Monte Carlo Says We Have 18 Months of Runway — With 94% Confidence

# Monte Carlo Runway Simulation — On demand
name: monte-carlo-runway
trigger: "simulate runway"
channel: telegram

task: |
  Run Monte Carlo simulation for runway projections.

  INPUT PARAMETERS:
  Current cash: €2.8M
  Monthly burn (base): €145K
  Burn growth rate: 3-5% per month (uniform distribution)
  Monthly revenue (current): €85K
  Revenue growth: 8-15% per month (normal distribution, μ=11%, σ=3%)

  RISK EVENTS (Bernoulli trials per month):
  - Major client churn: 5% probability, -€25K/month revenue impact
  - Hiring acceleration: 10% probability, +€30K/month burn increase
  - Unexpected cost (legal, infra): 3% probability, €50-150K one-time
  - Follow-on funding: 8% probability after month 12, €1-3M injection

  SIMULATIONS: 10,000
  HORIZON: 36 months

  OUTPUT:
  - Probability distribution of runway (months)
  - Confidence levels: 50%, 75%, 90%, 95%
  - Sensitivity analysis: which variable impacts runway most
  - Scenario comparison: base vs optimistic vs pessimistic
  - Board-ready summary

import numpy as np

def monte_carlo_runway(params, n_simulations=10000, horizon_months=36):
    results = []

    for sim in range(n_simulations):
        cash = params["current_cash"]
        monthly_revenue = params["current_revenue"]
        monthly_burn = params["current_burn"]
        runway_months = 0

        for month in range(1, horizon_months + 1):
            # Revenue growth (normal distribution)
            rev_growth = np.random.normal(
                params["rev_growth_mean"],
                params["rev_growth_std"]
            )
            monthly_revenue *= (1 + rev_growth)

            # Burn growth (uniform distribution)
            burn_growth = np.random.uniform(
                params["burn_growth_min"],
                params["burn_growth_max"]
            )
            monthly_burn *= (1 + burn_growth)

            # Risk events
            if np.random.random() < params["churn_probability"]:
                monthly_revenue -= params["churn_impact"]

            if np.random.random() < params["hiring_accel_probability"]:
                monthly_burn += params["hiring_accel_impact"]

            if np.random.random() < params["unexpected_cost_probability"]:
                one_time = np.random.uniform(
                    params["unexpected_cost_min"],
                    params["unexpected_cost_max"]
                )
                cash -= one_time

            if month > 12 and np.random.random() < params["funding_probability"]:
                funding = np.random.uniform(
                    params["funding_min"],
                    params["funding_max"]
                )
                cash += funding

            # Net cash flow
            net = monthly_revenue - monthly_burn
            cash += net

            if cash <= 0:
                runway_months = month
                break
        else:
            runway_months = horizon_months  # Survived full horizon

        results.append(runway_months)

    return np.array(results)

📊 MONTE CARLO RUNWAY ANALYSIS — PyratzLabs
Simulations: 10,000 | Horizon: 36 months

RUNWAY PROBABILITY DISTRIBUTION:
  ≥12 months:  98.2% ✅
  ≥18 months:  94.1% ✅
  ≥24 months:  73.4%
  ≥30 months:  52.8%
  ≥36 months:  41.2%

PERCENTILE ANALYSIS:
  P5  (worst 5%):    14 months
  P25 (conservative): 21 months
  P50 (median):       28 months
  P75 (optimistic):   34 months
  P95 (best case):    36+ months (cash-flow positive)

BREAK-EVEN ANALYSIS:
  Month revenue exceeds burn (median): Month 16
  Probability of reaching profitability within 24mo: 67.3%

SENSITIVITY ANALYSIS (impact on median runway):
  Revenue growth ±5%:     ±6.2 months  ← HIGHEST IMPACT
  Burn growth ±2%:        ±3.8 months
  Client churn rate ±3%:  ±2.4 months
  Funding probability:    ±4.1 months (if removed entirely: -4.1mo)

SCENARIO COMPARISON:
                    P(≥18mo)   P(≥24mo)   Median
  Base case         94.1%      73.4%      28 months
  Revenue +20%      98.8%      91.2%      34 months
  Major client churn 87.2%     58.1%      22 months
  Hiring freeze      97.4%     84.8%      32 months
  No follow-on       91.3%     64.2%      24 months

🎯 BOARD SUMMARY:
  With 94% confidence, we have 18+ months of runway.
  Revenue growth rate is the single biggest lever — a 5%
  increase in monthly growth adds 6 months of runway.

  Key risk: client concentration. One major churn event
  drops 24-month probability from 73% to 58%.

  Recommendation: maintain current burn, accelerate revenue
  diversification, begin fundraising conversations at month 9
  (6 months before median break-even).