Predictive Growth: Using Julius AI to Model Revenue Scenarios and Stress-Test Assumptions

Every business operates on assumptions: conversion rates, churn rates, acquisition costs. Most founders have never formally tested whether those assumptions are accurate. Julius AI has made scenario modelling accessible to any founder in 30 minutes.

Building the Base Model

Start by feeding Julius AI your current key metrics: MRR, new customers, churn rate, ARPU, CAC, and fixed/variable costs. Ask Julius to build a 12-month revenue projection. The result is a dynamic financial model that Julius can manipulate in response to natural language questions.

Stress-Testing

The most valuable use of the model is stress-testing: deliberately inputting pessimistic assumptions. "What happens to my runway if CAC increases by 30%?" "Model the impact of reducing churn from 8% to 5%." Knowing the answer to these questions isn't pessimism — it's strategic clarity.

Julius can also run Monte Carlo simulations—running hundreds of randomised scenarios to understand the probability distribution of outcomes, giving you a range of plausible outcomes (10th, 50th, and 90th percentile).