Machine Learning and Predictive Analytics
Forecasting, scoring and detection models trained on your historical data and validated against business outcomes. Twelve engagements, each measured against the simplest thing that could already work.
Most of these projects fail before the modelling starts.
The two things that sink machine learning projects are framing and leakage. A model predicting something nobody can act on is an expensive form of correctness, and a feature that quietly contains the answer produces excellent test scores and worthless production performance. Both are visible in week two if anyone looks, and both are usually found in month four instead.
So every engagement here starts with the decision the model is meant to improve, audits when each field actually becomes available in the real process, and measures a simple baseline before anything else is built. A meaningful share of proposed models do not beat that baseline, and finding that out early is worth more than the model would have been.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
Custom ML Model Development
A bespoke model for a problem no off-the-shelf product fits, benchmarked against a simple baseline and deployed properly.
Read more →Predictive Analytics
Predictions embedded in the decisions they are meant to improve, measured on the business outcome rather than on AUC.
Read more →Demand and Sales Forecasting
SKU and location level forecasts with promotions, seasonality and hierarchy handled, measured where error costs money.
Read more →Recommendation Engine Development
Recommenders tuned to a business objective, with cold start, diversity and rules handled, proven by A/B test.
Read more →Fraud and Anomaly Detection
Detection tuned to what your review team can actually work, with explanations analysts can act on.
Read more →Churn Prediction and Retention Modelling
Churn models aimed at persuadable customers, with uplift modelling and impact measured against a holdout.
Read more →Dynamic Pricing Optimisation
Prices moved on measured elasticity, inside guardrails, proven by controlled experiment rather than by simulation.
Read more →Customer Segmentation and Propensity Scoring
Segments that map to actions and propensity scores per offer, activated in your marketing platform.
Read more →Credit Risk and Underwriting Models
Underwriting models built to survive validation: documented, fairness tested, with reason codes and monitoring.
Read more →Time Series Forecasting
Forecasts for any metric over time, backtested properly and delivered with usable uncertainty.
Read more →Reinforcement Learning
Sequential decision systems with safe exploration and offline evaluation, and honest advice when a simpler method wins.
Read more →Optimisation and Operations Research
Scheduling, routing and allocation solved against your real constraints, with solutions planners will actually run.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
A specific prediction problem
Custom ML model development where the target is unique to your process, or the named engagement where it is a standard shape: demand forecasting, churn, fraud, credit risk.
A programme rather than a model
Predictive analytics, which prioritises the decisions worth predicting for, takes the first one into production, and measures whether the decision actually changed.
Commercial and customer decisions
Recommendation engines, segmentation and propensity, and dynamic pricing, each proven by controlled experiment rather than by simulation.
Deciding, not just predicting
Optimisation and operations research for scheduling, routing and allocation, and reinforcement learning in the narrow set of cases where decisions are genuinely sequential.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
How much data do we need for machine learning?
It depends on the event rate rather than the row count. A few thousand examples of the outcome you care about is often workable; a million rows containing forty positive cases usually is not. The data audit in the first fortnight answers this for your case, and we say plainly when there is not enough signal to learn from.
Is machine learning still relevant now that large language models exist?
For numerical prediction, ranking, forecasting and optimisation, yes, decisively. Language models are the right tool for text, documents and conversation, and they are a poor and expensive way to forecast demand or score credit risk. The two pillars solve different problems and we will tell you which one your task belongs in.
How do you know whether a model is actually worth having?
By measuring it against a simple baseline on a validation design that matches how the future arrives, and by costing the errors in both directions. If the margin over the baseline does not justify owning a model, we say so and you keep the analysis.
What is the most common reason these projects fail?
Data leakage, followed by framing. Leakage is when a feature contains information unavailable at the moment of prediction, which produces excellent offline results and useless production performance. Framing failures produce accurate predictions nobody can act on. Both are cheap to catch early and expensive to discover late.
Who owns the models and the pipelines?
You do, in full, on final payment: training and scoring pipelines, feature definitions, validation results, monitoring and documentation. Models decay as the world changes, so the durable deliverable is the ability to detect that and retrain, which is handed over with the code.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.