Machine Learning Development
Outcomes
Decisions informed by patterns humans cannot see
A model examining thousands of variables across years of history finds relationships that intuition and dashboards miss.
Consistent scoring at scale
Every customer, transaction or asset assessed by the same criteria, continuously, without fatigue or drift in judgement.
Quantified uncertainty
A good model tells you how confident it is. That is often more operationally useful than the prediction itself, because it tells you when to involve a human.
A realistic verdict on feasibility
Some things are not predictable from the data you hold. We find that out in weeks through baselining, not after a full build.
What we build
Forecasting systems for demand, revenue, capacity and inventory, including seasonality and external drivers.
Scoring and classification models for credit risk, churn, lead quality, fraud and triage — with calibrated probabilities rather than bare labels, so thresholds can be set by business tradeoff rather than guesswork.
Recommendation systems using collaborative and content-based approaches, including the cold-start handling that determines whether they work for new users.
Anomaly detection for fraud, equipment failure and quality control, tuned against your actual tolerance for false positives — which is a business decision, not a technical one.
Optimisation models for routing, scheduling, pricing and allocation, where the answer is often classical operations research rather than machine learning, and we will say so.
Feature pipelines that compute the same features consistently in training and production. Training-serving skew is the most common cause of models that perform well in testing and badly in reality.
How it works
Weeks 1–2 — Problem framing and data assessment. Translating the business question into a prediction target, and establishing whether the data supports it. We define the baseline to beat — often a simple rule or the current human process — because a model that cannot beat the baseline is not worth deploying.
Weeks 2–4 — Feature engineering and baselines. Building features and running simple models first. A well-featured gradient-boosted tree beats a neural network on most tabular business problems, and starting simple establishes whether complexity is warranted.
Weeks 4–6 — Modelling. Iterating on approaches, tuning, validating with time-based splits that reflect how the model will actually be used. Random splits on time-series data produce impressive and meaningless results.
Weeks 6–8 — Deployment. Serving infrastructure, feature pipeline in production, monitoring, and integration into the system where the decision is actually made. A model whose predictions nobody consumes is a research project.
Ongoing. Performance monitoring, drift detection, retraining. Model performance decays as the world changes, silently, unless someone is watching.
Technology
Modelling: scikit-learn, XGBoost and LightGBM for tabular problems — which is most business ML. PyTorch where deep learning is genuinely warranted. Prophet and statistical methods for forecasting, benchmarked against ML approaches rather than assumed inferior.
Data: pandas and Polars, dbt and Airflow for pipelines, feature stores where multiple models share features.
Deployment: containerised inference on your cloud, batch or real-time depending on how the prediction is consumed.
Monitoring: prediction drift, feature drift, and business-metric tracking — because a model can look statistically healthy while its business value quietly disappears.
Where this applies
Strongest where you hold years of historical data with recorded outcomes, and where the same decision is made repeatedly at volume.
Weakest where history is short, outcomes are unrecorded, or the process changed recently enough that past data no longer describes the present.
How we scope and price
Fixed scope, quoted after a data assessment. Cost is driven by data readiness, how many models are in scope, and whether real-time serving is required. We recommend a short feasibility phase first on any problem where predictability is uncertain — it is cheaper to discover a ceiling early than to build against one.
Frequently asked questions
It depends on the problem, but as a rough guide: enough examples of the outcome you are predicting to cover its natural variation, including seasonal cycles for forecasting. Rare events need proportionally more history. We assess this before committing to a build.
Unknowable before seeing your data, which is why the first phase establishes a baseline and a realistic ceiling. Anyone quoting accuracy before examining your data is guessing. The more useful question is whether it beats what you do today by enough to justify the change.
Yes, substantially. Generative models produce content; these models produce predictions from your own historical patterns. Language models cannot forecast your demand, because your demand is not in their training data. Both are useful and they solve different problems.
Then we tell you, and you save the deployment cost. This is a legitimate outcome and the reason we baseline first.
Drift monitoring on inputs and predictions, plus tracking the business metric the model is meant to move. Both are built in, because the alternative is finding out from a complaint.
For most business ML, yes — feature importance and per-prediction explanations via SHAP or similar. Where regulation requires explainability, we choose model types that support it rather than bolting on approximations.
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