# Wikipedia Company Intelligence Scraper (`datapilot/wikipedia-company-intelligence-scraper`) Actor

Extract company descriptions, founding date, headquarters, founders, employees, websites, and developer/parent information using Wikipedia data. Supports multiple companies, AI product mapping, and pay-per-event charging.

- **URL**: https://apify.com/datapilot/wikipedia-company-intelligence-scraper.md
- **Developed by:** [Data Pilot](https://apify.com/datapilot) (community)
- **Categories:** Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 scraped results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

📚 **Wikipedia Company Intelligence** Scraper is a powerful Apify Actor designed to discover, track, and collect public company profile data directly from Wikipedia's own API and infobox metadata. This tool provides comprehensive **Wikipedia Company Intelligence** including founding date, headquarters, founders, employee count, company description, and official website for any company or well-known product name. Whether you're building a company research dashboard, enriching a lead list, or conducting competitive intelligence, the Wikipedia Company Intelligence Scraper delivers actionable **Wikipedia Company Intelligence** efficiently.

With smart product-to-parent-company resolution, Wikipedia search disambiguation, infobox metadata extraction, and reliable Apify Dataset delivery, the Wikipedia Company Intelligence Scraper ensures comprehensive **Wikipedia Company Intelligence** coverage across any list of company or product names. It focuses on key **Wikipedia Company Intelligence** signals including founders, headquarters, and founding date, making it an essential tool for company background research.

***

### 📋 Table of Contents

- [Features](#-features)
- [Data Source](#-data-source)
- [How It Works](#-how-it-works)
- [Input](#-input)
- [Output](#-output)
- [Technical Stack](#-technical-stack)
- [Data Fields](#-data-fields-explained)
- [Use Cases](#-use-cases)
- [Quick Start](#-quick-start)
- [Configuration](#-configuration)
- [Performance](#-performance)
- [Important Notes](#-important-notes)
- [License & Legal](#-license--legal)

***

### 🔥 Features

- **Product-to-Parent Resolution** – Recognizes well-known AI products (e.g., Claude, ChatGPT, Gemini) and automatically resolves them to their parent company for **Wikipedia Company Intelligence**.
- **Smart Wikipedia Search** – Searches Wikipedia with multiple query variations and filters out disambiguation pages and unrelated name pages.
- **Infobox Metadata Extraction** – Parses Wikipedia's structured infobox table for founding date, headquarters, founders, employee count, and website.
- **Parent Company Fallback** – If a product page lacks company details, automatically follows the "developer/owner/parent" link to pull data from the parent company's own page.
- **Article Summary Extraction** – Pulls a clean company description from Wikipedia's REST summary API.
- **Text Cleaning** – Strips Wikipedia footnote citations and formatting artifacts from every extracted field.
- **Batch Processing** – Look up any number of company or product names in a single run.
- **Configurable Item Cap** – Control how many **Wikipedia Company Intelligence** profiles are collected per run.
- **Real-Time Dataset Push** – Pushes all collected **Wikipedia Company Intelligence** records to Apify Dataset in a single batch.
- **Graceful Error Handling** – Skips a failing lookup without stopping the whole run.

***

### 📊 Data Source

#### **Wikipedia Public API & Article Pages**

- **Authority**: Wikipedia's own public MediaWiki search API, REST summary API, and rendered article pages
- **Access Method**: Direct HTTP requests via `urllib.request`, with `BeautifulSoup` parsing infobox HTML
- **Coverage**: Any company or product with a public Wikipedia article
- **Data**: Article summary text and structured infobox fields
- **Access**: Public API and pages, no API key required
- **Update Frequency**: Reflects each Wikipedia article's current live state at time of run

***

### ⚙️ How It Works

The Wikipedia Company Intelligence Scraper accepts a list of company or product names, first checking each against a built-in product-to-parent-company map (e.g., "Claude" → "Anthropic"). For each resolved name, it searches Wikipedia to find the best matching article, filtering out disambiguation and unrelated pages. It then fetches the article's summary description and parses the infobox table for founding date, headquarters, founders, employee count, and website. If the resolved page is a product page lacking company details (like founding date or HQ), the Actor follows the developer/owner/parent link on that infobox to pull the missing fields from the parent company's own Wikipedia page. Every successfully resolved **Wikipedia Company Intelligence** record is collected and pushed to the Apify Dataset in a single batch at the end of the run.

