#!/usr/bin/env python3 """ Revenue & Financial Tracking System Real-time revenue, expenses, and profitability tracking Author: Alexa Amundson Copyright: BlackRoad OS, Inc. """ import json from datetime import datetime, timedelta import random def generate_revenue_projections(): """Generate conservative, realistic, and optimistic revenue projections""" current_month = datetime.now().month current_year = datetime.now().year projections = { "current_state": { "historical_revenue": { "total_all_time": 26800000, "breakdown": { "securian_sales_commissions": 26800000, "blackroad_saas": 0, "consulting": 0, "licensing": 0, "sponsorships": 0 } }, "current_monthly_burn": 0, "runway_months": "infinite", "cash_position": 32350, # Crypto holdings "assets": { "crypto": 32350, "equipment": 5000, "domains": 2000, "total": 39350 } }, "revenue_streams": { "1_open_source_sponsorships": { "description": "GitHub Sponsors + direct support", "pricing": { "friend": {"price": 5, "monthly": True}, "supporter": {"price": 25, "monthly": True}, "sponsor": {"price": 100, "monthly": True} }, "projections": { "conservative": { "monthly": 100, "annual": 1200, "customers": {"friend": 10, "supporter": 3, "sponsor": 0} }, "realistic": { "monthly": 500, "annual": 6000, "customers": {"friend": 30, "supporter": 10, "sponsor": 2} }, "optimistic": { "monthly": 2500, "annual": 30000, "customers": {"friend": 100, "supporter": 40, "sponsor": 10} } } }, "2_commercial_licensing": { "description": "Commercial use licenses for businesses", "pricing": { "startup": {"price": 499, "annual": True}, "business": {"price": 999, "annual": True}, "enterprise": {"price": 2499, "annual": True} }, "projections": { "conservative": { "annual": 50000, "customers": {"startup": 50, "business": 25, "enterprise": 5} }, "realistic": { "annual": 150000, "customers": {"startup": 100, "business": 75, "enterprise": 20} }, "optimistic": { "annual": 500000, "customers": {"startup": 300, "business": 200, "enterprise": 50} } } }, "3_consulting_integration": { "description": "Custom integration and consulting services", "pricing": { "hourly": {"price": 250, "unit": "hour"}, "daily": {"price": 1500, "unit": "day"}, "project": {"price": 5000, "unit": "project"} }, "projections": { "conservative": { "annual": 50000, "breakdown": { "hourly": {"hours": 100, "revenue": 25000}, "daily": {"days": 10, "revenue": 15000}, "project": {"projects": 2, "revenue": 10000} } }, "realistic": { "annual": 150000, "breakdown": { "hourly": {"hours": 200, "revenue": 50000}, "daily": {"days": 40, "revenue": 60000}, "project": {"projects": 8, "revenue": 40000} } }, "optimistic": { "annual": 500000, "breakdown": { "hourly": {"hours": 400, "revenue": 100000}, "daily": {"days": 100, "revenue": 150000}, "project": {"projects": 50, "revenue": 250000} } } } }, "4_priority_support": { "description": "24/7 priority support with SLA", "pricing": { "monthly": {"price": 499, "monthly": True} }, "projections": { "conservative": { "monthly": 2500, "annual": 30000, "customers": 5 }, "realistic": { "monthly": 10000, "annual": 120000, "customers": 20 }, "optimistic": { "monthly": 25000, "annual": 300000, "customers": 50 } } }, "5_saas_platform": { "description": "Multi-agent orchestration platform as SaaS", "pricing": { "starter": {"price": 49, "monthly": True}, "professional": {"price": 199, "monthly": True}, "business": {"price": 499, "monthly": True}, "enterprise": {"price": 1999, "monthly": True} }, "projections": { "conservative": { "monthly": 5000, "annual": 60000, "customers": {"starter": 50, "professional": 15, "business": 5, "enterprise": 1} }, "realistic": { "monthly": 25000, "annual": 300000, "customers": {"starter": 200, "professional": 80, "business": 30, "enterprise": 5} }, "optimistic": { "monthly": 100000, "annual": 1200000, "customers": {"starter": 1000, "professional": 300, "business": 100, "enterprise": 20} } } }, "6_job_income": { "description": "Full-time employment while building", "projections": { "conservative": { "annual": 120000, "source": "AI/ML Engineer role" }, "realistic": { "annual": 180000, "source": "Senior AI Engineer role" }, "optimistic": { "annual": 250000, "source": "Staff/Principal Engineer role" } } } }, "total_projections": { "year_1_conservative": { "total_annual": 161200, "monthly_average": 13433, "breakdown": { "job": 120000, "sponsorships": 1200, "licensing": 0, "consulting": 10000, "support": 0, "saas": 0 } }, "year_1_realistic": { "total_annual": 456000, "monthly_average": 38000, "breakdown": { "job": 180000, "sponsorships": 6000, "licensing": 50000, "consulting": 100000, "support": 60000, "saas": 60000 } }, "year_1_optimistic": { "total_annual": 1280000, "monthly_average": 106667, "breakdown": { "job": 250000, "sponsorships": 30000, "licensing": 200000, "consulting": 300000, "support": 100000, "saas": 400000 } }, "year_3_conservative": { "total_annual": 280000, "monthly_average": 23333, "breakdown": { "job": 150000, "sponsorships": 5000, "licensing": 50000, "consulting": 50000, "support": 25000, "saas": 0 } }, "year_3_realistic": { "total_annual": 950000, "monthly_average": 79167, "breakdown": { "job": 200000, "sponsorships": 30000, "licensing": 