Python Tool: Swiss Company Leads

@adeelpirzada

Hi, I am Adeel. I built Boostifai, an AI SaaS that crawls sites and pulls structured data at scale, backed by Python for the extraction and enrichment logic. That is the same shape of problem as discovering companies and mining contact details from their websites. For discovery I would use the Google Places API per municipality, paginate the results, and dedupe on place ID and normalized address. For website analysis I would crawl each site's contact and about pages, pattern match general inboxes, and parse team pages for named contacts and titles, with clear logging so a failed run resumes rather than restarts. Given the scope, I would start with a pilot on five municipalities so you can judge accuracy and real Places API cost before committing to the full 30000 companies. Which Swiss regions or cantons are the priority to start with?

@imraz2016

Hi, I can build this as a modular Python/Windows 11 pipeline covering municipality-based company discovery, deduplication, website crawling, general/direct email extraction, executive identification, resumable processing, logging and structured XLSX export. For ~30,000 companies, I would use the official Google Places API for discovery rather than direct Google Maps scraping, then crawl the companies’ own websites for contact enrichment. I’ll also provide a transparent cost model covering Google/API usage at 10K, 100K and full-run scale before implementation. I can use your supplied Swiss municipality Excel file as the input structure and provide a small working sample first.

@Tanmoy236

Hi There! I specialize in Python automation, API-based company research and scalable Excel data pipelines. With 9+ years of experience, 450+ completed projects, Top 1% verified freelancer, I can build a Windows 11 tool that processes your municipality Excel, discovers companies via Google Places API, deduplicates records, analyzes websites for general/personal contacts, and exports structured XLSX results. I’ll design it with resumable processing, logging, rate-limit/error handling and modular components. I’ll also provide a clear cost model covering Google Places/additional APIs, third-party services, cost per 10K/100K requests and the estimated 30,000-company run, while evaluating compliant alternatives where they improve coverage or cost. Would you like me to base the initial architecture on Google Places API and then benchmark an alternative source against it for coverage and total cost?

@farhanyoussef18

Youssef, Full-Time Python Developer, expert in large-scale data extraction and automation. Your requirement is a fully automated tool to find Swiss companies per municipality via Google Places API, then analyze their websites for contact details and personal emails. I will build a Python solution using the Google Places API for initial discovery, with deduplication logic. For website analysis, I will use Playwright to scrape and parse sites, implementing regex and common patterns to reliably extract both general emails (info@, kontakt@) and personal contacts by searching for executive titles like CEO or Head of Sales. The system will include logging, resume capability, and output to Excel as specified. I have completed several similar B2B lead list building projects. What is your preferred primary data source if Google Places API coverage for smaller Swiss municipalities is limited? Ready to start immediately.

@intelliresponse

With over 13 years of experience in Python development, API integration, and web scraping, I believe I am the ideal candidate for your project. My expertise lies in developing tailored python web automation, scraping, and AI solutions. In similar projects like yours, I have built automated systems that analyze websites comprehensively to identify companies and collect relevant business information such as contact numbers and email addresses. I will leverage the Google Places API along with my knowledge of web scraping tools like BeautifulSoup and Scrapy to provide accurate data with a high success rate. I have carefully assessed the technical requirements of your project and can assure you my solution will meet all your needs. The final results will be conveniently exported to an Excel (.xlsx) file for easy access and analysis. I'll provide well-documented code along with installation guides and a user guide for your team's convenience. The modular architecture of my solution ensures its scalability and future expansion potential, ensuring you won't need to modify or recreate it for changing needs. On API costs and operating costs which has been asked to submit together with the proposal, it’s something we could discuss directly in order to reach an accurate estimation.

@onlinejob247

Dear client, I can do this job of Python Tool: Swiss Company Leads accurately as per your requirements and available to start immediately. Thanks!

@w3saddam

Hi, I can build this as a scalable Python solution for Windows 11, covering municipality-based company discovery, website crawling, email/contact extraction, deduplication, logging, resume support, and Excel export. I’d use the Google Places API where appropriate and clearly compare alternatives for coverage/cost. I’ll provide complete source code, requirements, documentation, user guide, sample output, and a transparent breakdown of API, infrastructure, and recurring costs. I can design the workflow for ~30,000 companies with maintainability and compliance in mind.

@devendrathakur12

Building the Swiss company research pipeline is straightforward work for me — I’d build a Windows 11 Python pipeline around pandas, Google Places API, openpyxl, requests, and SQLite checkpoints, with municipality Excel input and resumable batches for roughly 30,000 companies. I’d use Places Text Search/Place Details for discovery, then crawl each permitted company website with requests and BeautifulSoup, extracting mailto: links, contact pages, structured data, and role/name/email combinations while storing every Source URL. Deduplication would combine Place ID with normalized company name, postcode, address, and domain. A likely issue is the same company appearing under multiple municipalities or Place IDs; I’d flag these before XLSX export. Google API cost depends on request mix, so I’d provide the exact per-request and 30,000-company calculation from your selected billing tier, plus infrastructure costs. Send the municipality Excel file here in chat, and I can start immediately.

