An Agentic Lead-Generation Pipeline That Checks Its Own Work
Executive Summary
Built an AI lead-scoring and outreach pipeline that sources, scores, and drafts its own outbound messages, then hands each finished message off for a person to send. The first version ran on Make.com and Gemini; the version running today was rebuilt on Claude Code as a set of Python agents, with a Supabase Postgres database as the single source of truth. Over its life it scaled throughput roughly 3x — sourcing, scoring, and drafting outbound at volume — while a person still reviews and sends every finished message.
The Challenge: Manual Work at the Limit
Manual bottlenecks
Sourcing, screening, and data entry were linear manual work that capped how many leads could be handled at once.
Hours lost every week
Qualification and outreach prep ate roughly 20 hours a week, a hard ceiling on scaling the pipeline.
The Solution: An Agentic Pipeline, Rebuilt to Last
Automated sourcing and scoring
Scheduled collectors pull and deduplicate 100+ leads a day into one source of truth, and a model scores each against a written rubric before it reaches the board.
Generation that audits itself
Two model families split the work so nothing grades its own homework: one scores, another drafts the message, and a separate pass checks that draft against the source before it ships.
A human sends the finished message
The automation stops at a finished, source-checked document. A person reviews and sends it, so the pipeline is not a black box firing on its own.
The Breakthrough: Scale Without Losing Control
The pipeline moves each lead through the same path, unattended, up to the finished message — sourcing, scoring, and drafting at roughly 3x the original throughput, with a person sending every message that goes out. Hover any result to see it.
Scaled roughly 3x
About 20 hours a week back
Nothing grades its own homework
One source of truth
Monthly throughput, early vs later