Automated Supplier Price Scraper

@ZohaibRoy

⭐⭐⭐⭐⭐ Create Automated SKU Data Extraction from Supplier Portals ❇️ Hi My Friend, I hope you are doing well. I reviewed your project requirements and see you are looking for an automated data extraction solution. You need not look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for data scraping and automation. I will build a script that pulls every SKU, full description, and up-to-date price from the specified supplier portals, ensuring the data lands in a clean, structured CSV file. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in data scraping and automation. My expertise includes Python, BeautifulSoup, Scrapy, and handling web data extraction. Not only this, but I also have a strong grip on managing authentication and anti-bot measures across various websites. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Web Scraping ✅ Data Extraction ✅ BeautifulSoup ✅ Scrapy ✅ CSV Formatting ✅ API Integration ✅ Task Scheduling ✅ Authentication Handling ✅ Pagination Management ✅ Error Handling ✅ Documentation Waiting for your response! Best Regards, Zohaib

@hirujiyu

Hey there, I can replace the manual SKU by SKU checking with a reliable weekly scraper that collects complete supplier catalogues and produces a clean CSV ready for your quotation and AI workflow. ◆ How I’ll Build It: ➤ Use Python with Scrapy/BeautifulSoup , adding browser automation only where required for dynamic pages or authentication. ➤ Handle pagination, login/session flows, retries, rate limits, and site specific protections carefully while keeping each supplier scraper modular. ➤ Generate a consistent CSV containing SKU/code, description, unit price, packaging info, and date captured . ➤ Structure the code so new suppliers, fields, and extraction rules can be added without changing the core system. ➤ Provide a sample complete export from all three suppliers, documented source code, and a README/runbook for weekly scheduling via cron, GitHub Actions, or your server. An API endpoint exposing the collected catalogue data can also be added later without redesigning the scraper. I’d be happy to review the three catalogues and confirm the extraction approach. Thank you.

@OutsourceMan

Hello, We’ve reviewed your supplier price scraping requirements and can build a reliable, maintainable Python-based scraping solution for all three supplier portals. Our methodology: Site Analysis: Inspect each portal’s catalogue structure, pagination, product fields, authentication requirements, and access flow. Scraper Development: Use Python with Scrapy/BeautifulSoup and browser automation where required to extract SKU, description, price, packaging information, and other agreed fields. Data Normalization: Standardize data from all suppliers into one clean CSV format with SKU, description, unit price, packaging info, and capture date. Reliability: Add pagination handling, retries, logging, validation, and controlled request rates. Any authentication/access requirements will be handled according to the suppliers’ permitted access methods. Extensible Architecture: Keep supplier configurations and field mappings modular so additional suppliers or fields can be added without rewriting the core system. Scheduling & Handover: Provide a weekly cron/GitHub Actions-ready setup, sample exports, README, and troubleshooting guide. An API endpoint can also be added as a future extension. CnEL India can handle the complete Python implementation, testing, documentation, and handover.

@farhanyoussef18

Youssef, Full-Time Python Developer, specializing in automated data extraction and scheduling. Your goal is to automate weekly price checks from Mercante, ID Batacadistas, and Bartofil, outputting to a structured CSV. I will use Python with Playwright for robust JavaScript handling and anti-bot evasion, and BeautifulSoup for parsing. The script will manage authentication, session persistence, and pagination for each unique site. The CSV will have your mandatory columns: SKU/code, description, unit price, packaging info, and date captured. I've built many scheduled scrapers, including ones with configurable supplier modules. The core code will be separate from a configuration file (like a JSON or YAML) where you can add new supplier URLs and field mappings without modifying the script. For the weekly schedule, do you have a preference for a cron job on your server, or should I set it up with GitHub Actions? Ready to start immediately.

