Turn Your Data Into a Competitive Advantage

Less than most teams expect, and the amount matters less than the quality. For a classical model — forecasting, churn, risk scoring — a few thousand well-labelled historical examples is often enough to beat the spreadsheet or the rule of thumb you are using today. What actually blocks projects is not volume but labelling and consistency: data collected differently across years, outcomes recorded in free text, or the thing you want to predict never recorded at all. We look at what you have before quoting, because that review usually changes the plan.

Machine learning and data science learn patterns from your own historical data to produce a prediction — a number, a score, a category. Generative AI starts from a model somebody else trained and produces text, images or decisions from instructions and context. They need different skills, different infrastructure and different testing. If your question is "what will happen" or "which category is this", you are on the right page. If it is "read this, write that, or decide what to do next", see our generative AI and LLM apps service. Plenty of products end up needing both.

Models degrade because the world moves and the data moves with it — a pricing model trained before a market shift keeps confidently applying the old shape. The fix is operational rather than clever: monitor the live input distribution against the training distribution, track prediction quality against outcomes as they arrive, alert when either drifts past a threshold, and retrain on a schedule that matches how fast your domain actually changes. This is the part teams skip, and it is the reason most models quietly stop being used about a year in.

Yes, and for a first project we usually recommend it. A focused proof of concept takes your real data, builds the simplest model that could work, and answers one question: is the signal in this data strong enough to be worth building around? That is a few weeks rather than a few months, and it produces a number you can make a decision with. If the answer is no, you have saved the cost of the full build — which is a genuinely good outcome, even though nobody enjoys hearing it.