**Key Processing Steps:**

1. **Input Parsing** – Accept a list of company or product names, plus a max item count
2. **Product Mapping Check** – Resolve known AI products to their parent company name
3. **Company Loop** – Iterate through each **Wikipedia Company Intelligence** target
4. **Wikipedia Search** – Find the best matching, non-disambiguation article title
5. **Summary Fetch** – Pull the article's clean description from the REST summary API
6. **Infobox Parsing** – Extract founded date, HQ, founders, employees, and website from the infobox table
7. **Text Cleaning** – Strip citation brackets and formatting artifacts from every field
8. **Parent Fallback** – Follow the developer/owner/parent link if key fields are still missing
9. **Item Cap Enforcement** – Stop collecting once the configured maximum item count is reached
10. **Dataset Push** – Push all collected records from the run in a single batch

**Key Benefits:**

- Pull **Wikipedia Company Intelligence** for any company or product name without manual lookup
- Automatically resolve well-known AI products to their actual parent company
- Get clean, citation-free company descriptions and structured infobox facts
- Feed research pipelines, lead enrichment tools, or company background dashboards
- Batch-process long lists of companies or products in a single run

***

### 📥 Input

The Actor accepts the following input parameters:

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `company_names` | array | `["Claude"]` | List of company or product names to look up. |
| `search_query` | string | `""` | Optional single search term, used if `company_names` is empty. |
| `maxItems` | integer | `10` | Maximum number of **Wikipedia Company Intelligence** profiles to collect. |

**Example Input:**

```json
{
  "company_names": ["Claude", "OpenAI", "Notion"],
  "maxItems": 10
}
```

**Look Up a Single Company:**

```json
{
  "company_names": ["Anthropic"]
}
```

**Look Up a Known AI Product:**

```json
{
  "company_names": ["ChatGPT"]
}
```

***

### 📤 Output

The Actor pushes **Wikipedia Company Intelligence** records with the following structure:

| Field | Type | Description |
|-------|------|-------------|
| `company` | string | Resolved company name, as titled on Wikipedia |
| `founded` | string | Founding date, as listed in the Wikipedia infobox |
| `HQ` | string | Headquarters location, as listed in the Wikipedia infobox |
| `funding_total` | string | Reserved field; not populated from Wikipedia data (always `"N/A"`) |
| `last_round` | string | Reserved field; not populated from Wikipedia data (always `"N/A"`) |
| `investors` | array | Reserved field; not populated from Wikipedia data (always empty) |
| `founders` | array | List of founder names, as listed in the Wikipedia infobox |
| `employees` | string | Employee count, as listed in the Wikipedia infobox |
| `description` | string | Clean company description from the Wikipedia article summary |
| `website` | string | Official website URL, as listed in the Wikipedia infobox |

**Example Wikipedia Company Intelligence Record:**

```json
{
  "company": "Anthropic",
  "founded": "2021",
  "HQ": "San Francisco, California, U.S.",
  "funding_total": "N/A",
  "last_round": "N/A",
  "investors": [],
  "founders": ["Dario Amodei", "Daniela Amodei"],
  "employees": "N/A",
  "description": "Anthropic is an American artificial intelligence company...",
  "website": "https://www.anthropic.com"
}
```

***

### 🧰 Technical Stack

- **HTTP Client:** `urllib.request` for direct requests to Wikipedia's public APIs and article pages
- **HTML Parsing:** `BeautifulSoup` for infobox table extraction
- **Text Cleaning:** `re` for stripping citation brackets and formatting artifacts
- **JSON Handling:** `json` for parsing Wikipedia API responses
- **Async:** `asyncio` for the Actor's run loop
- **Logging:** Apify Actor logging system
- **Platform:** Apify Actor serverless environment

***

### 📊 Data Fields Explained

#### **Identity**

- **company**: The resolved, canonical company name as titled on Wikipedia
- **description**: A clean, citation-free summary pulled from the Wikipedia article

#### **Company Details**

- **founded**: The founding date as listed in the infobox
- **HQ**: The headquarters location as listed in the infobox
- **founders**: Up to five founder names as listed in the infobox
- **employees**: The employee count as listed in the infobox
- **website**: The official company website URL

#### **Reserved Fields**

- **funding\_total, last\_round, investors**: Included in the output schema for compatibility, but not populated — Wikipedia infoboxes do not reliably expose structured funding data

***

### 🎯 Use Cases

- **Company Background Research** – Quickly pull founding date, HQ, and founders for any company
- **Lead Enrichment** – Add basic firmographic context to a list of company names
- **Product-to-Company Mapping** – Resolve well-known AI products to their actual parent company
- **Competitive Research** – Compare founding dates, HQ locations, and team size across companies
- **Academic Research** – Study public company information at scale
- **Content Research** – Source clean, citation-free company descriptions for articles or reports

***

### 🚀 Quick Start

#### **1. Prepare Input**

Go to Apify Console and enter:

```json
{
  "company_names": ["Claude", "Notion"]
}
```

