Targeted Company Email Scraping

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in PHP, Web Scraping, Software Architecture, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@Haider10111993

Hello, I can accurately extract and verify genuine contact details from your curated list of company URLs and deliver a clean, upload-ready CSV. I will: Scan each provided website and its relevant contact/about pages Extract valid company email addresses and matching phone numbers Remove duplicates, malformed addresses, fake entries, and role-based inboxes Validate email syntax, domain/MX records, and deliverability using a reputable verification service Match phone numbers with the exact source pages Preserve the original scraped URL for complete traceability Deliver a structured CSV in your required format Accuracy will be prioritized over inflated results. I will not guess email patterns or include unverified contacts simply to increase the final count. Any inaccessible website or email that cannot be reliably verified will be clearly excluded or flagged. I have over 10 years of experience in PHP, Python-compatible data workflows, web scraping, automation, and structured data processing. I can also provide a small sample from your URL list before processing the complete dataset. I’m available to begin immediately. Best regards, Haider Amin

@sabaehsan

Greetings, I've gone through your project details, and it sounds like you need a reliable way to extract valid email addresses and phone numbers from a list of specific company URLs. My approach would involve using tools like Python with BeautifulSoup or Scrapy to ensure accurate and efficient data scraping. I understand the importance of accuracy, so I'll make sure to clean up any duplicates and filter out role-based emails or invalid addresses. Once the data is gathered, I’ll verify the deliverability to ensure everything you receive is ready for your marketing efforts. The final output will be a clean, well-structured CSV file containing the URL, email addresses, and any matched phone numbers. I'm excited to help you with this project and deliver results that meet your expectations. Best regards, Saba Ehsan

@starttechno

I’ll build a focused scraper for your curated company URLs and return one clean CSV with the scraped URL, each discovered email, and the phone number found on the same page. I’ll use Python with Scrapy/BeautifulSoup, following relevant internal pages when needed, decoding obfuscated mailto links, normalizing addresses, and removing duplicates across the complete dataset. Email filtering will exclude role-based addresses such as info@, sales@, support@, and obvious test or fake inboxes, while retaining genuine person-specific contacts. I’ll also apply syntax, domain, MX, and deliverability checks using a suitable verification service, recording only addresses that pass your acceptance criteria. Phone numbers will be normalized and matched to the page where they appear, with blank values where no reliable number is available. Before delivery, I’ll run validation checks on the CSV so every row has the required structure and consistent formatting. I’ve built an AI outreach system that performs business discovery, contact scraping, personalized email preparation, and bounce-aware sending, so I understand the importance of clean data rather than inflated results. Are your curated URLs already provided in a CSV or spreadsheet? Muhammad Saad

@snapycode

HI, With extensive experience in automated data extraction, I will build a targeted Python scraper using BeautifulSoup/Scrapy that doesn't just scan the raw HTML, but maps the DOM structure. This allows me to pair emails and phone numbers based on their physical proximity on the page, ensuring that a specific person's email is accurately linked to their direct phone line, rather than a generic footer number. I will systematically filter out role-based inboxes (info@, support@, sales@) and consolidate duplicates by domain. The remaining list will be processed through an SMTP handshake validation service (like NeverBounce) to verify deliverability and catch dead mailboxes or catch-all domains before they hit your marketing platform. The final deliverable will be a pristine CSV formatted exactly to your specifications: Source URL | Verified Email | Matched Phone Number. Send me your URL list. I will process a 5-domain sample immediately so you can verify the formatting and data quality before we run the full batch. Thanks, Raj

@ambientinfotech

As a seasoned web developer, Ambient Infotech understands the importance of accuracy and optimization in web scraping. With an extensive background in Python and PHP backed by twelve-plus years of experience with over 1200 successfully delivered projects, my team is adept at using the latest and most efficient tools like BeautifulSoup and Scrapy to provide accurate and reliable data extraction. Our skills span far beyond scraping alone, as we specialize in creating scalable digital ecosystems, specifically tailored to meet the unique needs of businesses. This holistic perspective ensures that not only do we clean duplicates, remove fake inboxes and verify deliverability, as per your requirement but also construct a well-structured CSV form that's instantly uploadable for your marketing platform. With your project needing targeted emails and phone numbers, our comprehensive understanding of ERP and CRM would prove invaluable as this has given us deep insight on APIs, integrations and automations - expertise that could be invaluable for this project. Trust in our structured approach to improve operational efficiency, reduce manual processes while delivering measurable results. Choose me today to get an error-free project delivered on time.

