The client's team started each day the same way: manually combing job boards, news feeds and directories for companies that might need their service — copying details into a spreadsheet, guessing at relevance. Slow, inconsistent, and impossible to scale.
The key insight: never pay an API to look at noise. Each stage aggressively narrows the funnel, so expensive AI reasoning only ever touches records that already passed cheaper local checks. The result is near-zero running cost.
Headless Chrome crawls 15+ sources daily, extracting structured data from JS-heavy sites.
A local Mistral model pre-screens every record, eliminating 80%+ of noise for free.
Claude scores the survivors against custom criteria and extracts the key signals.
The top-ranked leads arrive as a formatted message — company, score, direct link.
This is the real shape of a daily run. Watch thousands of raw records collapse into three leads worth a human's attention. Hit Run pipeline.
Drop in your own screenshots — dashboard, Telegram delivery, scoring detail.
"We went from dreading the morning lead hunt to just checking Telegram over coffee. The quality is better than what we found by hand — and it never takes a day off."
If a task is repetitive, rule-based and data-heavy, it can probably run itself. Tell me about it — I'll reply with an honest assessment within 2 hours.
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