Natural Language Search vs Boolean Operators: Why We Chose Plain English
Most lead generation tools make you think like a database engineer. We built Scrapll to think like a human.
Every lead generation tool on the market makes you build complex queries: industry equals this, company size between that, location in these cities, job title contains these keywords.
It works. But it's slow, error-prone, and requires expertise that most SDRs don't have — and shouldn't need.
The problem with filters
Boolean filter interfaces have two fundamental issues:
1. They're rigid. You can only filter on dimensions the tool supports. Want to find "companies that just launched a product on Product Hunt"? Good luck expressing that in dropdown filters.
2. They miss intent. The best leads often don't fit neatly into predefined categories. A "SaaS founder who's actively hiring SDRs" implies growth-stage readiness, but that's an inference — not a database field.
Why natural language works better
When you describe your ideal customer in plain English, you're expressing intent, not just attributes. Our AI parses these descriptions and translates them into multi-source search strategies that capture both explicit criteria and implied signals.
"B2B startups in fintech that raised Series A in the last 6 months" tells us more than any filter stack could — it implies growth, hiring, and active spending. Our pipeline uses that context to score and rank leads by relevance.
The technical challenge we solved
Natural language search sounds simple, but making it work reliably required building an 8-stage AI pipeline: query parsing, company discovery, contact finding, email verification, data enrichment, lead scoring, and summarization. Each stage is optimized independently, and the entire pipeline completes in about 3 minutes.
The result: you type what you're thinking, and you get verified leads that actually match your intent.