A Technical Guide by Leon Basin | basinleon.com

TL;DR

You don’t need a $50k/year tech stack to build serious revenue intelligence. This guide shows you how to assemble a complete GTM system using free tools: n8n for orchestration, free enrichment APIs, local LLMs, and Python glue code. Total cost: $0. Total capability: enterprise-grade.

The Problem: GTM Tools Are a Tax on Growth

Every Series A founder knows this pain: you need revenue intelligence, but the SaaS vendors want $2k/month per seat. By the time you’ve bought:

• CRM ($150/user/mo)
• Enrichment ($500/mo)
• Outreach automation ($200/user/mo)
• Intent data ($1,000/mo)
• Analytics ($300/mo)

…you’re spending $20k/month before closing a single deal.

The alternative: Build the 80% yourself, buy only what you can’t replicate.

The Architecture

The $0 GTM Stack has three layers:

1. Signal Ingestion — Google Sheets, Webhooks, RSS feeds, LinkedIn Export
2. Enrichment + Scoring — Hunter.io, Clearbit, GitHub API, Local LLM
3. Output Layer — Executive Brief, Cold Email, CRM Update, Slack Alert

All orchestrated by n8n — self-hosted, unlimited executions, visual workflows.

Layer 1: Signal Ingestion

What We’re Building

A unified intake layer that captures signals from wherever your prospects live.

Free Tools

Code Snippet: URL Ingestion

# logic/ingest.py
import requests
from bs4 import BeautifulSoup

def fetch_text_from_url(url: str) -> str:
    """Ingest article content from any URL for signal extraction."""
    response = requests.get(url, timeout=10)
    response.raise_for_status()
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # Try article tag first, fall back to body
    article = soup.find('article')
    if article:
        return article.get_text(separator='\n', strip=True)
    return soup.body.get_text(separator='\n', strip=True)

Layer 2: Enrichment + Scoring

What We’re Building

Automated data enrichment that fills in company/contact details, then scores for priority.

Free APIs

The Scoring Engine

# logic/prompt_engine.py
def score_lead(company_data: dict) -> float:
    """
    Score a lead based on signal strength.
    Returns 0.0-1.0 confidence score.
    """
    score = 0.0
    
    # Firmographic signals
    if company_data.get('employee_count', 0) > 50:
        score += 0.2
    if company_data.get('funding_stage') in ['Series A', 'Series B']:
        score += 0.3
    
    # Intent signals
    if company_data.get('recent_job_posts', 0) > 5:
        score += 0.2
    if company_data.get('tech_stack_match', False):
        score += 0.3
    
    return min(score, 1.0)

Layer 3: Output Generation

What We’re Building

LLM-powered content generation that turns enriched data into actionable artifacts.

Options (All Free)

Executive Brief Generator

# logic/generator.py
def generate_executive_brief(company: dict, signals: list) -> str:
    """Generate a McKinsey-style executive brief from signals."""
    prompt = f"""
    Generate a one-page executive brief for {company['name']}.
    
    Key Signals:
    {chr(10).join(f'- {s}' for s in signals)}
    
    Format:
    1. Executive Summary (2 sentences)
    2. Key Opportunities (3 bullets)
    3. Recommended Action (1 sentence)
    4. Risk Assessment (1 sentence)
    """
    return call_llm(prompt)

The n8n Workflow

Workflow Steps

1. Trigger: New row in Google Sheet OR webhook
2. Enrich: Call Hunter + Clearbit APIs
3. Score: Run Python function node
4. Branch: If score > 0.8, proceed
5. Generate: Call local LLM for brief
6. Output: Update CRM + send Slack alert

Running the Demo

Quick Start (5 minutes)

# Clone the starter repo
git clone https://github.com/BasinLeon/basin-signal-engine
cd basin-signal-engine

# Install dependencies
pip install -r requirements.txt

# Run the pipeline with sample data
python scripts/run_pipeline.py --input master_resume.md

# Output: outputs/brief.json + outputs/audio.mp3

Cost Comparison

What’s Next

This stack got me 160% pipeline growth at a Series A startup. The secret wasn’t the tools—it was the architecture: unified signal ingestion, deterministic scoring, and LLM-powered output.

To go deeper:

• GitHub Repo — Full source code
• Live Demo — Try it yourself
• Newsletter — Weekly insights

Questions? DM me on LinkedIn or email lbasin23@gmail.com with subject “Signal received.”

#GTMEngineering #RevenueArchitecture #BuildInPublic