@denisSolution

Hi, I'm Denis, a full-stack developer with experience building data collection and processing systems involving APIs, web scraping, and large-scale data pipelines. I understand your project: an automated system that discovers companies through an API, extracts contact data from websites, and delivers a clean Excel export. This is a solid lead generation workflow. For company discovery, Google Places API is ideal—reliable, well-documented, and comprehensive for Swiss businesses. I'd query each municipality by category, handle pagination, and deduplicate results. For 30,000 companies, expect 10,000-15,000 API calls depending on density and filtering. Website analysis combines pattern matching for common email formats and HTML parsing for contact pages. I'll extract from team pages, leadership sections, and email signatures using targeted parsing and NLP for job title matching. The solution includes modular Python code with error handling, resume capability for interrupted runs, comprehensive logging, and Excel-based configuration. It runs on Windows 11 with only standard Python packages. Costs: Google Places API runs ~$0.032 per call, so 30,000 companies costs roughly $300-500 in API fees. I'll provide detailed cost breakdowns covering all APIs and recurring expenses. Website scraping success rates vary by site structure, so I'll build quality metrics and reporting so you understand coverage per municipality. I'm ready to start immediately. Thanks, Denis.

@juanpablomacf

My name is Juan and I am an experienced full-stack developer and AI implementation expert with a knack for turning intricate business challenges into scalable solutions. With a reputation for architecting systems that not only encompass your existing needs but prepare you for future growth, I would be the perfect fit for your project. Building upon my proficiency in API development and integration along with my deep knowledge of Python and Excel, I would leverage the Google Places API for company discovery. My approach involves creating a robust and scalable architecture that ensures maximum efficiency and minimal duplicate entries. The solution would then analyze each website thoroughly using automation and advanced web scraping techniques. I firmly believe in maintaining high accuracy standards when it comes to data processing. Given the volume of companies involved here, my estimates suggest identifying several thousand companies per municipality within reasonable timeframes. I My extensive experience with API usage allows me to calculate monthly operating costs down to per-request level, which I will efficiently do to provide you a feasible budget plan. Overall, I have no doubt that my skills, expertise, and in-depth project understanding make me the right choice to transform your ambitious idea into an executable reality. Let's schedule a call and chart your leads acquisition journey together!

@PINESCRIPTERR

Hi, I understand you need a scalable Windows 11 Python solution that starts from Swiss municipalities, discovers companies, analyses their websites for business and professional contacts, removes duplicates and produces a structured Excel dataset. I would use the Google Places API as the primary discovery source where its coverage and usage model are appropriate, while evaluating complementary compliant sources where they can improve coverage. I would not rely on direct Google Maps scraping where it conflicts with applicable terms or creates an unreliable long-term dependency. The website-analysis pipeline can asynchronously process company domains, extract publicly available contact pages and mail addresses, identify relevant professional names/roles from public business content, and record the source URL for every extracted contact. Deduplication can combine Place ID where available with normalised company name, address, phone and domain matching. The Python application will be modular, configurable through Excel, resumable after interruptions and designed for large batches using controlled concurrency, retries, rate limiting, caching and structured logging. It will include the requested source code, requirements, documentation, sample input/output and Windows 11 setup instructions. Best regards, Houssame

@seefattechnologi

Hi, For 30,000 Swiss companies, the main challenge is not writing a scraper. It is building a resumable discovery pipeline that gets broad municipal coverage, avoids duplicate companies, respects source terms, and finds usable website contacts without turning API and crawling costs into a problem. I would build this in Python with Excel-driven municipality input, Google Places API as the primary discovery source, strict field masks, caching, batching, deduplication by Place ID/domain/address, and checkpoint-based processing so an interrupted 30,000-company run can resume safely. Website analysis would then extract general and relevant personal contacts from permitted public pages, with source URLs and collection dates stored for verification. For cost control, I would use Places IDs where possible and request only required fields. Current global Google pricing shows Places Text Search Pro at $32/1,000 events after the 5,000 free monthly events, while Place Details Essentials is $5/1,000 after its free tier. Key question: Does the attached municipality Excel contain only municipality names, or does it also include municipality/postal codes that can be used to improve geographic querying and deduplication? Please ping to get started and get outstanding results. Thanks!!!

@AndrewJSMAN

I've built similar Swiss business data pipelines before, so this is right in my wheelhouse. I'll use Google Places API for company discovery, then scrape websites with BeautifulSoup and regex for email extraction, and deduplicate with fuzzy matching on company names and addresses. The system will log errors, resume on failure, and export to Excel. Costs will include $700 for Google Places API (30k requests at $0.022 each) and $200 for website scraping (rate-limited to avoid bans). Processing 30k companies will take ~48 hours. I can start immediately. Thanks, Andrii.

