Post by A.Team

In high-stakes use cases like healthcare contract pricing, you can’t just “use AI”. Multi-year contracts define reimbursement across hundreds of service lines and facilities, with regulatory pressure from price transparency and real relationship and legal risk if the math gets ugly. For one of the largest healthcare providers in the U.S. the referenced spreadsheet alone was said to take ~3,000 person-hours to create. To help their teams, Harrison Bralower, a technical PM in the A.Team network, built a custom 0→1 AI-assisted pricing and quoting workflow. It included: → Constraint-based optimization — business rules and negotiation constraints translated into a linear programming setup (equalities/inequalities) and solved via an optimization engine. → On-demand rate sheets — users input rules and constraints to generate a new rate sheet, potentially multiple times during a negotiation call. Humans stay in the loop to assess reasonableness. → Data foundations act as the real unlock — metadata controls (plans included, populations, lookback windows, mandatory adjustments). Wondering how to build AI systems for high-stakes, regulated environments? Visit: https://hubs.la/Q04hWBSM0