Avenir builds machine learning systems for healthcare decision-making, starting with employer-sponsored care. We integrate claims, benefits, vendor, and population data to find cost drivers, emerging risk, and where to intervene.
Avenir connects a broker's TPA, PBM, stop-loss, biometric, and benchmark data, then generates the analyses that usually take a week: renewal prep, stewardship reports, claims analysis, and competitive benchmarking. Work that runs about 40 hours by hand takes closer to 4.
The team comes out of MIT, Apple, and Microsoft, with early Fortune 500 customers and measured ROI, backed by a16z Speedrun. I'm co-founder and CTO, responsible for the analytics engine, data integrations, and the AI workflows that model noisy, multi-source claims data into decision-ready insight.
Benefits consultants already know what needs to get done. The slow part is pulling the data, cleaning it, reconciling it across three portals and formats, analyzing it, and turning it into something they can present. Healthcare is most employers' biggest controllable expense, yet only Fortune 500 companies can usually afford the analytics to manage it. Avenir handles that whole workflow and brings the same caliber of analysis to groups from 200 to 20,000+ lives.
Built at MIT and validated by brokers managing employer groups from 200 to 20,000 lives. Enterprise-grade by design: SOC 2 Type II, HIPAA-compliant, 256-bit AES encryption, SSO & RBAC.