150000, "consulting": 200000, "support": 120000, "saas": 250000 } }, "year_3_optimistic": { "total_annual": 3500000, "monthly_average": 291667, "breakdown": { "job": 0, # Full-time on BlackRoad "sponsorships": 100000, "licensing": 500000, "consulting": 500000, "support": 400000, "saas": 2000000 } } }, "expenses": { "current_monthly": { "infrastructure": { "cloudflare": 20, "railway": 0, # Currently paused "domains": 50, "github": 0, # Free "total": 70 }, "tools_software": { "anthropic_api": 50, "other_apis": 20, "total": 70 }, "marketing": 0, "total_monthly": 140, "total_annual": 1680 }, "scaled_monthly": { "infrastructure": { "cloudflare": 200, "railway": 500, "domains": 100, "databases": 200, "cdn_bandwidth": 300, "total": 1300 }, "tools_software": { "ai_apis": 500, "monitoring": 200, "analytics": 100, "email": 50, "total": 850 }, "marketing": { "ads": 1000, "content": 500, "total": 1500 }, "team": { "contractors": 5000, "total": 5000 }, "total_monthly": 8650, "total_annual": 103800 } }, "profitability": { "year_1_conservative": { "revenue": 161200, "expenses": 1680, "profit": 159520, "margin_pct": 99.0 }, "year_1_realistic": { "revenue": 456000, "expenses": 20000, "profit": 436000, "margin_pct": 95.6 }, "year_1_optimistic": { "revenue": 1280000, "expenses": 103800, "profit": 1176200, "margin_pct": 91.9 }, "year_3_realistic": { "revenue": 950000, "expenses": 103800, "profit": 846200, "margin_pct": 89.1 }, "year_3_optimistic": { "revenue": 3500000, "expenses": 500000, "profit": 3000000, "margin_pct": 85.7 } }, "milestones": { "first_dollar": { "target_date": "2025-01-15", "source": "First GitHub sponsor or consulting client", "amount": 25 }, "first_1k_month": { "target_date": "2025-03-01", "source": "Mix of sponsors + consulting", "amount": 1000 }, "first_10k_month": { "target_date": "2025-06-01", "source": "Licensing + consulting + sponsors", "amount": 10000 }, "quit_job": { "target_date": "2025-12-01", "required_mrr": 20000, "safety_buffer": 100000 }, "first_100k_year": { "target_date": "2025-12-31", "source": "All revenue streams", "amount": 100000 }, "first_1m_year": { "target_date": "2027-12-31", "source": "SaaS scaling", "amount": 1000000 } } } return projections def generate_monthly_forecast(months=24): """Generate month-by-month forecast""" forecast = [] start_date = datetime.now() for i in range(months): month_date = start_date + timedelta(days=30*i) # Growth curves (exponential for optimistic, linear for conservative) month_num = i + 1 # Conservative: slow linear growth conservative_revenue = 1000 + (month_num * 500) # Realistic: steady growth with some acceleration realistic_revenue = 2000 + (month_num * 1500) + (month_num ** 1.5 * 100) # Optimistic: exponential growth optimistic_revenue = 5000 * (1.15 ** month_num) forecast.append({ "month": month_date.strftime("%Y-%m"), "month_num": month_num, "conservative": { "revenue": int(conservative_revenue), "expenses": 150 + (month_num * 10), "profit": int(conservative_revenue - (150 + month_num * 10)) }, "realistic": { "revenue": int(realistic_revenue), "expenses": 500 + (month_num * 100), "profit": int(realistic_revenue - (500 + month_num * 100)) }, "optimistic": { "revenue": int(optimistic_revenue), "expenses": 1000 + (month_num * 300), "profit": int(optimistic_revenue - (1000 + month_num * 300)) } }) return forecast def main(): print("šŸ’° Generating comprehensive financial projections...") projections = generate_revenue_projections() forecast = generate_monthly_forecast(24) output = { "data": { "projections": projections, "monthly_forecast": forecast, "summary": { "year_1_range": { "min": 161200, "likely": 456000, "max": 1280000 }, "year_3_range": { "min": 280000, "likely": 950000, "max": 3500000 }, "profitability": "High margins (85-99%) due to low overhead", "time_to_first_revenue": "2-4 weeks", "time_to_sustainability": "3-6 months", "time_to_full_time": "6-12 months" } }, "metadata": { "updated_at": datetime.utcnow().isoformat() + 'Z', "source": "financial-modeling", "copyright": "Ā© 2025 BlackRoad OS, Inc.", "confidential": True } } with open('revenue_projections.json', 'w') as f: json.dump(output, f, indent=2) print(f"āœ… Financial projections generated") print(f"\nšŸ“Š Year 1 Projections:") print(f" Conservative: ${projections['total_projections']['year_1_conservative']['total_annual']:,}") print(f" Realistic: ${projections['total_projections']['year_1_realistic']['total_annual']:,}") print(f" Optimistic: ${projections['total_projections']['year_1_optimistic']['total_annual']:,}") print(f"\nšŸ“Š Year 3 Projections:") print(f" Conservative: ${projections['total_projections']['year_3_conservative']['total_annual']:,}") print(f" Realistic: ${projections['total_projections']['year_3_realistic']['total_annual']:,}") print(f" Optimistic: ${projections['total_projections']['year_3_optimistic']['total_annual']:,}") print(f"\nšŸ’” Path to Full-Time:") print(f" Required MRR: ${projections['milestones']['quit_job']['required_mrr']:,}") print(f" Safety Buffer: ${projections['milestones']['quit_job']['safety_buffer']:,}") print(f" Target Date: {projections['milestones']['quit_job']['target_date']}") if __name__ == "__main__": main()