Predictive analytics
We train regression and classification models on your historical data to forecast demand, churn, maintenance windows, or pricing. Most projects reach production-grade accuracy within four weeks of data handover.
What we build
Each project starts from a business question, not a technology wish list. Here is where our work tends to cluster.
Natural language processing
Document classification, entity extraction, sentiment scoring, and summarisation pipelines. We fine-tune open-weight language models so your data never leaves your infrastructure.
Computer vision
Defect detection on production lines, medical image triage, satellite imagery analysis. We handle annotation, model training, and edge deployment on devices with as little as 2 GB RAM.
Data engineering
Before any model can learn, the data has to be clean, joined, and flowing. We design ETL pipelines, data lakes on AWS or Azure, and monitoring dashboards that alert you when upstream data drifts.
MLOps and integration
A model sitting in a notebook helps nobody. We containerise, version, and deploy models behind REST or gRPC endpoints with CI/CD pipelines, A/B testing, and automatic retraining triggers.
How a project moves from idea to production
We keep the steps short and the communication frequent. You will never wait three months wondering what is happening.
Week 1: problem definition workshop
A two-hour session where we pin down the exact metric you want to improve. We review your available data, agree on success criteria, and write a one-page scope document. No charge for this step.
Weeks 2–3: data audit and baseline
We connect to your data sources, profile quality, and build a simple baseline model. This tells us whether the signal exists before we invest in anything complex. You get a written report with charts.
Weeks 3–5: model development
Iterative experimentation with candidate architectures. We share a live dashboard so you can watch accuracy, precision, and recall evolve day by day. Fortnightly video calls keep everyone aligned.
Week 5–6: deployment and handover
The final model is packaged into a container, deployed to your cloud or on-premise servers, and connected to your application via API. We write operational runbooks and train your team on monitoring.
Ongoing: support and retraining
Data changes. Models decay. We offer monthly retainer plans that include drift monitoring, quarterly retraining, and priority bug fixes. Most clients stay on a plan for at least a year.
Results we can point to
Three recent projects, each measured against the metric the client cared about most.
Demand forecasting for a Scottish logistics firm
Their previous Excel-based forecast missed peaks by 18% on average. Our gradient-boosted model, retrained weekly on live sales and weather data, brought the error down to under 5% within two months.
72% error reduction
Visual defect detection for an electronics manufacturer
Manual inspection caught roughly 88% of solder defects. Our convolutional neural network, running on an edge GPU beside the production line, now catches 99.2% and flags uncertain cases for human review.
99.2% detection rate
Patient message triage for an NHS-adjacent clinic
Clinicians spent 40 minutes each morning sorting inbox messages by urgency. Our NLP classifier now pre-sorts them with 94% agreement against clinician labels, cutting triage time to under ten minutes.
75% time savedQuestions we hear often
How much does a typical project cost?
Most projects fall between £15,000 and £60,000 depending on data complexity and deployment requirements. We quote a fixed price after the free problem-definition workshop, so there are no surprises. Smaller proof-of-concept engagements can start at £8,000.
Do we need to share our raw data with you?
Not necessarily. We can work inside your own cloud environment under your security policies. For clients in regulated sectors like healthcare or finance, we routinely sign data processing agreements and operate under strict access controls. If synthetic data is sufficient for early experiments, we use that first.
What happens if the model does not perform well enough?
We agree on a minimum performance threshold before development starts. If we cannot hit it after a fair attempt, we write up what we learned and you owe nothing beyond the data-audit phase. That has happened twice in four years, and in both cases the root cause was insufficient training data rather than a modelling failure.
Can you work with our existing engineering team?
Yes, and we prefer it. Embedding with your developers means the knowledge transfer happens naturally during the project rather than as a rushed handover at the end. We use your version control, your CI pipeline, and your coding standards wherever possible.
Which cloud platforms do you support?
AWS, Google Cloud, and Azure. We also deploy on-premise for clients who need it, using Docker and Kubernetes. About a third of our projects run on bare-metal GPU servers behind the client's firewall.
Talk to our engineers
Describe the problem you are trying to solve. We will reply within one working day with an honest assessment of whether AI is the right tool for it.