We solve complex production, scheduling, maintenance, and supply chain problems in Energy and Pharma operations — from first-principles modelling to a live system in the real process.
Optimise throughput, reduce downtime, and bring model-driven clarity to how your plant actually runs.
From batch scheduling to continuous process — build production plans grounded in real physics and constraints.
End-to-end visibility from raw materials to delivery — demand planning, inventory, logistics that adapt in real time.
The Approach
We own the full chain — from the first conversation to a live, running solution. The engagement ends when the solution runs. Not when the deck is delivered.
We don't build until we understand. We walk the process, interview the people who run it, and build a first-principles model of what is actually happening before writing a single line of code. Most operational problems are misdiagnosed before they reach an external provider — we fix that first.
We model the physical reality of the operation — not just the data. We gather process logs, sensor readings, maintenance records, and expert knowledge, then choose the modelling approach that fits the problem. The right method for the right problem, every time.
We redesign the process so the solution fits your operational reality. Then we design the right architecture and select the right tools. Technology follows the business requirement — not the other way around.
No off-the-shelf tools, no library defaults. Every model is purpose-built for the specific process, the specific data, and the specific constraints of the operation. This is the difference between a model that works in a demo and one that works on the shop floor.
We stay through go-live. That means specifying equipment procurement requirements, monitoring tool implementation, training the people who run the operation, and supporting change management. The engagement ends when the solution runs — not when the deck is delivered.
Contact
Tell us about the problem. We'll confirm whether it's something we can solve — and how we'd approach it.
We confirm the problem, build the model, and make it run.