# TechCognita Website Contact Extractor v1 (`atharvshinde2004/techcognita-contact-extractor`) Actor Extracts emails, phone numbers, social media links, page metadata, and tech stack from starting URLs using Crawlee, Playwright, and headless Chrome. - **URL**: https://apify.com/atharvshinde2004/techcognita-contact-extractor.md - **Developed by:** [Atharv Shinde](https://apify.com/atharvshinde2004) (community) - **Categories:** Lead generation, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## TechCognita Website Contact Extractor v1 An advanced, lightweight, and highly optimized **TypeScript-based Apify Actor** that scrapes websites to extract business contact details, social media links, page metadata, and technical stack details. Built with **Crawlee** and **Playwright**, it executes headless Chrome navigations, handles dynamic content (single-page applications), respects depth/page limits per domain, and aggregates all results uniquely. --- ### Features - 📧 **Regex & mailto: Email Extraction:** Searches page HTML, text, and `mailto:` links, while filtering out assets (`.png`, `.svg`) and dev tools (e.g. `sentry.io`). - 📞 **Smart Phone Extraction:** Extracts `tel:` links and formats raw phone numbers while avoiding false positives (like timestamps, zip codes, or IDs). - 🔗 **Social Media Finder:** Automatically identifies and normalizes links to LinkedIn, Facebook, Instagram, Twitter/X, YouTube, and GitHub. - ⚙️ **Technology Detection:** Automatically scans website source files to detect signatures of platforms and tools (WordPress, Shopify, React, Webflow, Google Analytics, Nuxt, HubSpot, etc.). - 🧭 **Intelligent Crawling Rules:** - Standardizes URLs and filters out tracking parameters (e.g. `utm_source`). - Restricts crawls strictly to the input target domains. - Controls crawl depth (`maxDepth`) and maximum pages per website (`maxPagesPerDomain`). - Identifies and scores the best candidate for the `/contact` or `/about` page. - 💾 **State-Safe Aggregation:** Merges page-level contact and stack data into a consolidated record per website using Crawlee's state persistence. --- ### Input Configuration The Actor accepts the following JSON schema input parameters: | Field | Type | Required | Default | Description | | :--- | :--- | :--- | :--- | :--- | | `startUrls` | `array` | **Yes** | `[{"url": "https://techcognita.com"}]` | List of starting URLs or domains. | | `maxPagesPerDomain` | `integer` | No | `10` | Maximum pages to scrape for each domain to control run length. | | `maxDepth` | `integer` | No | `2` | Link recursion depth limit (`0` = only home page, `1` = homepage and its direct links). | #### Example Input ```json { "startUrls": [ { "url": "https://techcognita.com" } ], "maxPagesPerDomain": 10, "maxDepth": 2 } ```` *** ### Output Structure The Actor aggregates page data and exports a single clean JSON record per domain into the Apify dataset: ```json { "url": "https://techcognita.com", "domain": "techcognita.com", "emails": [ "founder@techcognita.com" ], "phones": [ "+917558508876" ], "socials": { "linkedin": "https://linkedin.com/company/techcognita", "facebook": "https://facebook.com/techcognita", "twitter": "https://twitter.com/techcognita" }, "technologies": [ "Google Analytics", "React", "Tailwind CSS" ], "scrapedAt": "2026-07-02T17:05:11.687Z", "title": "TechCognita | Enterprise Software Development & IT Services", "description": "TechCognita delivers premium custom software engineering, business automation, and AI chatbots.", "contactPage": "https://www.techcognita.com/about" } ``` *** ### Local Development & Setup 1. **Install dependencies:** ```bash npm install ``` 2. **Run locally:** ```bash npm start ``` 3. **Check results:** Review scraped output items in `storage/datasets/default/`. 4. **Compile TypeScript:** ```bash npm run build ``` *** ### Deployment to Apify Platform Authenticate your local environment and push the code: ```bash apify login apify push ``` Your Actor will build on the Apify platform using the optimal Docker container configuration with headless Chrome and Playwright dependencies preset. # Actor input Schema ## `startUrls` (type: `array`): Starting website URLs to extract contact information from. ## `maxPagesPerDomain` (type: `integer`): Maximum number of pages to crawl and extract from for each domain/website. ## `maxDepth` (type: `integer`): Maximum link depth (0 = start page only, 1 = start page + first-level links, 2 = up to second-level links). ## Actor input object example ```json { "startUrls": [ { "url": "https://techcognita.com" } ], "maxPagesPerDomain": 10, "maxDepth": 2 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "startUrls": [ { "url": "https://techcognita.com" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("atharvshinde2004/techcognita-contact-extractor").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 = { "startUrls": [{ "url": "https://techcognita.com" }] } # Run the Actor and wait for it to finish run = client.actor("atharvshinde2004/techcognita-contact-extractor").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 '{ "startUrls": [ { "url": "https://techcognita.com" } ] }' | apify call atharvshinde2004/techcognita-contact-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=atharvshinde2004/techcognita-contact-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TechCognita Website Contact Extractor v1", "description": "Extracts emails, phone numbers, social media links, page metadata, and tech stack from starting URLs using Crawlee, Playwright, and headless Chrome.", "version": "1.0", "x-build-id": "v6kPa8IXQtgCQ0lfO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/atharvshinde2004~techcognita-contact-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-atharvshinde2004-techcognita-contact-extractor", "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/atharvshinde2004~techcognita-contact-extractor/runs": { "post": { "operationId": "runs-sync-atharvshinde2004-techcognita-contact-extractor", "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/atharvshinde2004~techcognita-contact-extractor/run-sync": { "post": { "operationId": "run-sync-atharvshinde2004-techcognita-contact-extractor", "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": [ "startUrls" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "Starting website URLs to extract contact information from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPagesPerDomain": { "title": "Max Pages per Domain", "type": "integer", "description": "Maximum number of pages to crawl and extract from for each domain/website.", "default": 10 }, "maxDepth": { "title": "Max Crawl Depth", "type": "integer", "description": "Maximum link depth (0 = start page only, 1 = start page + first-level links, 2 = up to second-level links).", "default": 2 } } }, "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 } } } } } } } } } } ```