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Autonomous AI Sales Pipeline — SAP B1 Lead Generation

  • 2026 – Present
  • Internal Project
  • 200h
ai-mlagent-frameworksautomationsales-pipelinegcpinfrastructure

Built a fully autonomous multi-agent AI sales pipeline using OpenClaw on GCP. 6-agent team: Scout (lead scraping from partner case studies), Qualifier (composite scoring with identity/event/contact dimensions), Verifier (SAP B1 confirmation), Enricher (contact discovery via Apollo/Hunter/theHarvester/Perplexica cascade), Signal Hunter 'Pulse' (intent-based scoring using Gemini/Groq for hiring/funding/complaint/migration signals), and Closer (email generation). Custom pipeline database with SQLite, real-time dashboard on port 3000, WhatsApp gateway integration, automated signal sweeps on cron. Built from scratch in 3 weeks.

The problem

Finding SMBs that actually run SAP Business One — and reaching the right person with a reason to talk — is slow, manual, and easy to get wrong. The goal was a sales pipeline that could find, qualify, verify, enrich, and prioritise leads on its own, surfacing only the ones worth a human's time, with evidence for every score.

The approach

A fully autonomous multi-agent pipeline built on OpenClaw on GCP. Six specialised agents run on cron schedules and hand off through a shared pipeline database — no human in the loop after setup:

  • Scout — scrapes leads from partner case studies and the open web.
  • Qualifier — composite scoring across identity, event, and contact dimensions.
  • Verifier — confirms the prospect actually runs SAP B1.
  • Enricher — discovers contacts via an Apollo / Hunter / theHarvester / Perplexica cascade.
  • Signal Hunter ("Pulse") — intent scoring from live web signals (hiring, funding, complaints, migration) using Gemini / Groq.
  • Closer — drafts the outreach email.

Architecture

Each agent is independently schedulable and writes to a SQLite pipeline database that tracks a lead's state through the funnel. A real-time dashboard renders the pipeline; a WhatsApp gateway pushes notifications; signal sweeps run on cron. Built from scratch in roughly three weeks and deployed to a VPS.

What it produced

The run scored and classified 1,084 leads end to end, of which 644 landed on the client track (avg score 7.8/10), 569 were fully researched by the agents, and 75 were verified as confirmed SAP B1 users — all with zero manual intervention after the initial setup. (This pipeline later became one of the automations that informed the broader AgentOne platform.)

Outcomes

  • 1,084 total leads scraped and classified autonomously
  • 644 client-track leads with avg score 7.8/10
  • 569 leads fully researched by AI agents
  • 75 leads verified as confirmed SAP B1 users
  • Multi-agent orchestration: 6 specialized agents running on cron schedules
  • Intent signal detection: hiring, funding, complaint, and migration signals from live web data
  • Composite scoring combining static profile data with live behavioral signals
  • Zero manual intervention after initial setup

Tech Stack

Python
Docker
GCP
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