GTM Growth Engine
A self-hosted outbound prospecting engine that discovers, classifies, and enriches private-lender prospects end-to-end. A Python worker runs scheduled classifiers, a Postgres warehouse holds 9,000+ scored prospects, and a Next.js dashboard drives the pipeline — from web-scrape detection to AI-drafted cold outreach.
01 Overview
Filling the top of the sales funnel with qualified private-lender prospects normally needs a sales-ops team and a stack of paid SaaS tools. The GTM Growth Engine does it solo: it discovers prospects, classifies whether they're a real lender, enriches them with business detail, scores them against an Ideal Customer Profile, and drafts cold outreach — all self-hosted.
It evolved out of — and absorbed — an earlier HubSpot lead-loading pipeline that built a pre-associated, deduped cold-outbound queue. What started as a loader became a full prospecting engine: one system that owns discovery, classification, enrichment, scoring, and outreach drafting from end to end.
02 How it works
A Postgres 16 warehouse is the system of record. A lender_type
detector runs regex over scraped site HTML, then an LLM enrichment layer adds loan products,
fund status, jurisdictions, and tech-stack detection — reaching ~99% coverage of the prospect
base. An APScheduler-driven Python worker runs 13 chained classifiers on a schedule;
search-API-powered discovery finds new prospects; and cold-email and sequencing integrations
handle outreach.
A Next.js dashboard sits on top to drive and monitor the pipeline. The whole stack is Dockerized behind a Traefik reverse proxy on a Tailscale-meshed VPS — the same production posture as any other service I run.
03 Engineering highlights
- Postgres 16 warehouse with a regex
lender_typedetector plus an LLM enrichment layer at ~99% coverage of the prospect base. - APScheduler-driven Python worker running 13 chained classifiers on a schedule, keeping every prospect continuously re-scored.
- Search-API-powered discovery plus cold-email and sequencing integrations for end-to-end outreach — from finding a prospect to drafting the first touch.
- Subagent web-check classifier used as the gold-standard ICP method — strict-JSON verdicts at ~96% precision on edge cases.
- Fully Dockerized stack behind a Traefik reverse proxy on a Tailscale-meshed VPS, with a Next.js operator dashboard driving and monitoring the pipeline.
04 Outcome
A one-person outbound machine that fills the top of the funnel without a sales-ops team — replacing a stack of paid tooling and manual research with one self-hosted pipeline.