Most AI projects stall in the proof-of-concept stage. Ours go live. We design, train, and deploy machine-learning systems for mid-size businesses across Scotland and the rest of the UK, with a typical timeline of eight to twelve weeks from kickoff to production.
Talk to our AI team
What we do
Each engagement starts with your data and your business question. We pick the right technique, not the trendiest one.
We build regression and classification models that forecast demand, churn, or equipment failure. One logistics client cut spoilage costs by 22% within three months of go-live using a gradient-boosted model trained on their last five years of dispatch data.
From quality-control cameras on a factory floor to aerial image analysis for agriculture, we train convolutional networks that see what humans miss. Our smallest deployed model runs on a £35 edge device, no cloud round-trip needed.
Chatbots, document classifiers, sentiment dashboards. We fine-tune large language models on your own corpus so they speak your terminology. A legal-services client reduced contract review time from four hours to forty minutes per document.
An AI model is only as reliable as the data feeding it. We audit your existing data stores, design extraction pipelines, and set up monitoring so model drift gets caught early. Most clients already have the data they need; it just lives in the wrong shape.
Your team should understand what the model does and why. We run two-day on-site workshops covering model interpretation, bias detection, and day-to-day maintenance. Participants leave with a runbook specific to their deployment.
Proof, not promises
We measure success by what changed in the client's operations, not by how complex the model architecture looks on a slide deck.
Trained an ensemble model on 2.3 million historic shipment records. The client now pre-positions vans based on predicted next-day volumes, saving an estimated £180k per year in fuel and overtime.
A convolutional neural network flags potential abnormalities in livestock ultrasound scans before the vet reviews them. False-positive rate sits at 4.1%, and the system processes each image in under two seconds on a local GPU.
Sensor data from temperature, pH, and CO₂ probes feeds a time-series model that predicts optimal washback timing. The head distiller reports a measurable consistency improvement across batches since deployment in March 2024.
How it works
We do not hand over a Jupyter notebook and call it done. Every project follows this path.
We sit down with your domain experts, audit available data sources, and agree on a measurable success metric. If the data isn't ready, we build the pipeline first. No point training a model on garbage.
A working model appears quickly, trained on a representative subset. You see early results, test edge cases, and tell us where it breaks. This feedback loop matters more than any algorithm choice.
We retrain on the full dataset, add monitoring hooks, write integration code for your existing systems, and deploy to your preferred environment: on-premise server, private cloud, or edge device.
Models degrade as the world changes. We offer monthly retrain cycles, drift alerts, and quarterly performance reviews. You can also bring support in-house using our training programme.
Questions
Get in touch
Describe what 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.
149 Alize Place, DuBuque-upon-Jacobson, Scotland, QB47 9PN, United Kingdom
