Contingency Analysis AI
AI determination of appropriate project contingency based on risk analysis.
Definition
Contingency Analysis AI uses Monte Carlo simulation and risk modeling to determine appropriate contingency amounts for construction projects. It analyzes project-specific risks, historical cost data, and uncertainty factors to recommend contingencies that balance adequate protection with budget efficiency.
In Depth
Project contingency — the budget reserve for unforeseen conditions and risks — is one of the most debated line items in construction budgets. Too little contingency exposes the owner to budget overruns; too much ties up capital unnecessarily. AI brings data-driven rigor to contingency setting by analyzing risk factors and historical cost variance data.
The analysis starts with the project's specific risk profile — the design stage (earlier designs carry more risk), the project type (renovation carries more risk than new construction), the procurement method (design-build typically has lower owner contingency than design-bid-build), and the site conditions (urban sites carry more risk than greenfield). AI quantifies each risk factor based on historical variance data from comparable completed projects.
Examples
Calculating risk-based contingency
Running Monte Carlo simulations
Allocating contingency by trade
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Contingency Analysis AI uses Monte Carlo simulation and risk modeling to determine appropriate contingency amounts for construction projects. It analyzes project-specific risks, historical cost data, and uncertainty factors to recommend contingencies that balance adequate protection with budget efficiency.
Calculating risk-based contingency. Running Monte Carlo simulations. Allocating contingency by trade.
Project Research: Instantly access all project-critical information from a single search interface.