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Custom models for forecasting, classification, scoring, recommendation and anomaly detection — trained on your own history, evaluated against held-back data, and deployed where the prediction is acted on.
Machine learning earns its place when you have years of records of a decision being made, the outcome being known afterwards, and the decision happening often enough that a few percentage points of improvement is real money. Stock reordering, churn, credit exposure, demand by branch by day, which invoices go unpaid.
It earns nothing when the pattern changes faster than the data arrives, or when only a handful of examples exist. We check which situation you are in before quoting a model, because the wrong answer there is the most expensive mistake available in this discipline.
The current method is scored first. If the model cannot beat it on held-back data, we say so and you stop paying.
Extraction, cleaning and feature work are quoted openly rather than hidden inside a modelling estimate.
The metric reflects what a false positive and a false negative each cost you, not whichever number looks best.
Input distributions and prediction quality are tracked after launch, with alerts when they move.
Predictions arrive in the ordering screen, the approval queue or the dashboard — not in a notebook.
Training code, feature definitions and evaluation results are handed over. You can retrain without us.
Framed as the business question rather than the algorithm — the algorithm is an implementation detail, and the right one is usually the simplest that clears the bar.
Model development is iterative, so it is quoted in phases with a decision point between them. You are not asked to fund deployment before anyone knows whether the model beats the rule of thumb it would replace.
Included: the data pipeline, the model, the evaluation, the serving API, the integration and the documentation. Not included: guaranteed accuracy, third-party compute and hosting costs, and ongoing retraining unless it is contracted as part of a support agreement. Models age. A retraining cadence is a running cost, and pretending otherwise is how clients end up with a model quietly making worse decisions than the spreadsheet it replaced.
A business with years of records in which a decision was made and its outcome was stored, and where that decision repeats often enough for a small improvement to be real money: reordering stock, forecasting demand by branch by day, spotting customers likely to leave, or predicting which invoices will go unpaid.
With only a handful of examples, or a pattern that changes faster than the data arrives, machine learning is the wrong tool, and that is checked before a model is quoted.
Straight answers, including the ones that rule us out.
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