Scaling Lead Scrapers with Smarter Lead Filtering

After the first round of scrapers went live, LeadForce360 needed more coverage and better consistency. Sources changed often, volumes kept growing, and not every scraped business was worth a call.

Expanded the scrapers to cover more sources and kept them stable as source sites changed

Maintained and optimized the pipeline in Node.js and Python so large runs finish reliably

Added a lead filtering step, including a simple scikit-learn model, to flag which leads are worth calling

Kept NAICS mapping and campaign exports running so the telemarketing team always had fresh lists

Combined across sources, runs typically collect between 100k and 3 million records.

The team gets bigger, fresher lead lists with less noise, so callers spend less time on businesses that were never a fit.

This was the second phase of an ongoing engagement with LeadForce360, building on the scrapers from the first phase.