Machine learning consulting services pay for themselves on tabular problems: rows, columns, a target, and a decision waiting on a number.
Those are answered with regression, classification, clustering and gradient boosting. Well understood, cheap to run on hardware you already have, and explainable to somebody who has to sign the result off.
What These Machine Learning Consulting Services Cover
Classification and regression. Will this account lapse, what will this cost, which of six categories does this belong to. Gradient boosted trees remain hard to beat on tabular data.
Feature engineering. Where accuracy genuinely comes from: time since last event, rolling aggregates, ratios between two fields nobody thought to compare. More projects are won here than by the choice of algorithm.
Evaluation against a holdout. Reported in terms of the decision rather than a textbook metric. Precision at the top of the list when somebody will ring two hundred accounts, error at the planning horizon when it is a forecast.
Deployment and monitoring. A model behind an endpoint, with input validation, logged predictions and an alert when the inputs drift, is a system rather than a finding.
Explainable, Which the AI Act Rewards
Classical models on tabular data can be explained feature by feature, which matters when a model informs decisions about people.
Our ai ml solutions ship with the feature list, the evaluation record and the training data lineage written down. That is the evidence base the EU AI Act asks for, and assembling it during the build costs an afternoon.
Everything runs inside EU regions by default, training data included.
Choosing the Right Tool for the Question
Ask a language model to predict churn from a table of numbers and it will answer confidently, at roughly a thousand times the cost per prediction of the model that fits.
Pulling structured fields out of unstructured contracts is the reverse case, and that work sits on the AI and LLM applications page. Our ai and ml consulting starts by settling which of the two you have.
The Baseline First, Always
Before modelling anything we build the obvious version: last year plus growth, everyone inactive for ninety days, or a three month average.
Where that is close enough, finding out in week one at our cost beats month four at yours. When a model does beat the baseline, the margin is the business case.
What You Get at Handover
The code, the features, the evaluation and the monitoring configuration, in your repository, with retraining documented as a runbook.
Our ai ml services are bought in pieces, so one scored model can prove the approach before anything larger is agreed. Machine learning consulting services bought that way stay proportionate to what they return. Tell us what decision the prediction would change.
Related Services
Modelling rests on earlier work. Unreliable inputs are a data quality problem, and modelling them produces confident nonsense.
Missing history is data engineering. Where the framing is a business question about forecasting or segmentation rather than an engineering one, that is predictive analytics.