AI/ML
Forecasting and predictive models that convert your historical data into forward-looking signals for demand, risk, churn, and operational decisions.
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Projects delivered
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Years in production
What it is
Predictive analytics applies machine learning models to historical data to forecast future outcomes — demand, churn, risk, price, or equipment failure — with quantified uncertainty, enabling decisions to be made on probability rather than intuition.
What you get
The value of predictive analytics is in the decision it changes, not the accuracy metric it achieves. A churn model is useful when it is accurate enough to make proactive intervention cost-effective. A demand forecast is useful when it reduces inventory costs more than the modelling effort costs to build. We scope every predictive project around the business decision it should improve.
We build regression, classification, time series, and survival models depending on what is being forecast. Gradient boosted trees for tabular data, ARIMA and N-HiTS for time series, neural networks where the relationship is non-linear and data volume justifies it. We evaluate models on business metrics — not just RMSE or AUC — and include prediction intervals or confidence scores in every output.
Deployment is part of the scope. A model that only runs in a Jupyter notebook does not change decisions. We build prediction APIs or scheduled pipelines that feed outputs directly into your BI dashboards, operational systems, or alerting infrastructure — so forecasts are visible when and where decisions are made.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Predictive maintenance for equipment failure
Inventory optimisation with probabilistic demand signals
Price elasticity and revenue optimisation models
Automated feature engineering and selection
Model monitoring with drift detection and alerting
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
For monthly sales forecasting, 2–3 years of history covering at least two full seasonal cycles is a good minimum. For event-driven predictions like churn, the number of labelled examples matters more than time span — typically 1,000+ positive examples to start.
Data cleaning and imputation are a standard part of every engagement. We document every transformation applied and the rationale, so the pipeline is reproducible and auditable. Severe data quality issues are surfaced during discovery before development begins.
We instrument deployed models with data drift and prediction drift monitors. When input distributions shift significantly from the training distribution, or when prediction quality degrades against ground truth, the system flags for review. We set monitoring thresholds during deployment and include retraining triggers.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
