Mission Brief
Interpretable Outcome Forecasting
Decision support with explanations that survive scrutiny.
Problem
Forecasts without explanations fail in high-scrutiny environments. This mission builds interpretable forecasts with evidence and sensitivity analysis.
Constraints
- Explainability required
- Audit trail for data, model, and decisions
- Access control and logging
- Integration into existing reporting flows
What ships
- Forecasting service with explanation outputs
- Dashboard with sensitivity and scenario comparison
- Data lineage and versioning
- Approval workflow for published forecasts
- AI-First interfaces for model and data contracts
AI-First interface map
Interfaces are explicit. Dependencies are documented. Swaps are practiced.
Success metrics
- Forecast accuracy on agreed horizon
- Explanation usefulness
- Time-to-produce report
- Audit trail completeness
- Adoption in decision cycles
Reuse kit
Starter structures you can adapt inside your environment.
DoD mapping
- AI-first operating model
- Pace-setting demos