Data Science & ML
Forecasting
Demand, revenue and capacity forecasts with honest intervals, built to ship and to stay correct as the world moves.
100% delivery success — delivered worldwide across seven regions
What you get
Where Forecasting earns its place
Demand, revenue and capacity.
- Forecasts with p10–p90 bands
- Backtested against history
- Monitored for drift
Tools & platforms
What we build it with
The stack MOD actually delivers this on.
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Related work
From manual checks to automated oil-field monitoring
A custom API pings IoT sensors across oil fields into MongoDB, transformed in Python and surfaced in a Dash dashboard at individual-sensor granularity for predictive maintenance.
Workforce analytics for Nestlé’s supply & demand planning
HR data marts on SAP feeding automated daily ingestions through AWS Glue and Databricks, on a medallion architecture, so planners could see available resource against production targets.
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionWe publish forecasts as intervals (p10–p90), backtest on real historical windows, and design the intervention with the team who will use it.
Demand, revenue, capacity and cash — for example, predictive maintenance from IoT-sensor data across oil fields.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.