AI Models Development
We fine-tune foundation models on your data to achieve performance that outperforms generic solutions in your specific domain. Your vocabulary, your rules, your precision standards.
Predictive models calibrated on your business history: churn, demand, optimal pricing, credit risk or anomaly detection. No generic templates.
Image recognition, object detection and visual quality control systems for manufacturing, retail or logistics. What the human eye takes hours to do, the model does in seconds.
Classifiers, entity extractors, sentiment analysis and semantic search engines adapted to your sector vocabulary and specific use cases.
Infrastructure to train, evaluate, version and redeploy models systematically. The model is not a one-off project — it is a living asset that requires maintenance.
We measure performance against real business metrics, not just technical ones. A model with 95% accuracy can be useless if the error concentrates in the cases that matter most.
01
We translate the business challenge into a machine learning problem with clear success metrics. This stage prevents months of work in the wrong direction.
02
We audit your available data, identify gaps and define the data pipeline needed to train with quality guarantees.
03
We train and compare multiple approaches with full transparency. We share the results of every experiment, including those that did not work.
04
We productionise the model with robust infrastructure and automatic alerts when performance degrades. Go-live is the beginning, not the end.
Depends on the problem. Some classifiers work well with hundreds of examples; others need millions. We evaluate this in the exploration phase and tell you honestly whether your data is sufficient before committing budget.
It is part of the normal cycle. We design the retraining process from the start to be systematic, not requiring a new project each time. MLOps is part of the deliverable.
Yes, completely. All the code, model weights and documentation belong to your company. We retain no rights over what we build together.
Yes, and in many cases it is the most efficient option. We evaluate whether fine-tuning a foundation model gives better results than training from scratch before recommending an approach.