@techplusintl

Hi, I can automate the data extraction from the specified supplier portals by developing a robust web scraper. I will utilize Python with the BeautifulSoup and Scrapy libraries to ensure efficient data handling and extraction. To address potential challenges, I will implement strategies for authentication, pagination, and anti-bot measures, ensuring seamless access to the data. The final CSV will include mandatory columns such as SKU/code, description, unit price, packaging info, and date captured, structured for easy integration into your workflow. I will also design the solution to allow for easy configuration of new suppliers or fields without altering the core code, ensuring scalability for future needs. Deliverables will include fully documented source code, a sample CSV export from each site, and a simple runbook to guide you in launching the weekly job on your server. Additionally, I can provide an API endpoint as an optional add-on for future workflow extensions. Ready to start immediately!

@toriquldev123

Hello There! I’m Md Toriqul Islam, and I’m excited to partner with you. I can dive into your project immediately. I have rich experience in Python, Scrapy, BeautifulSoup, Playwright, web scraping, automation, CSV processing, APIs, and scheduled data pipelines. I understand you need an automated weekly scraper for Mercante, IDB Atacadistas, and Bartofil that collects every available SKU, description, current price, packaging information, and capture date into a clean CSV. I’ll handle pagination, authentication where required, dynamic pages, retries, rate limits, and site-specific extraction logic while keeping the system maintainable. I am skilled in Python, Scrapy, BeautifulSoup, Playwright, web automation, CSV processing, APIs, and scheduled jobs. I’m ready to start immediately and can structure suppliers and fields through configuration files so future additions won’t require changes to the core scraper. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@devendrathakur12

Hi, I’m Dev Singh, a Senior AI Integration Engineer with 20+ years in software engineering. I have gone through your specific requirement for supplier catalogue extraction I have built something close to this for business workflows handling 3 supplier sources, with Python automation and data pipelines. I would use Python Scrapy over Puppeteer where possible, because browser automation adds needless overhead for scheduled catalogue extraction. I would first map authentication, pagination and product fields for all three portals, at least that is where I would start. Then I’ll build supplier adapters with BeautifulSoup so new fields can be added without changing the core flow. Finally, I’ll generate the CSV and add a cron based weekly run, with captured dates for audit. For anti bot limits, I’ll respect each site's access rules and use browser automation only where the pages require it. API exposure can be added later with FastAPI. Screenshots and relevant automation samples I can send. Do all three portals require your login credentials today? Roughly how many SKUs should each weekly export contain? Are packaging details shown directly on product pages or in separate catalogue fields? Free for a quick call this week? Or answer those three and I’ll map the first version. Dev Singh

@juanpablomacf

The tricky part isn’t scraping three catalogs once—it’s making the weekly extraction reliable when each supplier handles authentication, pagination, product variants, and site changes differently. I’d design each supplier as an independent adapter so a change on one site doesn’t break the entire pipeline. I’d use Python with Scrapy/BeautifulSoup for standard pages and browser automation only where JavaScript rendering or authentication requires it. Each adapter would normalize results into the same CSV schema: SKU/code, description, unit price, packaging info, and capture date. I’d add validation for missing SKUs, duplicates, malformed prices, and unexpected page structures so failures aren’t silently hidden. For authentication, I’d use the supplier’s permitted login/session mechanisms and won’t bypass CAPTCHAs or access controls. Pagination and catalog changes would be handled explicitly with logging for failed pages or products. Supplier configuration would remain separate from the core scraper, making new suppliers or fields easier to add later. The weekly job can run via cron or GitHub Actions, with a clear README for deployment. The optional API can use the same normalized data layer. A few questions: * Do you already have accounts for all three suppliers? * Are prices public or only visible after login? * Should each CSV contain only current prices, or should historical price snapshots also be retained? Juan Pablo

@sagarwal3005

To automate the extraction of supplier data from the specified websites, I will develop a Python script utilizing BeautifulSoup for web scraping. I will implement handling for authentication and pagination while addressing anti-bot measures, ensuring a reliable data retrieval process. The CSV structure will include mandatory columns: SKU/code, description, unit price, packaging info, and date captured, which will be organized for your internal workflow. With a proven track record of a 4.9-star rating across 200 client reviews and 220 projects completed, my capabilities include web scraping and CSV automation. Can you provide more details about any specific authentication methods used by the supplier portals?

