This is the pipeline when the source is a single prospect URL. A B2B data-analytics company was selling advanced analytics into enterprise accounts ($5–10M+ revenue), with outreach run by the Head of Sales and a team of SDRs. I built them an enrichment pipeline that takes a LinkedIn URL or domain, extracts and normalizes intelligence well beyond firmographics, and drops a clean, structured profile into their sheets and CRM. It is the same extract-and-route job at the heart of every build here, and it lifted sales 10% in the first month.
Results at a glance
- 10% sales lift within the first month
- Under 3% false positives
- 500–1,000 leads enriched per day
- $0.06 per enriched lead
- Delivered in two weeks, half the planned timeline
The problem
Selling into high-stakes enterprise accounts needs more than surface-level data. Off-the-shelf tools like Clay and Apollo missed the context that actually earns a reply: hiring signals, executive and media appearances, and org-level figures such as analyst headcount. Without it, outreach felt generic, research was slow, and strong prospects slipped through.
"We tried existing platforms, but the costs were too high and the answers were often not good enough."
What I built
A production-grade n8n pipeline that turns a single LinkedIn URL or company domain into a standardized, data-rich profile, ready for outreach, CRM, and alerts.
- Input: paste a LinkedIn URL or domain into a Google Sheet
- Sources: Apify scrapers, targeted AI research, and curated public web data
- Output: hiring signals, appearances, org insights, normalized job titles, and deduped company and contact fields
- Operations: retries with backoff, full run logs, and Slack alerts on failure
The whole thing is self-hostable and controlled entirely from a Google Sheet. The team was running it on live leads the same day it shipped.
The results
In the first month running the new pipeline, the team's sales rose 10%. A single change like enrichment is never the only factor behind a sales number, but the directly measurable wins were unambiguous: research that was manual now runs in under two minutes per lead at $0.06 each, and the build landed in two weeks against a one-month plan.
| Before (Clay / Apollo) | After (custom automation) |
|---|---|
| Surface-level data | Deep enrichment: hiring, appearances, org signals |
| Slow, manual enrichment | Automated in under two minutes per lead |
| Generic lists | Standardized, actionable profiles |
| Missed prospects | Sharper targeting and stronger follow-up |
"When we started, we didn't really know what the end product should look like, but Noah made sense of our rough ideas, built exactly what we needed, and delivered more than we expected. The iterative process was a lifesaver."
Built with: n8n, Apify, AI research, Google Sheets, Slack.
Want enrichment like this for your team? Tell me your stack and goals and I'll send back a build plan and timeline.