Optimisation for complex operations that change in real time.
Qatalyst turns operational rules, constraints, and objectives into ready-to-solve optimisation models. Classical optimisation is the default. Hybrid and quantum back-ends are used selectively when the problem is highly coupled, time-critical, or hard to solve at scale.
Faster decisions
Automate model building and re-optimise as conditions change.
Operational clarity
Make constraints explicit, auditable, and easy to communicate across teams.
Decarbonisation ready
Optimise time, cost, energy, and emissions in one decision layer.
See it in 60 seconds
A short overview of how Qatalyst captures constraints, builds an optimisation model, and returns a practical decision plan.
Qatalyst overview — optimisation that just works.
What users get
Recommendations that are fast, explainable, and operationally grounded. The optimisation engine stays behind the scenes.
- Clear decision outputs: assignments, schedules, routes, or capacity plans
- Constraints made explicit and auditable
- Rapid re-optimisation when the system state changes
- Classical-first delivery, with hybrid or quantum options as an upgrade path
What we do
We turn complex operational problems into optimisation models that can be solved quickly and repeatedly, without manual maths or lengthy modelling cycles.
- Problem structuring and constraint capture
- Automated model generation for routing, scheduling and allocation
- Continuous re-optimisation and decision support
Where it fits
Start with classical optimisation. Add hybrid or quantum back-ends only when the problem scale and coupling justify it.
Featured use case: Electric freight charging coordination
Depots and shared hubs face limited power, clustered arrivals, and tight readiness deadlines. Qatalyst produces rolling charging plans that reduce queues, stay within power caps, and keep fleets on schedule.