AI GTM Enrichment Pipeline · Raymond Fenton

Three versions.
One problem.
Not finished.

Fleet Engine is an AI pipeline that turns job listings and funding signals into warm, grounded outreach. Three agents work as one crew: Ahab hunts the open water for companies showing the right signals, Nemo dives deep into each one and surfaces the friction behind why they're hiring, and Neptune — commanding the whole sea — synthesizes an outreach bite that speaks to that exact pressure. This is three versions of building them until they ran clean.

Select a version to enter
V1 · Archive
Fleet Engine: Fathom

Three agents over n8n, Postgres, and Docker on GCP. 1,500 stress-test cycles across six campaigns. Broke 18 times. Every failure documented forensically before the rebuild.

3 agents 15 funded leads 18 failure modes 1,500 cycles
V2 · Live
Fleet Engine: Cortex

Claude Code as Admiral. ADK on Agent Engine. GCS file handoff. Three Vertex AI RAG corpora compounding across runs. Clay delivery. Same agents, new substrate.

Python ADK GCS handoff 3 RAG corpora Clay → HubSpot 0 self-hosted
V3 · Locked
Self-Improving Pipeline

Reply outcomes flow back into the RAG store. A fourth agent uses that signal to improve outreach before delivery. The pipeline learns what converts.

Polish agent Outcome feedback loop RAG compounds Reply signal
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Locked
Unlocks after V2 Phase 2.