Predictive modelling
We train regression and classification models on your historical records: sales forecasts, churn probability, equipment failure windows. Output is a live API your existing tools can query, not a static report.
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Models deployed since 2021
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What we actually build
Every project starts from a specific question your team can't answer fast enough with spreadsheets or gut feeling. Here are the categories we work in most often.
Natural language processing
Ticket routing, contract clause extraction, sentiment tracking across customer reviews. We fine-tune transformer models on your domain vocabulary so they understand your jargon from day one.
Computer vision
Quality inspection on production lines, document digitisation, aerial survey analysis. We handle annotation, model training and edge deployment so inference runs where the camera is, not in a distant data centre.
Anomaly detection
Fraud signals in transaction streams, unusual sensor readings in HVAC or water systems, outlier behaviour in user sessions. We deploy unsupervised and semi-supervised approaches depending on how much labelled data you have.
Workflow automation
Connecting AI outputs to the tools your team already uses: ERP triggers, email drafts, dashboard alerts. We wire everything together with event-driven architecture so nothing depends on someone remembering to check a screen.
How a typical engagement works
Most projects follow a four-phase arc. Timelines vary, but the structure keeps scope creep under control and gives you a working prototype before any big commitment.
1. Data audit (1–2 weeks)
We review your existing data sources, schema quality and access patterns. The deliverable is a written feasibility memo: what questions your data can answer today, what gaps exist, and a rough accuracy estimate for each proposed model.
2. Prototype sprint (2–4 weeks)
A small team builds a minimum viable model on a representative sample of your data. You get a live demo you can test with real inputs, plus a confusion matrix or error distribution so you know exactly where the model struggles.
3. Production hardening (3–6 weeks)
We retrain on the full dataset, add monitoring hooks, write integration tests, and deploy behind a versioned API. If the model needs to run on-premise or at the edge, we handle containerisation and hardware selection too.
4. Ongoing tuning
Models drift as your data changes. We offer monthly retraining contracts or teach your internal team to manage the pipeline themselves. Either way, you keep full ownership of the trained weights and code.
A small team, deliberately
We are six engineers and two project leads based in Upper Kreiger, Scotland. Keeping the team small means every client works directly with the people writing the code, not with an account manager relaying messages.
Our backgrounds span astrophysics, quantitative finance and biomedical research. That mix matters because the hard part of applied AI is rarely the algorithm; it is understanding the domain well enough to ask the right question.
We have been operating since 2021. Most of our revenue comes from returning clients, which tells us more than any award would.
Common questions
It depends on the task. A tabular classification model can work well with a few thousand labelled rows. Computer vision usually needs tens of thousands of images, though transfer learning can cut that by 80 percent. During the data audit we give you a concrete number for your specific use case.
Yes. We routinely work inside client VPNs or on-premise clusters. For regulated industries like healthcare and finance this is usually non-negotiable, and we have the tooling set up for it already.
A data audit starts at £2,400. A full prototype-to-production engagement ranges from £15,000 to £60,000 depending on data volume, model complexity and integration requirements. We quote fixed-price per phase, so there are no surprise invoices.
Completely. You receive the trained weights, the training pipeline code, and documentation sufficient for another engineer to retrain the model without us. We never hold models hostage behind a proprietary platform.
AWS, GCP and Azure all work. We also deploy on bare-metal GPU servers for clients who need lower latency or want to avoid per-request cloud inference costs. The choice usually comes down to where your existing infrastructure lives.
Talk to us
Describe the problem, not the solution. We will figure out whether AI is even the right tool, and tell you honestly if it isn't.
Where to find us
98 Roman Way, Upper Kreiger, Scotland, RZ22 8KW, United Kingdom
