The $0 GTM Stack: Building Enterprise-Grade Revenue Intelligence Without Enterprise Budgets

"You don't need a $50k/year tech stack to build serious revenue intelligence."

This guide demonstrates how founders can replicate enterprise capabilities through intelligent tool composition rather than vendor lock-in.

Three-Layer Architecture

Code Components

URL Ingestion Logic

def fetch_text_from_url(url: str) -> str:
    """Ingest article content from URLs for signal extraction."""
    response = requests.get(url, timeout=10)
    soup = BeautifulSoup(response.text, 'html.parser')
    article = soup.find('article')
    if article:
        return article.get_text(separator='\n', strip=True)
    return soup.body.get_text(separator='\n', strip=True)

Lead Scoring Engine

def score_lead(company_data: dict) -> float:
    """Score leads based on signal strength (0.0-1.0)."""
    score = 0.0
    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
    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)

Brief Generator

def generate_executive_brief(company: dict, signals: list) -> str:
    """Generate McKinsey-style executive briefs from signals."""
    prompt = f"""Generate executive brief for {company['name']}.
    Format: 1. Summary (2 sent) 2. Opportunities (3 bullets)
    3. Action (1 sent) 4. Risk (1 sent)"""
    return call_llm(prompt)

Cost Comparison

n8n Workflow Sequence

1. Trigger: Google Sheet row or webhook
2. Enrich: Call Hunter/Clearbit APIs
3. Score: Execute Python scoring function
4. Branch: Conditional logic (score > 0.8)
5. Generate: Local LLM brief creation
6. Output: CRM update + Slack notification

Quick Start

git clone https://github.com/BasinLeon/basin-signal-engine
cd basin-signal-engine
pip install -r requirements.txt
python scripts/run_pipeline.py --input master_resume.md
# Outputs: brief.json + audio.mp3

Results

Using this architecture, I achieved 160% pipeline growth at a Series A company. The emphasis: unified signal ingestion, deterministic scoring, and LLM-powered output generation.