Predictive analytics
Forecasting demand, churn, risk and revenue with models validated on your real history.
Services
Data Science & ML
Adaptive ML models that earn their compute.
scikit-learnPyTorchTensorFlowXGBoost
Adaptive ML models that earn their compute.
We build machine learning that ships — models that live in production and drive real decisions, not notebooks that never leave a data scientist’s laptop. Every engagement is grounded in your actual data and a clearly defined business outcome.
Our work spans classical ML and deep learning: forecasting and demand prediction, classification and recommendation, NLP for text understanding, and computer vision for images and video. We use scikit-learn, XGBoost, PyTorch and TensorFlow, choosing the simplest model that solves the problem well.
Just as importantly, we handle the parts teams often skip — feature pipelines, evaluation, monitoring for drift, and clean deployment — so a model keeps earning its keep long after it goes live.
What we deliver
The core capabilities inside our data science & ml engagements.
Natural language processing
Text classification, extraction, sentiment and search over your documents and messages.
Computer vision
Detection, classification and OCR pipelines for images and video streams.
Recommendation systems
Personalisation engines that lift engagement and conversion.
Model deployment (MLOps)
Feature pipelines, versioning, monitoring and drift detection so models stay reliable.
Deep learning
Custom PyTorch and TensorFlow models where the problem genuinely needs them.
Who it's for
Is this the right fit?
We do our best work when the problem is a genuine fit. Data Science & ML typically makes sense for:
Businesses that want to forecast demand, churn or risk from their own data
Teams drowning in unstructured text or documents that need automated understanding
Companies with image or video data ready to automate inspection or tagging
Organisations with ML prototypes that never made it to production
FAQ
Common questions
Do we have enough data for machine learning?
Often, yes — and if not, we will tell you honestly. We start with a short assessment of your data before committing to a model, so you never invest in ML that the data cannot support.
Do you deploy models or just build them?
We deploy. A model only creates value in production, so we handle feature pipelines, serving, and monitoring for drift — not just the training notebook.
Which ML frameworks do you use?
scikit-learn and XGBoost for classical ML, PyTorch and TensorFlow for deep learning. We choose the simplest approach that meets your accuracy and latency targets.
Let's build
Ready to start?
Tell us what you're trying to build. We'll tell you honestly whether we're the right team — and how we'd approach it.
Talk to us