AI Lead Intelligence & Qualification Engine

Turning raw company signal into scored, verified, contactable prospects, with stop conditions built in.

Sole builder and operator · independently built production infrastructure · not client work

01 / CONSTRAINT

Constraint

Most lead pipelines score volume. Scoring fit takes real evidence per account, and pipelines that skip that step spend outreach budget on companies that were never going to buy. The expensive failure is not a bad email. It is a good email sent to the wrong company.

02 / ARCH

Architecture

signal intake  ->  enrichment  ->  qualification gate  ->  LLM scoring + confidence
                                                                |
                                          below bar  ->  hold list (never contacted)
                                          above bar  ->  CRM state  ->  routing  ->  outreach handoff
                                                                |
                                    reply / opt-out / bounce  ->  suppression

03 / FAILURE

Silent Failure

The scoring layer reported clean for a week while having scored nothing.

04 / TRACE

Trace

Found by walking the graph backward from the scorer and checking output against source state rather than run status.

05 / FIX

Fix

The routing node upstream recognised only legacy source tags and had no branch for the channel actually producing leads. Fixed with a direct intake by the real tag.

06 / VERIFY

Verification

Verified against the next live run and the real records, not the workflow's status field.

07 / EVIDENCE

Evidence / Stack

Every qualified record carries the evidence and confidence that produced its decision, so a call can be audited after the fact rather than trusted.

n8n
Airtable
Supabase
Apollo
LLM classification
HTTP / API integration

08 / CTA

CTA

Happy to walk through the live workflow canvas on a call.