Machine Learning Development Services
Custom machine learning models for prediction, classification, recommendation and forecasting, taken from data all the way to production, with the MLOps to keep them reliable.
Machine learning, all the way to production
A model in a notebook is not a product. The value of machine learning shows up only when a model is integrated into a decision or workflow and kept accurate as the world changes.
We build custom models for your problem, deploy them into your systems, and put the monitoring and retraining in place so they keep earning their place after launch.
- Custom models tied to a real decision or workflow.
- Deployed into your systems, not left in a notebook.
- Monitoring and retraining so accuracy holds over time.
Our ML development process
- Framing: we agree the decision the model should improve, the success metric and the current baseline.
- Data assessment: we check which data exists, its quality and whether it can legally be used.
- Baseline and prototype: a simple model first, so every later model has something to beat.
- Model development: feature engineering, model selection and validation on held-out data.
- Deployment: integration into the systems where decisions are made, with monitoring from day one.
- Operation: drift detection, retraining and regular reviews of business impact.
Why choose us as your machine learning development company
Many models never reach production. They work in a notebook, but nobody planned the data pipelines, the integration or who maintains them. Our teams build and run ML systems, so we plan for production from the first week: where the data comes from, how predictions reach the people or systems that use them, and how the model is monitored.
As a machine learning development firm based in the EU, we design for GDPR and the EU AI Act, can keep data in EU regions and document models so your team can understand and maintain them. You own the code, the models and the documentation.
Machine learning development services we deliver
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Prediction and forecasting
Models for demand, risk, churn and other outcomes, tied to the decisions they are meant to improve.
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Classification and recommendation
Classification, ranking and recommendation systems built on your data and evaluated on your metrics.
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Anomaly detection
Models that flag unusual transactions, sensor readings or behaviour for review.
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Optimisation
Pricing, scheduling and allocation models that recommend the best option under your constraints.
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Computer vision
Detection and inspection models for images and video, deployed on the edge or in the cloud.
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Deployment and MLOps
Deployment, monitoring and retraining so models stay accurate as data and behaviour shift.
Frequently asked questions
What types of ML models do you build?
Forecasting, classification, anomaly detection, recommendation and computer vision models, plus LLM-based systems.
Do you maintain models after launch?
Yes. We set up monitoring, retraining pipelines and alerts, or hand them over to your team with documentation.
How much data do we need?
It depends on the problem. We check this in a short feasibility phase before development starts.
Turn your data into decisions
Tell us the outcome you want to predict or improve. We will scope a model and the path to run it in production.