@ShaikhAneesa

Hi there, I understand you need a reusable Python scraper that can pull the complete product catalogues from Mercante, IDB Atacadistas and Bartofil, including SKU, description, current price and packaging information, then produce a clean CSV ready for your quotation and AI workflow. My approach is to build this with Python + Scrapy/BeautifulSoup, using browser automation such as Playwright only where the sites require JavaScript rendering. I’ll first map each supplier’s catalogue structure, then handle pagination, product variations, authentication where required, dynamic content and reasonable rate-limiting/retry logic while respecting each site's access rules. The extracted data will be normalised into one consistent schema and deduplicated, with the capture date recorded for every row. I’ll also keep the architecture configuration-driven, so adding another supplier, changing a field or adjusting selectors can be done through configuration rather than rewriting the core scraper. The weekly run can be packaged for cron/GitHub Actions or your preferred server environment. Deliverables will include the documented source code, complete sample CSV exports from all three sites, and a clear README/runbook covering setup, configuration and scheduling. Do the three supplier portals require account login, or should the scraper work entirely from their publicly accessible catalogues? I’m ready to start immediately. Warm Regards, Aneesa.

@hiberokpars

Hello! I will create a PHP script to scrape prices data you need Please provide the details I have extensive experience in writing PHP scripts for Price data scraping Please see my reviews for reference.

@denisSolution

Hi, I'm Denis, a developer who has built several data extraction systems that handle multiple sources and structured output. For this supplier price scraper, the main challenge is reliably pulling structured data from three Brazilian wholesaler sites that likely use dynamic content and anti-bot measures. I'll use Python with Scrapy for the scraping layer since it handles pagination and retries well, and BeautifulSoup as a fallback for any simpler pages. All three sites show catalogues without login walls, but I'll implement rotating user agents, randomized delays, and proxy rotation to avoid blocks. The CSV will follow your exact columns: SKU/code, description, unit price, packaging info, and date captured. I'll structure the code so new suppliers or fields can be added just by editing a simple configuration file—no core logic changes. For scheduling, a GitHub Actions workflow will trigger the scraper weekly and upload the CSV to your server via SFTP. If you need the data via API later, a FastAPI endpoint can wrap the same extraction logic with minimal extra work. The main risks are site structure changes breaking selectors and anti-bot systems flagging requests. I'll add basic monitoring in the scraper to catch failures early and log selector paths so updates are quick when sites change. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@seefattechnologi

GET clear communication------creative thinking-----Reliable delivery without constant follow-ups SURE------I will do it as per the given specification so lets get started and complete it-------Automated Supplier Price Scraper I am highly appreciative to work on this project. I am an Innovative Python web scrapper /Full stack developer having rich experience with so many successful Tasks. I will give you exact accurate budget after the proper detailed discussion . Let’s connect on chat for further discussion and start quickly. Thanks!!

@gowthamsarathy

Hello, The main challenge is turning three separate supplier catalogues into one dependable weekly data pipeline rather than maintaining three independent scrapers. The output needs consistent SKU, product description, pricing, packaging and capture-date fields regardless of how each supplier structures its catalogue, while authentication, pagination and changing page layouts are handled without producing silent gaps or duplicate products. Here’s my approach to the project: I’d use Python with Scrapy/BeautifulSoup where the catalogues are server-rendered, switching to Playwright/Selenium only where JavaScript or authenticated sessions require it. Each supplier would have its own adapter for login, pagination and field extraction, feeding a shared normalization layer that validates SKU/price data, removes duplicates, and generates the required CSV schema. Supplier configuration and field mappings would be separated from the core pipeline so additional suppliers or fields can be added without rewriting the scraper. Rate limiting, retries, session handling and respectful request scheduling will be built in, with anti-bot behaviour handled through compliant throttling rather than aggressive bypassing. Relevant Work: https://www.freelancer.in/projects/beautifulsoup/Maritime-Job-Board-Scraping https://www.freelancer.in/projects/beautifulsoup/Python-Meetup-Events-Scraper https://www.freelancer.in/portfolio-items/11493246-email-automation-system Kind regards, Gowtham

