Approach
Work begins with the operation, not with a predetermined product. The sequence below is how an engagement typically proceeds. It is not a commercial package.
01
Understand
Map the system: the decisions that matter, who currently makes them, the constraints that bind, the incentives in play, and the data that actually exists — as opposed to the data that was promised.
02
Formulate
Translate the operational problem into models, rules and computational structure. This is the step that generic delivery often skips. If the formulation is wrong, the software will faithfully solve the wrong thing.
03
Solve
Develop the algorithms, simulations or analytical methods required. Quality is judged by feasible, explainable decisions under the real constraints — not by novelty of technique.
04
Engineer
Build the production software around the method: services, interfaces, data contracts, failure modes and the operational surfaces people need in order to trust and override the system.
05
Integrate
Connect to the systems of record and the workflows already in use. A decision engine that cannot write back, or cannot be called at the right moment, is unused theory.
06
Improve
Compare intended decisions with live outcomes. Refine constraints, models and software as the operation reveals what the first formulation missed.
How a conversation starts.
Bring the decision that is currently painful — the plan that never quite fits, the allocation that consumes a team every week, the process nobody can simulate before changing. We will say quickly whether it is a computational problem we can take on.
If the problem is operationally hard, start there.
A useful conversation begins with the decision that is currently made by heuristic, spreadsheet or committee — and with the constraints that make it difficult.