Improve cost estimates, set defensible risk buffers, and test macroeconomic downside using historical project evidence and AI and ML-based probability ranges.
Built for capital-intensive projects where estimate error is costly
Why Large-Project Estimates Go Wrong
Single-point estimates miss real uncertainty. Optimism bias, scope complexity, and macro volatility lead to under-budgeting and misallocated contingency.
A Practical Reference Class + AI Workflow
We apply the following steps for our AI-based Reference Class Forecasting Engine (RCF-AI):
- Step 1: Build relevant reference classes from comparable projects.
- Step 2: Run the AI-driven reference class forecasting engine (RCF-AI) to produce context-dependent cost-overrun distributions and confidence ranges.
- Step 3: Deliver decision outputs: estimate range, contingency recommendation, and macro-risk scenarios.
Where Teams Apply It First
Defensible Outputs for Decision Committees
Every forecast includes cohort logic, assumptions, uncertainty bands, and model drivers so results are explainable and auditable.
Typical Impact
- Estimate variance: in-sample 76%; out-of-sample: 37% explained
- Contingency error: 50% lower
- Decision confidence: 88% higher
Pressure-Test Your Next Major Project Before Capital Is Committed
Bring one active project. Get a reference-class benchmark, AI/ML forecast range, and recommended contingency buffer.
FREQUENTLY ASKED QUESTIONS
It combines external reference-class evidence with AI/ML, instead of relying only on internal bottom-up assumptions.
Yes. Your project history, taxonomy, and governance requirements are incorporated.
We run context dependent regime scenarios and map them to project-level cost impact distributions.
Yes. Assumptions, cohorts, and uncertainty ranges are documented in each output.