Machine learning that turns your data into reliable decisions
Beyond language models, we build classical and deep learning systems for forecasting, detection, ranking and optimisation, and run them with the same discipline as any other production software.
What we deliver
Predictive modelling & forecasting
Demand, churn, risk, pricing and maintenance models built on tabular and time-series data.
Computer vision
Classification, detection, segmentation and OCR for quality inspection, document processing and monitoring.
Natural language processing
Classification, entity extraction, topic analysis and semantic search tuned to your domain.
Recommendation & ranking
Personalisation and search relevance systems that improve with usage.
Anomaly detection
Fraud, outlier and operational anomaly detection on streaming and batch data.
MLOps
Feature pipelines, experiment tracking, model registries, CI/CD for models, drift monitoring and retraining.
What you can expect
- Baselines and success metrics agreed before modelling starts
- Reproducible experiments and versioned data and models
- Monitoring so accuracy does not silently decay
Tools we use
- Python
- PyTorch
- scikit-learn
- XGBoost
- TensorFlow
- ONNX
- MLflow
- Airflow
- Spark
- SageMaker
How we work together
- 1
Discover
We learn your goals, users and constraints, and define what success looks like.
- 2
Design
Architecture, UX and a delivery plan, with risks surfaced early.
- 3
Build
Iterative delivery in short cycles with demos, automated tests and CI from the start.
- 4
Launch
Staged rollouts, monitoring and runbooks so go-live is uneventful.
- 5
Evolve
Measure, learn and improve, with ongoing engineering support as you grow.
Questions about core machine learning
Do we need a lot of data to start?
Not always. We begin with a data and feasibility review, and often start with simple baselines or pre-trained models before investing in larger-scale training.
How do you take a model into production?
We package models behind versioned APIs or batch jobs, add monitoring for data and prediction drift, and automate retraining and rollout through MLOps pipelines.
Can you work with our existing data team?
Yes. We frequently pair with in-house data scientists and engineers, and document everything so your team can own the system.
When should we use classical ML instead of an LLM?
For structured prediction, forecasting, ranking and detection, classical ML is usually cheaper, faster and easier to evaluate. We recommend the simplest approach that meets the goal.
Related services
- Web & Platform EngineeringFast, search-friendly websites, web apps and API platforms built to scale.
- Cloud & DevOps EngineeringSecure, cost-aware infrastructure and delivery pipelines on AWS, Azure and Google Cloud.
- AI & Generative AI EngineeringLLM applications, RAG, agents and copilots that are evaluated, observable and production-ready.
Ready to set your course?
Tell us what you are building. We will reply within one business day with honest next steps.