Data exists, value doesn't
The company has stored customer, sales or operations data for years, but decisions are still made on experience and intuition.
Machine Learning
Models that run in production
We build ML models against a specific business metric and take them to production, not to a polished report.
Rules no longer keep up
Manual rules for scoring, pricing or fraud have grown into hundreds of exceptions and fail more and more often.
Models never reached production
There were ML experiments, but the models stayed in notebooks and were never built into the product or the process.
Problem framing: business metric, baseline and success criteria
Data audit: volume, quality, gaps and availability
Data preparation and feature design for the model
Model build and comparison, with a clear quality score
Deployment as a service with an API for your systems
Monitoring of model quality and data drift after launch
Documentation and handover of the model to your team
PROCESS
[ 01 ]free · 30 min
[ 02 ]14 business days
[ 03 ]depends on scope
[ 04 ]ongoing, monthly cycle
From request to result
[ 01 ]free · 30 minClarity Session
We review the problem and the data you already have. You leave knowing whether ML can have an effect here. No sales pitch.
[ 02 ]14 business daysData audit and PoC
We analyze the data and build a first model. You get real quality on your data and an estimate of the effect.
[ 03 ]depends on scopeBuild and deployment
We take the model to production, connect it to your systems and compare it with the current approach.
[ 04 ]ongoing, monthly cycleMonitoring and growth
We watch quality, retrain on new data and improve the model against the business metric.
Frequently asked questions
and answers
How is Machine Learning different from Data Science?+
Data Science studies data: find patterns, test hypotheses, answer a business question. Machine Learning builds models that make decisions or forecasts inside a product or a process. Data Science often comes first and shows where a model will have an effect. ML then makes that model work.
How much data does ML need?+
It depends on the problem. Demand forecasting needs history across several seasons. Classification needs enough labeled examples of each class. We will not build a model on data that is clearly too thin, and we say so after the audit, not after development.
How is ML better than the rules we already have?+
Not always. If the rules are simple and they work, ML adds complexity with no benefit. ML wins when factors are many, relationships are not obvious, and rules can no longer keep up with change. We always compare the model with the current approach on the same data.
What happens to the model after a year?+
Without support its quality declines, because customers, the market and the data change. That is why we monitor quality and data drift, and build retraining into the process. A model is a product that needs maintenance, not a one-off project.
THE FINAL STEP TOWARD A SYSTEMReady to turn growth
Ready to turn growth
into predictability?
Start with a free 30-minute Clarity Session.
