FEATURES · MODELS · SERVING
Prediction that changes a decision.
A model is only useful if someone acts differently because of it. We start from the decision and work backwards to the features, rather than starting from the dataset and hoping for relevance.
- Baselined
- Measured against the simple approach
- Reproducible
- Versioned data, features and models
- Monitored
- Drift detection after deployment
THE SHORT VERSION
Start from the decision
The first question is not which algorithm. It is: what decision does this change, who makes it, how often, and what does being wrong cost in each direction. That determines the metric, and the metric determines everything else.
We always build the boring baseline first — a heuristic or a simple model. Often it is close enough, and when it is not, it is the number the real model has to beat.
The difference is where the effort goes.
Model-first
- Accuracy optimised on a metric nobody in the business uses
- Features computed differently in training and in production
- No versioning, so results cannot be reproduced
- Deployed once, then left to drift quietly
Decision-first
- A metric derived from the cost of each kind of error
- One feature definition, shared by training and serving
- Versioned datasets, features and model artefacts
- Monitoring for drift, with a defined retraining trigger
WHAT IT INCLUDES
Scope, in plain terms.
Every engagement is shaped to the problem, but this is the ground it typically covers.
Recommendation systems
Ranking and personalisation with cold-start and diversity handled.
Forecasting
Demand, load and capacity prediction with honest uncertainty ranges.
Classification & scoring
Risk, triage and prioritisation models with calibrated probabilities.
Feature pipelines
Consistent feature computation across training and low-latency serving.
Model serving
Versioned deployment, shadow traffic and safe rollback.
Anomaly detection
Finding the unusual in operational data without drowning in false alarms.
What you get
- Problem framing tied to a business decision and its error costs
- Baseline model and the evaluation it must beat
- Feature pipeline shared between training and serving
- Versioned model artefacts and a reproducible training run
- Serving endpoint with monitoring, drift detection and rollback
QUESTIONS
The things people ask first.
How much data do we need?
Less than people expect for a useful baseline, more than they expect for a reliable one. We assess what you have before proposing an approach.
Can you work with our existing data warehouse?
Yes. Building on the warehouse you already trust is usually faster and safer than standing up a parallel pipeline.
What happens when performance degrades?
Drift monitoring is part of the deployment, with a defined trigger for retraining and a rollback path to the previous model version.
Often paired with
- 07Data & AnalyticsTransform complex data into insights, decisions and measurable business intelligence.
- 03AI DevelopmentProduction-ready artificial intelligence systems built to solve practical business problems.
- 09Computer Vision & NLPIntelligent systems for image, video and natural-language understanding.
Need machine learning?
Tell us the problem and the constraint. We will tell you what it takes — and whether this is even the right service for it.