AI Formulary Optimization Software for Pharmacy Benefit Managers                                

[![JSRRB Technologies](/logo.png)](/)

# Dynamic formulary modeling that rebalances tiers against live rebate and outcomes data  
— Pharmacy Benefit Management

**Solving:** Static formulary tiers that ignore real-time drug cost and outcomes data

## Machine Learning Architecture

Dynamic formulary modeling that rebalances tiers against live rebate and outcomes data

Python, Scikit-learn, PostgreSQL

## Validated Business Impact

Lowers net plan drug spend by 19%

## Technical FAQ

### How does JSRRB Technologies solve static formulary tiers that ignore real-time drug cost and outcomes data?

We deploy dynamic formulary modeling that rebalances tiers against live rebate and outcomes data. Typical result: lowers net plan drug spend by 19%.

### What technology and security model powers this Pharmacy Benefit Management solution?

The solution is engineered on Python, Scikit-learn, PostgreSQL, deployed under JSRRB's zero-trust architecture so your proprietary Pharmacy Benefit Management systems and data stay encrypted and are never exposed to public AI training models.

Explore Related:[ai pbm member adherence](/solutions/ai-pbm-member-adherence)•[ai pbm prior authorization automation](/solutions/ai-pbm-prior-authorization-automation)

[Meet Our Founder](/founders-vision)