@iosifpeterfi

Hello, I'm Iosif, a solutions architect and project lead with a background in web scraping, data processing, and automation pipelines. Your goal is clear: extract verified, deliverable email addresses and phone numbers from your curated list of company URLs, cleaned and formatted for immediate use in your marketing platform. Here's how I'd structure the engagement: Phase 1 — Setup and validation: • Review your URL list and confirm expected volume and any domain-specific requirements • Set up the scraping pipeline with appropriate rate limiting and error handling Phase 2 — Extraction and verification: • Scrape each URL for email addresses and phone numbers • Deduplicate, remove role-based and invalid inboxes • Run deliverability verification on all extracted emails Phase 3 — Delivery: • Structured CSV with URL, email, and matched phone number columns • Summary report on extraction rates and any problematic domains Deliverables: • Clean, verified CSV ready for platform upload • Deduplication and deliverability validation applied • Summary of results per domain I've delivered similar data extraction projects for organizations including MPDL and the U.S. Department of Defense. Best regards, Iosif Peterfi

@justmian876

As an experienced team of web scraping and data extraction specialists, our ability to deliver reliable and high-quality digital solutions sets us apart from the competition. We can scrape and process over 1 million+ data records daily with a proven track record in maintaining massive databases of over 20+ million business and retail data records. We are confident that we have the skills and expertise to handle your targeted company email scraping project with ease.

@ShaikhAneesa

Hi there, I have carefully reviewed your project requirements for targeted company email scraping. I understand the importance of accuracy in extracting valid email addresses and phone numbers from the provided URLs. Let's chat and discuss it further. To handle your project, I will start with utilizing Python along with BeautifulSoup for web scraping. I will focus on cleaning duplicates, filtering out role-based or fake email addresses, and verifying deliverability. My approach will ensure that the final CSV file is well-structured and contains the URL scraped, discovered email addresses, and matched phone numbers (if available). The deliverables for this project will include a meticulously organized CSV file with the specified information. Before signing-off my bid, I would like to ask a question, i.e., what specific criteria would you like me to use for verifying the deliverability of email addresses? Warm Regards, Aneesa.

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced web-scraping and data-processing specialist with strong expertise in Python, BeautifulSoup, Scrapy, browser automation, data cleaning, and structured CSV generation. I have rich experience extracting contact information from company websites, normalizing and deduplicating records, matching emails with phone numbers, and validating collected data for accuracy. I understand you need every valid company email and associated phone number extracted from your curated URLs, with duplicates, role-based addresses, and obviously invalid/fake inboxes removed before delivering a clean CSV. I am skilled in Python, BeautifulSoup, Scrapy, Selenium, data validation, deduplication, email verification, and CSV automation. A few questions: 1) Approximately how many company URLs are included in your list? 2) Do you have a preferred email verification service such as Hunter or NeverBounce? 3) Should I scrape only the provided URLs, or also follow relevant contact/about pages within each domain? I’m ready to start immediately and can deliver a clean, verified, marketing-ready CSV with consistent formatting and accurate source mapping. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@mjanchi

Hello there, as you can see from my profile I have experience in similar projects. How much data in total? Please contact me for more info Thanks

@malika335

Hi there! Building a robust Python web scraping pipeline using BeautifulSoup and regular expressions to parse company URLs, extract valid emails and matched phone numbers, remove duplicates/fake inboxes, and format everything into a clean CSV is right in my wheelhouse. I regularly build advanced web scraping pipelines and data extraction tools, so feel free to check my profile portfolio. Before we get started, could you share the list of company URLs or a quick sample so I can test the extraction against your target sites? Once you share the details, I can map out everything cleanly and deliver the runnable script and CSV file step-by-step. Looking forward to working together! Best regards, Ahmad Hassan

@temoberishvili

Hello! I see you're looking for someone to extract valid email addresses from a curated list of company-specific URLs. Your main goal seems to be gathering a comprehensive list of contacts to enhance your outreach efforts. I understand that accuracy and efficiency are crucial for this task. To ensure we get the best results, could you clarify if there are any specific formats or restrictions for the email addresses? Also, how will the extracted data be used? This will help tailor the approach. I have extensive experience in web scraping using Python and PHP, leveraging libraries like Scrapy and BeautifulSoup. I successfully completed projects involving data collection for various internal tools and marketplace applications. Some similar things I've built: a regional booking platform for a tutoring company, an internal CRM for a property agency, and a React Native field-reporting app. Looking forward to your thoughts!

