Use-case discovery and feasibility
Assessing available data, expected value and success metrics before any model is built.
Data science and machine learning in production
We build predictive models for forecasting, scoring and optimisation and take them from a notebook to production with MLOps, monitored, retrained and integrated with your systems.
What we do
Scope of work
Forecasting
Demand, sales, load and inventory forecasts with clear error metrics and prediction intervals.
Classification and scoring
Churn, fraud, lead scoring and quality prediction models, with explanations that business users can trust.
Computer vision and NLP
Visual inspection, document classification and text analytics where tabular data is not enough.
MLOps
Feature pipelines, experiment tracking, a model registry, automated retraining and drift monitoring.
Technologies
Typical technologies
We are not tied to any single vendor: we work on public and private clouds and adapt to your environment, licences and team skills. These are the tools we use most often in this area.
Languages and libraries
- Python
- R
- scikit-learn
- XGBoost
- LightGBM
- PyTorch
- TensorFlow
MLOps
- MLflow
- Kubeflow
- Weights & Biases
- DVC
- Databricks
Processing and orchestration
- Apache Spark
- Polars
- dbt
- Apache Airflow
Model serving
- FastAPI
- ONNX
- BentoML
- Docker
- Kubernetes
Related services
Often combined with this service
- Databricks
- Snowflake
- BigQuery
- Microsoft Fabric
Data Engineering
Batch and streaming pipelines, data warehouses and lakehouses, database design and data quality you can rely on.
- SQL
- Python
- R
- pandas
Data Analytics
Analysis of business and operational data that answers concrete questions and supports decisions.
- Power BI
- Looker
- Tableau
- Apache Superset
Visualisation & BI
Dashboards and self-service BI in Power BI, Looker and Tableau built on a consistent data model.
Let's talk about your project
Tell us where you are today and what you want to achieve. We will suggest a concrete first step, and we can sign an NDA before any technical discussion.