Finn.no Scraper & CRM Uploader

@shivansh121

Hi, I have done many Data Scraping projects using selenium and BeautifulSoup library of python. Message me here. I am available to start the work . Looking forward to an early and positive response. Regards, Shalu

@raminde

Have over 18 years of experience in data mining/ Web scrapping/ Scraping Bots/ Chrome/Opera Extensions I have done it all. Tell us your source and we will put it in excel for you, Or we can even give you filtered results as per your requirement, In the format you want. You can also ask for data into a particular format - Excel, Json, Mysql, Databases, XMLs, you name them. Further Can help you with integrating it with ur databases, Can create json outputs. We are not only good with scraping but also with the tools that u may need after that. We can help you build you softwares round the data we have 99% Data Accuracy. We have Duplicate finder. etc., We can help with Statistics on the data We can help with creating Api's front the data We can create Softwares to manage that data We can build Sites round the data

@hiberokpars

Hello! I will create a PHP script to scrape data you need and upload/update/sync data into your CRM Please provide the details: what coluimns exactly will we scrape? how many records will be? I have extensive experience in writing PHP scripts for data scraping Please see my reviews for reference.

@FizzaNadeemK

Hi, you need a reliable pipeline that extracts structured property data from Finn listings and pushes it into Supabase without breaking formatting rules or data consistency between scraped and stored records. I’ve built automation and data workflow systems where structured scraping, transformation, and database syncing had to stay stable under changing HTML structures and strict schema requirements, including Python-based pipelines with external API integrations. I would build this in Python using Playwright or BeautifulSoup for resilient parsing of Finn sold listings, apply a deterministic transformer layer to split apartments vs other property types into separate CSVs with your exact schema rules, and then add a Supabase ingestion module that handles inserts with round-robin salesperson assignment and computed price fields based on square meter data, ensuring idempotent and traceable writes. The system would be modular so scraping, transformation, and database upload can run together or independently depending on your workflow. Do you want the Supabase upload to run immediately after scraping in one command, or as a separate step for validation before insertion? Best regards Fizza Nadeem K

@lazarr4

Hello, I recently built a scraper that pulled real-estate data from multiple Norwegian portals into a CRM. The parser handled inconsistent HTML and produced two clean CSVs—one for apartments, one for houses—with automatic type detection and blank Boenhet where required. Using Python + BeautifulSoup keeps the code simple and fast. A small Supabase client script will load the CSVs, compute price per square metre, and assign salesperson IDs in a round-robin loop so no record is left unassigned. I’ll wrap everything in a single CLI tool with one output directory; you can run scrape then upload or both at once. Every change will be in a separate branch and tagged so the CRM stays untouched until you approve. The code will include a small test suite you can run with pytest to verify the CSV headers and Supabase schema before touching production. When the script finishes, your CRM will have accurate, up-to-date listings without manual entry—exactly the data you need for follow-ups. If the approach looks right, I can share a sample output within a day so you can confirm the format before we move forward. I sincerely hope the project is completed successfully and exceeds your expectations.

@erincguray

Greetings, I am Erinc. I am a Full stack developer knowledgeable in PHP and Data Processing. I can help you with you need a small utility that tackles two tightly-linked jobs for you, scrape sold listings and paste any results URL into the tool. Looking forward to hear from you. Thanks for your consideration.

@toqeer74

Hi, I can definitely help with this. Here's how to approach it: I'll write a Python script using BeautifulSoup to scrape sold listings from Finn.no. It will export data into two CSV files as specified. For uploading to Supabase, I'll use the official client and implement simple round-robin logic for the salesperson ID. I can deliver the initial milestone in 2 days. Should we do a quick trial task so you can see the quality first-hand?

@mundadiya

Hi, I hope you're having a great day. I’ve delivered several Python scraping and ETL tools matching this scope, using BeautifulSoup and the Supabase client for uploads. On a recent project I parsed Finn-like listings with BeautifulSoup, extracted square metres and price-per-m, handled 2k items across paginated results, and used round-robin assignment for sales IDs. I’d start by inspecting the Finn HTML, build a parser with BeautifulSoup, produce the two CSVs with the exact headers, then use supabase-py to insert records and compute price from sqm. Could we hop on a short call to confirm your Supabase table names and the exact field that stores square metres? Happy to share sample CSVs and the README. Thank you, Alpeshbhai M.

@iterativeTech

As the team with expert skills in Data Processing, we, at Iterative Tech, can surely deliver the results you need impeccably. With a strong command over technologies like Python 3, BeautifulSoup and Playwright for web scraping combined with a deep understanding of Supabase for data manipulation, our solutions always give precise outcomes. H9e task requires us to handle large amounts of data and design an efficient Scraper and CRM Uploader, and our experience in projects involving complex data structures equips us well. Our proactive approach is to understand your requirement thoroughly and ensure the smooth functioning of your project. We will take care of every detail you mentioned- from parsing the HTML to exporting the CSV files as per your specifications. We will also build a companion script to push data into the required Supabase tables considering all your rules like assigning salesperson ID using round-robin logic and calculating price field ect. Lastly, it is our utmost priority to put your satisfaction first. Through constant communication and quick turnarounds, we will ensure a seamless project execution that meets your requirements precisely. Attractive pricing and guaranteed resultswith enduring support are waiting at your fingertips with us!

@noryidos

Hi, I've checked your requirements and I have some questions. 1. Supabase schema and assignment logic Could you share the Supabase table schema (columns, data types, and constraints) and explain how the salesperson assignment should work? For example, should the round-robin sequence continue across multiple runs, or restart each time the script is executed? 2. Price calculation You mentioned the price should be calculated from the listing's square metres. What is the exact pricing formula or business rule? Is there a fixed rate per square metre, or should it be derived from another value in the listing? 3. Scraping scope and edge cases Should the scraper process every page of the sold search results automatically, and are there any edge cases I should handle (e.g., duplicate listings, missing address fields, unavailable square metres, or changes in the HTML structure)? I hope to discuss more via chat. Best Regard, Nory Idos

@abdelhafidg

Hello, Your project is a strong match for my experience in Python automation, web scraping, API development, and Supabase integrations. I would build the solution as a clean, modular Python application using BeautifulSoup or Playwright (depending on the page structure) to extract the required property information, generate the two CSV files with the exact format you specified, and then upload the data directly to Supabase using the official Python client. The upload process will include configurable round-robin salesperson assignment, automatic price calculation based on the property's square metres, validation to avoid malformed records, and clear logging for traceability. The final deliverable will include well-structured, documented source code, sample CSV outputs, a README covering setup and environment variables, and an easy-to-run workflow for both scraping and uploading. I focus on building maintainable automation tools that are reliable, easy to extend, and simple to operate. I look forward to discussing your project. Best regards, Abdelhafid Guesmia AI/ML & Python Engineer

@catalinb24

Hi, I can build this. I'll use Python with BeautifulSoup for the scraping part, parsing the sold listings page and pulling out the address fields, property type, and the Finn link for each entry. For the split logic, apartments go into one CSV with Boenhet left blank, everything else goes into the second with Boenhet set to H0101, matching your header order exactly. For the Supabase side, I'll use the official client to push both CSVs into your tables, assign a salesperson ID with simple round-robin as records come in, and calculate the price field from the square metres already in the listing before storing it. I'll structure the scraper and uploader as separate but connected pieces, so you can rerun just the scrape or just the upload independently if you ever need to. You'll get the source, two sample CSVs from a test URL, and a README covering setup, environment variables, and how to run both parts. Can deliver in 3 days.