Letter from the Founders

Dear Reader,

We are building a technology rooted in mathematics and physics, not a dashboard.

We do not believe in a single general-purpose product that can be dropped into every domain. Reality is too specific. Constraints differ. Sensors differ. Objectives differ. What transfers is not the application, but the underlying mathematical engine: how to represent a complex system, forecast its evolution, quantify uncertainty, and produce decisions that can be tested against outcomes.

Airports are where we start because the problem is concrete and mission-critical. HVAC and energy operations are constrained, coupled, and measurable. Flight schedules, weather, occupancy dynamics, and tariff exposure create patterns you can model and costs you can reduce. This is a proving ground for building a rigorous decision system that integrates with existing infrastructure and produces auditable actions.

From there, expansion is not "more dashboards." Expansion is domain-by-domain adaptation of the same underlying modeling approach to new problem structures.

In airports, that means coordinated control across major energy systems: HVAC, other controllable loads, and distributed resources where they exist. The goal is operational control that respects comfort, reliability, and cost, with clear accountability for what changed and why.

Outside airports, the adaptation looks different. In natural resources, the objective may not be energy optimization at all. It may be discovery, estimation, forecasting, or risk characterization under sparse and noisy measurements. The value is that the same mathematical foundation can be re-specified to a different physical and economic reality, producing domain-specific systems rather than a one-size-fits-all solution.

Daniel and I are obsessive by nature about the mathematical and physical truth underneath systems. It is the source of innovation. When you understand the structure of a system, you can predict what it will do, choose what matters, and design decisions that hold up under stress.

I also believe the act of human learning is the best innovation known to man. It is the mechanism that pushes a being to explore and discover. Our work is an attempt to encode that discipline: to build systems that learn from reality, stay honest about uncertainty, and improve through measurement rather than narrative.

This is our vision.