@ffulb

The Google Places API caps at 60 results per query, so 30k companies across Swiss municipalities means you'll hit that ceiling fast and need a fallback scraper for the long tail. I'd build a dual-source tool: Places API for the first wave (fast, clean data, good for main streets), then a Playwright-based Google Maps scraper with residential proxy rotation for the rest, all writing to SQLite with deduplication on name+address hash so you can pause and resume without burning API credits twice. I've built similar pipelines before (trading platform QA with API audit, PDF processing for 500+ pages). This one would be a Python CLI tool you run on Windows 11, takes your Excel in, spits out CSV with all ten fields plus a log of what hit rate limits or got blocked. Phase 2 (website crawling for emails) would be a second pass: load the CSV, fetch each site with httpx, parse with selectolax, write contacts back into the same table. I'd include regex patterns for common email formats and a simple scorer so you get the most likely contact, not every address in the footer. Check ffulb

@juanjosec63

Hi, I have 9 years experience in software development, with hands-on experience in Python, data processing, Excel automation, web scraping, API integration, data mining, MySQL/PostgreSQL, and scalable automation systems. I have gone through your project description and understand you’re looking to automate Swiss company discovery from municipality Excel files, enrich company data, analyze websites for business and personal contacts, remove duplicates, and export the results to Excel for approximately 30,000 companies. For this project I can build a modular Python pipeline using Google Places API as the primary discovery source, with municipality-based searches, pagination, Place ID deduplication, website crawling, email extraction, contact/role detection, retries, logging, checkpointing, and resumable processing. I’ll keep Google requests optimized through field masks and only request the data required for each stage. Google currently uses SKU-based pricing with free monthly usage per SKU, so the final API cost depends on the selected Places endpoints and request volume.

@ozbas

With my extensive 10-year background in Full-Stack software engineering and proficiency in languages such as Python, I am well-equipped to build the automated tool you need for researching Swiss companies. My expertise is perfectly aligned with your project requirements as I have a solid understanding of API development/integration and data mining. In addition, my Excel skills will be essential in providing a smooth user experience through configurations and an organized output. Regarding data sources, while Google Places API would be our primary channel, I am always open to exploring additional options that could improve efficiency and accuracy. In the same vein, I am more than capable of developing a mechanism to identify duplicate records using robust algorithms which will ensure the validity of your database. As far as operating costs are concerned, I commit to giving you a comprehensive estimate including all APIs, data sources, Google Places API costs, Google API costs, third-party services costs(if applicable), monthly operating costs as well as cost per request attributes. Combined with my wide-ranging skillset and solution-oriented approach, this makes me an ideal candidate, ensuring a successful completion of your project well within deadlines and budgetary limits.

@sabaehsan

Greetings, I’m excited about the opportunity to help you develop an automated solution for researching Swiss companies. By using the Excel file with municipalities, I will implement a process that identifies companies, gathers essential business information, and analyzes their websites for contact details. My plan is to leverage the Google Places API for company discovery and ensure compliance with all terms of use. In the first phase, I will focus on extracting relevant company data, addressing duplicates effectively to maintain accuracy. The second phase will include a thorough analysis of websites to find both general and personal email addresses, prioritizing key contacts. I will ensure the solution is fully automated, scalable, and user-friendly for Windows 11. I’m confident that my experience with Python and data processing will result in a reliable tool that meets your needs. Best regards, Saba Ehsan

@rosscarter1011

Hey, looks like you need a Python script to pull around 30k companies across Swiss municipalities using Places API and then crawl their sites for emails and staff contacts. I usually handle this kind of pipeline with requests and BeautifulSoup backed by a SQLite queue so you can easily pause and resume without losing progress. Dealing with rate limits on Google and handling messy website structures is the trickiest part of these scrapers. Are you planning to run this locally on Windows via a command line script, or do you need a simple GUI wrapper for it?

@vishal1145

Your Google Places API will hit quota limits if you query 30,000 companies without batching requests and caching results. This creates cost overruns and incomplete data extraction. Quick questions - are you planning to run this as a one-time extraction or recurring updates? And do you have existing Google Cloud credits, or should I architect this to minimize API calls from the start? Here is the architectural approach: - GOOGLE PLACES API: Implement tiered search using Text Search API with municipality boundaries, then enrich via Place Details API only for missing fields, reducing cost from ~$900 to ~$200 per full run. - WEB SCRAPING: Deploy Scrapy with rotating user agents and rate limiting to extract emails from company websites, parsing HTML and PDF documents for info@, kontakt@, and executive contacts using regex patterns and NLP entity recognition. - DATA PROCESSING: Build deduplication pipeline using fuzzy matching on company names plus address normalization, then export to Excel with resume-from-checkpoint logic so interrupted jobs don't restart from zero. I've built similar lead generation systems for 2 B2B clients that processed 50K+ European companies without API bans. Let's schedule a 15-minute call to confirm your Google Cloud setup and finalize the cost model before I start development.