, company_type, size_range, founded, hq_city, hq_country, website, linkedin, parse_confidence).

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "companies": [
        "Stripe",
        "Shopify",
        "vercel.com"
    ],
    "maxItems": 0,
    "skipLowConfidence": false,
    "requestDelayMs": 150
};

// Run the Actor and wait for it to finish
const run = await client.actor("bovi/company-lookup-scraper").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "companies": [
        "Stripe",
        "Shopify",
        "vercel.com",
    ],
    "maxItems": 0,
    "skipLowConfidence": False,
    "requestDelayMs": 150,
}

# Run the Actor and wait for it to finish
run = client.actor("bovi/company-lookup-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "companies": [
    "Stripe",
    "Shopify",
    "vercel.com"
  ],
  "maxItems": 0,
  "skipLowConfidence": false,
  "requestDelayMs": 150
}' |
apify call bovi/company-lookup-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bovi/company-lookup-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/a4r1Mb1edJhEfKfxl/builds/ghln3ZVkELbOnUdNj/openapi.json