Forecasting & predictive models
Estimate demand, workloads, stock needs or likely outcomes using suitable historical data. Backtest against practical baselines and communicate uncertainty.
Build / See what may happen. Choose what to do.
Predictive AI & optimisation.
Use forecasting, optimisation and simulation to improve operational decisions. We build systems for demand, capacity, workforce allocation, routing and planning around the constraints your business actually faces.
Discuss this with usThe opportunity
Make better decisions with the data you have.
A spreadsheet can show what happened. Planning the next shift, delivery route or stock decision also requires uncertainty, competing goals and practical limits to be considered together.
Prediction estimates a likely outcome. Optimisation chooses an action within constraints. Simulation explores how a system might behave under different conditions. We select the combination that fits the decision and compare it with your current planning approach.
What we can do.
Scheduling, routing & allocation
Balance available people, skills, vehicles, capacity, time windows and service requirements. Make trade-offs explicit and allow planners to inspect and adjust recommendations.
Simulation & digital twins
Model selected operational behaviour and compare scenarios before making a change. Validate assumptions and distinguish modelled outcomes from real-world results.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- A defined planning decision and baseline
- Data preparation and a validated modelling approach
- Forecasts, recommendations or scenario tools
- An interface integrated with the planning workflow
- Monitoring and review criteria for changing conditions
Illustrative workflow
Planning capacity for a busy period
Forecast demand → apply availability and service constraints → compare plans → a planner adjusts and approves → track actual outcomes.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Performance against a practical baseline
- ↗Constraint satisfaction and planning effort
- ↗Operational outcomes after recommendations are used
Your questions, answered.
How much historical data do we need?
It depends on the decision, seasonality and how stable the process is. We assess coverage and quality before selecting a method.
Will a digital twin predict everything accurately?
No. A simulation represents selected behaviour and assumptions. Its value depends on validation and on using it for decisions within those limits.
Can planners override the recommendation?
Yes. A planning system can expose constraints, explanations and adjustments, with a record of changes and their effect.
Useful before you start
Questions worth exploring.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
From AI pilot to production: what changes?
A working demonstration is a starting point. A released service also needs integration, ownership and a dependable response to failure.
What does AI implementation really cost?
A useful budget covers the whole workflow: data, integrations, evaluation, people and the cost of keeping it running.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.