ADAPT

How do you keep making good decisions as conditions change in real time?

Why it's Tough

Static plans break when reality shifts. But reacting to every fluctuation creates chaos. You need to learn what matters, update your strategy continuously, and balance exploring new approaches against exploiting what works.

Our Approach

We use reinforcement learning, model predictive control (MPC), and online optimization to create systems that sense, learn, and adjust. Prices update in response to demand, routes shift around traffic, and policies improve with every interaction.

TECHNICAL COMPETENCIES & KEYWORDS

real-time optimizationdynamic pricingadaptive routingcontrolreinforcement learningbanditsonline learningMPC

USE CASES FOR ADAPT

6 EXAMPLES

Adaptive Trial Design
and Supply Planning

Continuously optimize clinical trials and supply plans as enrollment and efficacy data accumulates, reducing waste and accelerating drug development.

Dynamic Pricing and
Tariff Optimization

Design time-varying electricity prices that balance revenue maximization, demand shaping, renewable integration, and grid stability while encouraging efficient customer consumption patterns.

Dynamic
Pricing

Real-time price optimization for logistics services based on capacity, demand, and market conditions to maximize profitability under volatile costs.

Peak Shaving and
Demand Response

Peak shaving optimization reduces electricity costs by up to 30% through optimal battery storage scheduling that minimizes demand charges and shifts consumption from peak to off-peak periods.

Energy Storage Management
and Real-Time Optimization

Optimize battery charging and discharging in real-time to maximize revenue while preserving battery lifespan.

Real-Time Rerouting
and Reprioritization

Enable delivery fleets to dynamically adjust routes in response to traffic, road closures, weather, and new demands, using real-time data streams, AI, and mathematical optimization to reduce costs and improve efficiency.