We build predictive models, language-processing pipelines and vision systems for mid-sized UK companies. Each project starts with your data, your constraints and a clear scope, then ships production-ready code you actually own.
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Our five-stage process
What we build
Demand forecasting, churn prediction, credit scoring. We work with tabular data from ERP and CRM systems, typically XGBoost or LightGBM ensembles served behind a REST endpoint. Recent project: a logistics firm reduced empty-mile journeys by 18 % after deploying our demand model across 23 depots.
Document classification, entity extraction, summarisation, sentiment analysis. We fine-tune transformer models on your domain vocabulary so the system understands "NQ5 1OI" is a postcode and "Volkman Place" is a street, not a person. Outputs integrate via webhook, email or direct database write.
Defect detection on manufacturing lines, aerial image segmentation, medical image triage. We handle labelling pipelines (CVAT or Label Studio), train YOLO or Mask R-CNN variants and optimise for edge deployment on NVIDIA Jetson or Intel NUC hardware when latency matters more than cloud convenience.
Already have a model but struggling to keep it running? We set up feature stores (Feast or Tecton), experiment tracking (MLflow), automated retraining triggers and A/B serving infrastructure. Our ML ops packages start at 12 hours per month and scale with your pipeline complexity.
Frequently asked
It depends on the task. For tabular prediction a few thousand labelled rows can produce useful results. Image classification usually needs 500+ examples per class. We run a free 30-minute data-sufficiency call to give you a straight answer before any money changes hands.
Yes. Our office is in North Sipes Gardens but most collaboration happens over video calls and shared Git repositories. We have delivered projects for clients in Scotland, Wales, Ireland and mainland Europe. Time-zone overlap is the only real constraint.
You do. Every line of code, every trained model weight and every dataset derivative we create during the engagement belongs to your company. Our standard contract makes this explicit in clause two. We retain no licence to reuse your data or models.
We define a minimum performance threshold at the scoping stage. If the prototype does not hit it after two iteration cycles, we stop and you pay only for work completed up to that point. We have triggered this clause three times in 87 projects, so it is rare, but the safety net exists.
Absolutely. About half our engagements are collaborative: we handle model architecture and training while your team manages data pipelines and business logic. We use your existing repos, CI tools and communication channels rather than imposing our own stack.
A scoping audit runs between £3,000 and £6,000. A full prototype-to-production cycle for a single model typically lands between £25,000 and £70,000 depending on data complexity and integration requirements. ML ops retainers start at £1,800 per month.
Describe the problem you want to solve and the data you already have. We will reply within one working day with an honest assessment of whether AI is the right tool and, if so, a rough scope and budget range.