@rizwange4

As a seasoned software engineer and AI specialist, I believe I have the ideal skill set to tackle your need for a powerful automated supplier price scraper. Over the years, I've successfully built numerous data scraping tools and AI automation systems for my clients across different industries. This includes building custom MCP servers, creating scalable SaaS platforms and integrating AI chatbots into businesses workflow. I propose we leverage the power of Python and the capabilities of libraries such as BeautifulSoup/Scrapy which will scrape data from multiple sites efficiently and create well-structured CSV files. I have extensive experience dealing with complex authentication, pagination, and anti-bot measures, which will be crucial in extracting data from the supplier portals you mentioned. In conclusion, my proven expertise in developing AI automation workflows along with my deep knowledge of web scraping using Python makes me tailor-made for this project. Along with delivering fully documented source codes and run-time instructions after completion; I'm open to adding an API endpoint (optional) for your convenience in the future if you decide to extend your workflow. Let's kickstart this process together!

@maxscender

My approach centers on building a resilient Python-based scraper using Scrapy for its superior concurrency and built-in retry mechanisms, supplemented by BeautifulSoup for precise HTML parsing where needed. To handle authentication, I will implement session persistence that securely stores login tokens and automatically refreshes them when they expire, ensuring uninterrupted access to gated supplier portals. Pagination will be handled by dynamically detecting and following next-page links or infinite-scroll triggers, while anti-bot measures will be mitigated through rotating user-agent strings, configurable request delays, and an optional proxy integration layer that can be switched on if a supplier enforces IP-based rate limiting. The final output will be a strictly formatted CSV with columns for SKU, description, unit price, packaging info, and a timestamp for the date captured, ensuring the data is immediately compatible with your internal quotation system. For extensibility, the core logic will read from a single YAML configuration file where you can register new suppliers by simply specifying their base URL, login credentials, and CSS/XPath selectors for each field, eliminating any need to modify the underlying code when adding sources or new data points.

@ffulb

Brazilian supplier portals often sit behind session-based authentication and lazy-load prices via XHR after page render, which means you can't just curl the catalog page and call it done. I'd use Python with Playwright to handle the login flow and wait for the price elements to hydrate, then parse the DOM with BeautifulSoup once everything settles. For pagination I'd detect the "next" button pattern on each site and loop until it disappears. The CSV columns you want (SKU, description, unit price, packaging, capture date) map cleanly to a Pydantic model so the output stays consistent across all three suppliers. I'd put each site's selectors and auth credentials into a YAML config file so you can add a fourth supplier later without editing Python. For scheduling I'd wrap it in a bash script that activates the venv and writes to a timestamped CSV, then show you how to wire that into a weekly cron job or a GitHub Action if you prefer pushing the code to a private repo. I built a similar scraper for an Australian distributor pulling SKUs from four regional wholesalers (VPS Infrastructure Setup on ffulb.

@snapycode

Hi Tiago, I’ll build one weekly Python pipeline that exports SKU, description, unit price and packaging from Mercante, IDB Atacadistas and Bartofil into a clean CSV ready for your quotation workflow. I’ll first audit all 3 portals and deliver a working sample export from one supplier for you to validate. Then I’ll add the remaining suppliers using separate modules, so a change on one site does not break the full weekly run. I’ll use Scrapy/BeautifulSoup for standard catalogue pages and Playwright only when login or JavaScript pricing requires it. The script will use normal authenticated sessions, pagination handling, conservative request pacing, retry logging and validation for duplicate SKUs, blank prices and failed pages. CSV: supplier, SKU/code, description, unit price, packaging info, date captured. Supplier rules and field mappings will live in config files, so you can add suppliers or fields later without modifying the core runner. You’ll receive source code, sample CSVs, a README and cron/GitHub Actions setup. A small REST API can be added later. Regards, Raj

@ganeshrasekar

I can do it