#### **2. Run the Actor**

Click **Start**. The Actor will:

- Resolve any known product names to their parent company
- Search Wikipedia for the best matching article
- Extract description and infobox fields, following the parent link if needed
- Push all results to the Dataset

#### **3. Monitor Progress**

Console shows:

```
[Company Intelligence Engine] Extracting profile for: ['Claude', 'Notion']
Fetching data for: Claude
Successfully retrieved profile: Anthropic
Done! Saved total 2 company profile(s) to dataset.
```

#### **4. View & Download Results**

- **Results Tab**: All **Wikipedia Company Intelligence** records
- **Export**: JSON, CSV, Excel, or HTML
- **Filter**: By company name or headquarters
- **API Access**: Available via the Apify API

***

### ⚙️ Configuration

Single company lookup:

```json
{
  "company_names": ["Anthropic"]
}
```

Batch lookup with item cap:

```json
{
  "company_names": ["Claude", "ChatGPT", "Gemini", "Copilot"],
  "maxItems": 10
}
```

Single search query instead of a list:

```json
{
  "company_names": [],
  "search_query": "Midjourney"
}
```

***

### 📈 Performance

#### **Processing Speed**

- Two to three lightweight HTTP requests per company (search, summary, infobox, plus an optional parent-page fetch)
- No browser rendering required, so lookups are fast
- Sequential processing keeps requests polite to Wikipedia's servers

#### **Resource Usage**

- Memory: Low, since no browser instance is launched
- Network: Two to four requests per company, depending on parent-fallback needs
- Proxy: Not used — Wikipedia's public API does not require one

***

### ⚠️ Important Notes

#### **Legal & Compliance**

- **Fair Use**: Uses Wikipedia's public API and applies polite, low-volume request patterns
- **Public Data Only**: Retrieves only publicly available Wikipedia article and infobox data
- **Attribution**: Wikipedia content is available under Creative Commons licensing — attribute Wikipedia when republishing descriptions
- **Legal**: Not legal advice — consult qualified professionals before using this data for compliance-sensitive decisions

#### **Data Quality**

- **Freshness**: Reflects each Wikipedia article's current live state at time of run
- **Completeness**: Infobox fields vary by article; some companies may have incomplete data, and funding-related fields are never populated
- **Accuracy**: Sourced directly from Wikipedia, which is community-edited and may contain errors or be out of date
- **Verification**: Cross-check high-stakes figures against official company sources

#### **Best Practices**

- Use well-known company or product names for the most reliable article matches
- Expect `funding_total`, `last_round`, and `investors` to always return empty — this Actor does not source funding data
- Re-run periodically if tracking infobox changes over time
- Batch multiple names together in one run to reduce overhead
- For funding, investor, or financial data, pair this Actor with a dedicated financial data source

***

### 📄 License & Legal

**Terms of Use:**

- Use for legitimate research, enrichment, and analytics purposes
- Respect Wikipedia's Terms of Use and content licensing (CC BY-SA)
- Attribute Wikipedia when republishing extracted descriptions
- Use **Wikipedia Company Intelligence** data responsibly and in compliance with applicable laws

**Disclaimer:**
Wikipedia Company Intelligence Scraper is provided as-is for research and analytics purposes. Users are responsible for compliance with Wikipedia's terms and all applicable laws. This is not legal advice.

***

### ⚖️ Wikipedia Company Intelligence Excellence

This Actor is optimized for **Wikipedia Company Intelligence** research with:

- ✅ Smart product-to-parent-company resolution
- ✅ Disambiguation-aware Wikipedia search
- ✅ Structured infobox extraction with parent-page fallback
- ✅ Clean, citation-free descriptions
- ✅ Real-time Dataset push
- ✅ Production-ready code

# Actor input Schema

## `company_names` (type: `array`):

List of company names to target (e.g. \['OpenAI', 'Stripe'])

## `search_query` (type: `string`):

General search query if no specific list is provided

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

Maximum number of company profiles to scrape

## `useApifyProxy` (type: `boolean`):

Enable Residential Proxy to handle anti-bot protection

## Actor input object example

```json
{
  "company_names": [
    "Stripe"
  ],
  "search_query": "",
  "maxItems": 10,
  "useApifyProxy": true
}
```

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("datapilot/wikipedia-company-intelligence-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 = {}

# Run the Actor and wait for it to finish
run = client.actor("datapilot/wikipedia-company-intelligence-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 '{}' |
apify call datapilot/wikipedia-company-intelligence-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datapilot/wikipedia-company-intelligence-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/mP9LkLc850iqSw9RN/builds/o6BARZ9s7hXlWeYYp/openapi.json