The Day I Realized We Were Wasting 20 Hours a Week
Every afternoon, one person disappeared for 4 hours into LinkedIn.
"Find me 50 marketing directors at Series B SaaS companies in the US."
They'd copy names. Paste into a spreadsheet. Copy email addresses. By the time they were done:
- Half the titles were wrong (LinkedIn updates them constantly)
- 15 emails bounced (people had changed jobs, updates didn't sync)
- 3 were the same person on different accounts (we didn't catch that)
- We paid $200 to do work that generated garbage data
I watched this happen every single week and asked: Why isn't this automated?
That question took me six weeks to answer. Now we process 500 leads/week automatically. Accuracy is 97%. No humans required.
The Real Trick: Deduplication
Here's what killed our old process: Sarah Chen exists 3 times in our CRM:
Our sales team thought we had 3 prospects. We had 1 person. And we were calling her three times.
The system deduplicates by email + domain + LinkedIn profile with 99.1% accuracy. It consolidates records, preserves history, and stops SDRs from embarrassment.
The Numbers
| Metric | Manual | AI |
|---|---|---|
| Leads/week | 50 | 500+ |
| Accuracy | 65% | 97% |
| Time/week | 20 hrs | 2 hrs |
| Cost/lead | $120 | $8 |
| Close rate (80+) | 8% | 34% |
That 34% close rate: the AI isn't magically better at closing. It's just better at finding people ready to engage.
What I want to know from you:
How are you handling deduplication today? Our 99.1% accuracy feels good but I'm curious if there's a better approach.
What's your bottleneck with lead sourcing? Is it finding leads, scoring them, or keeping the data clean?
When you score leads, do you weight all signals equally or prioritize intent? We found behavioral signals matter more than company size.
If you've automated this and found patterns we missed, open an issue.
