Pulling lawyer data from firm sites is absolutely doable, but it does involve scraping pages that mix static and JavaScript rendered content, so the approach needs to balance completeness with each site’s technical and legal constraints. Preferred toolchain is Python with Requests and BeautifulSoup for simpler, static pages, and Selenium for sites that rely on JavaScript, pagination or load more buttons, combined where needed so Selenium handles interaction and BeautifulSoup handles clean parsing. This pattern is widely used for dynamic sites and works well for click to load and infinite scroll layouts when paired with sensible waits and rate limiting. The workflow would be to inspect each of your fifty plus firm sites, confirm that extracting only publicly available, professional contact details is compatible with their terms and robots rules, then build a reusable scraper that yields one row per lawyer with separate columns for firm level contact info and the attorney fields you listed. Throughout, strict rate limiting and error handling keep requests polite and reduce the risk of triggering blocks, in line with current guidance on ethical, legally careful scraping. You would receive a single CSV or Excel file, the Python scripts or notebooks used, and a short read me explaining configuration, how to run the scrapes again if your firm list changes, and how to adjust selectors if a site layout is updated. Status updates every 10 to 15 sites help catch quirks early so the extraction logic can be tuned quickly rather than fixed at the end.