@prprojectsl

Hi, I can build a Python-based workflow that follows each supplied company URL, checks the agreed page scope (for example, the landing, contact, and about pages), extracts emails and phone numbers, and preserves the source URL for every record. The cleanup stage would normalize addresses and phone formats, remove duplicates, reject malformed or obvious fake addresses, and filter role-based inboxes according to an agreed rule set. Where multiple contacts appear on one page, I would avoid inventing a match and use only defensible page-level associations. Deliverability checks can then be run through an agreed verification service, with its status used to exclude or flag uncertain results. Before fixing the price, I would need the URL count, required crawl depth, expected turnaround, and whether verification-service credits are supplied or should be included. I can also process a small sample first so the CSV structure and acceptance rules are clear before the full run. Share the list size and preferred verification method, and I’ll suggest a bounded workflow and price. Regards, Priyam

@durranigts

Hi, I can scrape each provided company URL and extract valid email addresses along with matching phone numbers where available. I’ll remove duplicates, filter out generic or suspicious addresses, and verify deliverability before delivery. The final CSV will be clean and structured with URL, Email, and Phone columns, ready for your marketing platform. I'm ready to start as soon as you share the list.

@Hrolgar

Most email finders choke on JavaScript-rendered contact pages, so the crawl step has to actually execute the page rather than just fetch raw HTML, or half the list comes back empty. For the verification pass I'd run each address through SMTP handshake checks plus MX validation rather than a simple regex, since that's the only way to catch catch-all domains and dead mailboxes before they hit a marketing platform and tank deliverability. Role-based addresses (info@, support@, contact@) get filtered against a standard prefix list, and phone numbers get normalized to one format since scraped ones tend to come in three or four different styles from the same site. Rather than a throwaway script, I'd build this as four small stages, crawl, extract, dedupe/clean, verify, each one a separate step writing its own intermediate file. That way when you hand me a refreshed URL list in three months it's a rerun, not a rebuild, and if a site changes its contact page layout only one stage needs fixing instead of the whole thing. Output is one CSV, columns matched to what your marketing platform expects on import, duplicates collapsed by domain and by normalized email, verification status included per row so you can see at a glance what's safe to send to versus what to hand-check. Milestone 1 covers the crawler and extraction, pulling emails and phone numbers off the full URL list and getting you a raw output to sanity check before anything gets cleaned or verified. Milestone 2 is dedupe, role-address stripping, deliverability verification, and the final formatted CSV. The 400 over 3 days is based on the list size and site complexity as described, once I see the actual URL list and how many are single-page brochure sites versus ones with contact info buried behind a locations directory, I'll firm that up. M1: crawl + extract raw emails/phones from full URL list, $160, 1d. M2: dedupe, role-address strip, deliverability verification, final formatted CSV, $240, 2d.

@arslanshahid386

Hi, I can help you with this project. I have relevant experience with PHP, Python, Web Scraping, Software Architecture and can handle the work from development to testing and delivery. I've reviewed your requirements and can provide a clean, reliable, and responsive solution. Let's discuss the details and get started. Best, Arslan Shahid

@mayankinnovative

Hi, As per my understanding: The bottleneck isn't extraction itself, it's that "accuracy over volume" usually breaks on pairing, most scrapers grab every email and phone number on a page independently, then mismatch a footer phone number with a header email, which produces clean-looking but wrong records. Implementation approach: I'd scrape each URL with logic that pairs emails and phone numbers based on page-section proximity rather than a page-wide regex sweep, so a contact block's phone stays tied to that same block's email instead of a random match elsewhere. Role-based and generic inboxes get filtered at that same step using a standard exclusion pattern, and duplicates get collapsed by domain before moving further. The surviving list then runs through a verification service like NeverBounce to confirm deliverability, so the final CSV is both correctly paired and reachable, ready for direct upload. A few quick questions: 1. Roughly how many URLs are in the initial list, so I can size verification costs correctly? 2. If a root URL has no visible email, should I check a level deeper, like a Contact page, or stay strictly on the given URLs? 3. Any specific role-based addresses you want excluded, or should I use a standard list?

@tmoi06

Hello Sir, I can extract and organize all genuine, usable email addresses and matching phone numbers from your provided company URLs, with a strong focus on accuracy and clean data. ✅ Why Me? ✔ Extensive experience in web scraping, web research, data extraction, and contact-list building ✔ Skilled with Python, BeautifulSoup, Scrapy, browser-based research, and structured data extraction ✔ Able to identify emails and phone numbers across contact, about, team, and other relevant website pages ✔ Strong focus on removing duplicates, malformed addresses, fake inboxes, and irrelevant role-based emails ✔ Experienced with email verification tools to validate deliverability before final delivery ✔ Careful matching of phone numbers to the appropriate URL/email record ✔ Comfortable handling large batches of company websites efficiently and systematically ✔ Clean CSV formatting suitable for immediate marketing-platform upload ✔ 500+ projects completed | 5.0-star rating I’ll prioritize quality over volume, ensuring the final dataset contains genuine and usable contact information while clearly excluding anything invalid, duplicated, or obviously unreliable. I can start immediately upon receiving the URL list and will deliver the results in the requested CSV format. Best regards, Ayan