Skip to main content
Simulation enables running time-series simulations powered by LLMs to forecast metrics and analyze scenarios over time. Each simulation scenario models real-world dynamics using AI reasoning to predict future states.

​
Available Scenarios

Merchant Revenue Forecast

Forecast e-commerce merchant revenue with customer behavior and market dynamics

​
How Simulations Work

Simulations iterate through time steps, using LLMs to predict metric values based on previous state and contextual factors. Each simulation:
  • Builds prompts with previous state and business context
  • Calls the LLM to predict next-step metrics with structured output
  • Tracks metrics throughout the simulation timeline
  • Generates comprehensive reports in multiple formats

​
Using Simulations

Simulations are created with a scenario object and configuration: 
from upsonic.simulation import Simulation
from upsonic.simulation.scenarios import MerchantRevenueForecastSimulation

simulation = Simulation(
    MerchantRevenueForecastSimulation(
        merchant_name="TechCo",
        sector="E-commerce",
        location="San Francisco",
        current_monthly_revenue_usd=50000
    ),
    model="anthropic/claude-sonnet-4-5",
    time_step="daily",
    simulation_duration=100,
    metrics_to_track=["monthly recurring revenue"]
)

result = simulation.run()

# Export reports with chainable methods
result.report("summary").to_json("summary.json")
# result.report("summary").to_csv("summary.csv")
# result.report("summary").to_html("summary.html")
# result.report("summary").to_pdf("summary.pdf")
# result.report("summary").show()

# Other report types:
# result.report("detailed").to_json("detailed.json")
# result.report("visual").to_html("charts.html")
# result.report("statistical").to_json("stats.json")

# Save all reports at once:
# result.reports().save_all(directory="./reports", format="json")