AI-Powered Dynamic Pricing for a B2B Transportation Platform Problem. Passenger transport operators on a LatAm B2B SaaS platform were setting fares by intuition. No visibility into yield, load factor, or route-level profitability — and no way to act on a pricing signal fast enough for it to matter.

Action. Defined and shipped an AI-assisted revenue management system on top of ~20 years of transactional data. Built the pricing signal layer (yield, load factor, average fare, route profitability), then closed the loop so recommendations became executable price changes rather than reports nobody acted on. Prioritized execution latency over model sophistication — the constraint was operator workflow, not prediction accuracy.

Result. System in production with 10 enterprise operators, running pricing decisions on data instead of instinct.