Challenges We Solve
Common pain points that structured decision models eliminate.
Benefits Cost Explosion
Plan costs rise 6-8% annually with little visibility into drivers. Forecast costs by plan, segment, and utilization pattern to find savings before renewal season.
Claims Driver Blind Spots
High-cost claims surface after the fact. Build watchlists that identify emerging cost drivers, high-utilization cohorts, and stop-loss attachment risk early.
Dependent Eligibility Fraud
Ineligible dependents cost employers $400-$600 per dependent annually. Audit eligibility systematically and flag discrepancies before they compound.
Mental Health Access Gaps
Employees can't access the mental health benefits they're paying for. Measure network adequacy, utilization barriers, and program effectiveness.
Purpose-Built for Benefits Teams
Plan design trade-offs, premium BYOL data, and cost forecasting in one governed platform.
Plan Design Trade-Offs
Model the cost, utilization, and employee impact of plan design changes across every benefit line before committing to renewals.
BYOL Premium Data
Upload Mercer, Radford, and SHRM benchmark datasets to compare your benefits packages against market data.
Cost Forecasting
Monte Carlo simulation across claims trends, utilization patterns, and demographic shifts to forecast total benefits cost with confidence intervals.
Featured Models
Pre-built decision models ready to run with your data.
Chronic Condition Stratification
Stratifies member population by chronic condition burden, clusters high-risk segments, and models disease management program ROI.
Claims Driver Watchlist
Identifies top cost drivers and estimates savings from targeted programs.
Dependent Eligibility Audit
Estimates savings from dependent verification with Bayesian confidence bands.
Fertility Benefits Forecast
Projects fertility benefit utilization and costs across IVF, egg freezing, and fertility preservation scenarios with adoption rate modeling.
Mental Health Access
Assesses mental health network adequacy, identifies access gaps by geography/specialty, and models impact of parity compliance improvements.
Plan Cost Forecast
Projects next-year spend by plan and tests trend, design, and contribution scenarios.
Stop Loss Optimization
Tests specific/aggregate stop-loss settings to optimize risk vs cost.
Wellness Program Roi
Quantifies expected ROI from wellness, mental health, and chronic condition programs.
How It Works
Three steps to structured, auditable decisions.
Ingest Claims & Enrollment Data
Upload claims history, enrollment files, and plan design parameters. Map data fields once, refresh on schedule or via API.
Model & Stratify
Run cost forecasting, chronic condition stratification, and plan design trade-off models. Compare scenarios side by side with Monte Carlo uncertainty.
Optimize & Monitor
Track claims trends, audit dependent eligibility, and measure wellness program ROI. Generate compliance evidence for regulatory review.