Where it pays off
Where we add value
We don't start from the model — we start from the problem. The first question is always what changes once the model is running in production. If the answer isn't convincing, we won't take the project on.
- Predictive machine maintenance
- Image-based quality control
- Object detection and classification
- Ecological monitoring (forests, water, soil)
- Optical recognition in agriculture
- Time-series forecasting
- Automation of repetitive processes
- Anomaly detection in data
From prototype to production
MLOps is part of the work, not an add-on
A model in a Jupyter notebook is the start, not the goal. We build the complete lifecycle — from the data pipeline through model versioning to drift monitoring and automated retraining.
- Discovery — proof of value in 2–4 weeks
- Data — quality, annotation, versioning
- Training — experiments and model comparison
- Deployment — cloud, edge or on-prem
- Monitoring — drift, accuracy, latency
- Maintenance — retraining and extensions
Technology stack
What we use
Python ecosystem
PyTorch, TensorFlow, scikit-learn, Hugging Face, OpenCV.
MLOps
MLflow, DVC, Airflow, Kubeflow, BentoML, Weights & Biases.
Infrastructure
AWS, Azure, GCP, Kubernetes, NVIDIA edge devices.
Computer vision
YOLO, Detectron2, Segment Anything, custom CNN architectures.
Time series
Prophet, LSTM, temporal fusion transformers.
Ethics & governance
Model cards, bias audits, explainability (SHAP, LIME).
Have the data but not the certainty?
Let's run a discovery workshop. We'll show which model is worth building and what it would realistically return.
Ask the team