We are a small engineering team in Wales. We design, train and deploy machine learning systems for companies that have real data problems, not PowerPoint ones. If you can describe the decision you need to automate, we can probably build the model.
Talk to our engineers
What we build
Demand forecasting, churn prediction, dynamic pricing. We work with your historical data to build regression and classification models that update themselves weekly. Most clients see a measurable ROI within three months of deployment.
Quality inspection on production lines, document digitisation, aerial image analysis. We fine-tune convolutional and transformer-based architectures on your labelled images. Typical training cycles run two to four weeks depending on dataset size.
Automated contract review, support ticket classification, sentiment tracking across social channels. We build pipelines around large language models and fine-tune them on your domain vocabulary so they understand the jargon your staff actually uses.
Already have a model that works in a notebook but breaks in production? We containerise it, set up monitoring, build retraining triggers and connect it to your existing APIs. We handle the plumbing so your data scientists can focus on research.
How it works
You describe the decision or process you want to automate. We ask about your data sources, volumes, formats and any compliance constraints. By the end of the call we can tell you whether the project is feasible and roughly how long it will take.
We connect to your data warehouse, run profiling scripts and produce a short report covering data quality, missing values, feature candidates and labelling requirements. This report is yours to keep regardless of whether you proceed.
We train a baseline model, measure its accuracy against your KPIs and present the results in a live demo. No slides, just a working system you can poke at. If the numbers do not hit the agreed threshold, you pay nothing for this phase.
The approved model gets packaged into a container, deployed behind a REST API, and connected to your internal tools via webhooks or direct integration. We set up dashboards for accuracy drift and automated alerts.
Models degrade as real-world data shifts. We offer monthly retainer plans that include scheduled retraining, performance reviews and priority bug fixes. Most clients stay on the retainer because it is cheaper than hiring a full-time ML engineer.
Results
Built a time-series model that predicts next-day parcel volumes per depot with 94% accuracy. The client reduced temporary staffing costs by 18% in the first quarter after deployment.
Trained a YOLOv8 model on 12,000 labelled images of pipe surfaces. Detection rate reached 98.1%, replacing a manual inspection step that previously required two full-time staff per shift.
Developed a classification model that flags abnormal radiographs before a veterinary radiologist reviews them. Average turnaround time for urgent cases dropped from 4 hours to 22 minutes.
Questions
It depends on the task. For tabular prediction problems, a few thousand rows is often enough to build a useful baseline. Computer vision tasks typically need at least 1,000 labelled images per class, though transfer learning can stretch smaller datasets surprisingly far. During the scoping call we will give you an honest assessment of whether your data volume is sufficient.
Your choice. We can deploy to your own cloud account (AWS, Azure, GCP), to on-premises GPU servers, or to our managed infrastructure. Clients in regulated industries usually prefer on-prem or a dedicated VPC. We configure the environment either way.
We agree on a performance threshold before the prototype phase begins. If the prototype does not hit that number, you do not pay for the prototype work. We eat the cost. This has happened twice in four years. In both cases the root cause was data quality, and we helped the client fix the underlying pipeline before restarting.
Yes. About a third of our clients are in mainland Europe and we have done two projects for companies in Canada. Time zone overlap matters more than geography. We prefer at least four shared working hours per day so that async communication does not slow the project down.
You do. All model weights, training scripts, data pipelines and documentation are transferred to you at project close. We retain no licence to use your data or your model. The only thing we keep is anonymised performance metrics for our internal benchmarking.
Talk to us
Our office is in Wales, but most of our work happens remotely. If you prefer a face-to-face meeting, we are happy to travel within the UK.
70 Freeda Gate, Jaskolskiridge, Wales, XM0 5IX, United Kingdom