Machine Learning That Reaches
Production, Not Just Notebooks
Plenty of models perform well in a notebook and never make it into the business. We build the whole path — data pipeline, model, serving infrastructure, monitoring and retraining — so a prediction actually changes a decision. For generative AI specifically, we have a dedicated family of services.
Our AI & ML Development Services
Classical machine learning, deep learning and the engineering that keeps them running.
Predictive Modelling
Forecasting, churn and demand prediction, risk and propensity scoring — models trained on your history and validated against holdout periods rather than flattering in-sample results.
Computer Vision
Detection, classification, OCR and quality inspection from images or video, including the annotation strategy and edge deployment considerations that decide whether it works on your actual hardware.
Natural Language Processing
Classification, entity extraction, sentiment and summarisation over your documents, tickets and correspondence, tuned to your domain vocabulary.
Data Engineering
The unglamorous majority of most ML projects: pipelines, feature stores, labelling workflow and quality checks. Models fail on data problems far more often than on algorithm choice.
MLOps & Deployment
Versioned models, reproducible training, automated evaluation, staged rollout and rollback. The discipline that lets you change a model without holding your breath.
Monitoring & Retraining
Drift detection, performance tracking against live outcomes, and retraining pipelines — because a model's accuracy decays quietly as the world it was trained on moves on.
How We Approach ML Projects
Four habits that keep projects honest.
Baseline First
We establish what a simple rule or existing process already achieves. A model that cannot beat the baseline is not worth deploying, and knowing the gap keeps expectations grounded.
Measured on Business Metrics
Accuracy is not the goal — a decision improved is. We tie evaluation to the cost of a false positive versus a false negative in your specific context.
Explainable Where It Matters
Where a decision affects a customer or faces a regulator, we favour interpretable approaches and provide feature attribution rather than an unexplainable score.
Built to Be Retrained
Pipelines are automated from the start so refreshing a model is a routine operation rather than a rediscovery of how it was built.
Where Machine Learning Earns Its Place
Applications with a clear decision and enough history to learn from.
Demand Forecasting
Anticipate volume by product, region and period, so stock and staffing decisions stop relying on last year plus a percentage.
Churn & Retention
Identify accounts at risk early enough for intervention, and know which intervention has historically worked.
Fraud & Anomaly Detection
Surface unusual transactions and behaviour in real time, with thresholds tuned to your tolerance for false alarms.
Visual Inspection
Automate quality checks on a production line or in field imagery, at consistency a human inspector cannot sustain across a shift.
Recommendation
Surface the next relevant product, article or action based on behaviour rather than static rules.
Document Classification
Route and tag incoming documents and correspondence automatically at a volume manual triage cannot match.
Why Our ML Work Ships
We Start With the Data
Most stalled ML projects were data projects in disguise. We assess quality, coverage and labelling before promising anything about model performance.
Deployment Is In Scope
Serving, monitoring and retraining are part of the engagement, not a follow-on nobody budgeted for. A model that is not deployed has produced no value.
Your Team Can Operate It
Documented pipelines, reproducible training and handover sessions, so the system does not depend on us being available.
Our AI & ML Stack
Modelling
- Python
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- Hugging Face
Data
- Spark
- Airflow
- dbt
- Postgres
- Snowflake
- BigQuery
MLOps
- MLflow
- Weights & Biases
- Kubeflow
- Docker
- Model registries
- Feature stores
Deployment
- AWS SageMaker
- Azure ML
- Vertex AI
- Kubernetes
- ONNX
- Edge runtimes
AI & ML Development — Common Questions
It depends far more on the problem than on a headline row count. A well-defined classification task with clean labels can work on surprisingly little; a nuanced forecasting problem with strong seasonality needs several cycles of history. We assess this during discovery and will tell you plainly if the honest answer is that you need to improve data collection first.
Machine learning here means models that predict, classify or detect — forecasting demand, scoring risk, spotting a defect. Generative AI produces new content such as text and summaries. They solve different problems and we offer both; if your need is drafting, answering questions or conversational interfaces, our generative AI pages cover that in more depth.
Frequently we do. Engagements range from us delivering end to end, to us building the deployment and MLOps layer around models your data scientists have already developed. We are comfortable in a supporting role where that is the sensible split.
We test performance across relevant subgroups rather than only in aggregate, since a model can look accurate overall while performing badly for a particular segment. Where decisions affect individuals we document the approach, provide attribution for predictions, and design human review into consequential paths.
We structure engagements so this becomes clear early and cheaply. The first phase establishes a baseline and tests feasibility against your real data — and if performance will not clear a useful bar, stopping there is a good outcome compared with discovering it after a full build.
Monitoring against live outcomes, drift detection on inputs, and automated retraining pipelines with staged rollout. We agree alert thresholds and a retraining cadence during the project so degradation surfaces as an alert rather than as a complaint from the business.
Other Services We Offer
Web Development
Fast, accessible, search-friendly websites and web applications built to convert.
Mobile App Development
iOS and Android apps that feel native, work offline and survive store review.
Cloud & DevOps
Infrastructure that scales predictably, deploys safely and costs what it should.
UI / UX Design
Research-led product design and design systems that improve real conversion.
Turn your data into decisions
Tell us the decision you want to improve. We will assess whether your data can support it before anyone commits to a build.