Solutions
We design and manage every realistic funding model — fully insured, level funded, self funded — and the pharmacy, navigation, compliance, and implementation work that makes any of them succeed.
Funding models
Smaller groups, stable budgets, low risk appetite.
How it works
Carrier owns the risk. You pay a fixed monthly premium. Renewals adjust based on the carrier's view of your block — usually with little claims transparency.
When it's right
Right when stability matters most, when your team can't absorb year-over-year claim swings, or when administrative simplicity outweighs savings potential.
Mid-market employers ready for claims visibility.
How it works
You pay a fixed monthly amount that covers projected claims, fees, and stop-loss. Run favorable and you may get a year-end refund. Run unfavorable and stop-loss caps your exposure.
When it's right
Right when you want self-funded mechanics — claims data, plan flexibility — without volatile cash flow. A great bridge between fully insured and full self funding.
Established groups with stable claims and scale.
How it works
You pay claims as they happen, plus administrative fees and stop-loss. Maximum flexibility on plan design, network, and pharmacy. Maximum upside when things go well.
When it's right
Right when scale, stable utilization, and a real risk appetite unlock plan design choices a fully insured program can't touch.
Supporting capabilities
PBM analysis, formulary review, specialty carveouts, and copay programs. The pharmacy line is usually where the math actually moves.
Symplifiers help your employees find in-network providers, schedule care, sort prescriptions, and handle the parts of healthcare nobody else explains.
5500s, ACA reporting, COBRA, Section 125, audits. Quiet work in the background that keeps you out of trouble.
Honest renewal analysis with the math shown. Open enrollment, onboarding, and employee comms that people actually read.
That's literally our job. Bring us your current renewal and your claims, and we'll model all three honestly against your real data.
