# Company Intelligence - Hiring Data & Competitors (`kipeprsky/company-intel-actor`) Actor Give it a company name and domain, get back tech stack (150+ tools), hiring data with department breakdown, executive contacts, email format, LinkedIn headcount, competitors, recent news, and social links. No API keys needed. Tested on 100+ companies with 100% success rate - **URL**: https://apify.com/kipeprsky/company-intel-actor.md - **Developed by:** [Company Intelligence](https://apify.com/kipeprsky) (community) - **Categories:** Lead generation, Other, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ## company-intel-actor Apify actor that enriches companies. Input: name + domain. Output: tech stack, hiring signals, contacts, competitors, news, LinkedIn, GitHub, social links. Sold on Apify Store at $0.05/company. No API keys required. ### Running locally ```bash pip install -r requirements.txt python test_30.py ## 30 companies, quick validation python test_100.py ## 100 companies, full stress test ```` ### Coverage (100-company test) | Field | Coverage | |---|---| | website\_summary | 95% | | tech\_stack | 92% | | linkedin\_headcount | 82% | | contacts | 61% | | open\_roles | 59% | | email\_format | 22% (50% with GITHUB\_TOKEN) | | competitors | 28% | 100/100 success rate. avg 16s/company, p90 27s. ### Optional env vars | Var | Effect | |---|---| | `GITHUB_TOKEN` | Raises GitHub rate limit 60 -> 5000/hr, boosts email\_format to ~50% | | `LINKEDIN_COOKIE` | li\_at cookie, improves LinkedIn headcount extraction | ### Deploy ```bash apify push ``` # Actor input Schema ## `companies` (type: `array`): List of companies to enrich. Each item needs at least a company name. ## `skipWebsite` (type: `boolean`): Skip website scraping (faster, but no tech stack or website summary) ## `skipGithub` (type: `boolean`): Skip GitHub signals (useful for non-tech companies) ## `skipJobs` (type: `boolean`): Skip Greenhouse/Lever job board lookups ## `skipCompetitors` (type: `boolean`): Skip G2/AlternativeTo competitor lookup ## `skipNews` (type: `boolean`): Skip Google News headline fetch ## Actor input object example ```json { "companies": [ { "name": "Stripe", "domain": "stripe.com" }, { "name": "Datadog", "domain": "datadoghq.com" } ], "skipWebsite": false, "skipGithub": false, "skipJobs": false, "skipCompetitors": false, "skipNews": false } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "companies": [ { "name": "Stripe", "domain": "stripe.com" }, { "name": "Datadog", "domain": "datadoghq.com" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("kipeprsky/company-intel-actor").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "companies": [ { "name": "Stripe", "domain": "stripe.com", }, { "name": "Datadog", "domain": "datadoghq.com", }, ] } # Run the Actor and wait for it to finish run = client.actor("kipeprsky/company-intel-actor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).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": [ { "name": "Stripe", "domain": "stripe.com" }, { "name": "Datadog", "domain": "datadoghq.com" } ] }' | apify call kipeprsky/company-intel-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kipeprsky/company-intel-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Company Intelligence - Hiring Data & Competitors", "description": "Give it a company name and domain, get back tech stack (150+ tools), hiring data with department breakdown, executive contacts, email format, LinkedIn headcount, competitors, recent news, and social links. No API keys needed. Tested on 100+ companies with 100% success rate", "version": "0.0", "x-build-id": "KAqWcb4mc4rU0qo3b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kipeprsky~company-intel-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kipeprsky-company-intel-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/kipeprsky~company-intel-actor/runs": { "post": { "operationId": "runs-sync-kipeprsky-company-intel-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/kipeprsky~company-intel-actor/run-sync": { "post": { "operationId": "run-sync-kipeprsky-company-intel-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "companies" ], "properties": { "companies": { "title": "Companies", "type": "array", "description": "List of companies to enrich. Each item needs at least a company name.", "items": { "type": "object", "properties": { "name": { "title": "Company name", "type": "string", "description": "Company name" }, "domain": { "title": "Domain", "type": "string", "description": "Company website domain (e.g. stripe.com)" } }, "required": [ "name" ] } }, "skipWebsite": { "title": "Skip Website Scraping", "type": "boolean", "description": "Skip website scraping (faster, but no tech stack or website summary)", "default": false }, "skipGithub": { "title": "Skip GitHub", "type": "boolean", "description": "Skip GitHub signals (useful for non-tech companies)", "default": false }, "skipJobs": { "title": "Skip Job Postings", "type": "boolean", "description": "Skip Greenhouse/Lever job board lookups", "default": false }, "skipCompetitors": { "title": "Skip Competitors", "type": "boolean", "description": "Skip G2/AlternativeTo competitor lookup", "default": false }, "skipNews": { "title": "Skip News", "type": "boolean", "description": "Skip Google News headline fetch", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```