Practical Artificial Intelligence that ships on time

We design, train and deploy machine learning systems for UK businesses that need answers from their data, not slide decks about the future. Threat detection, predictive maintenance, document parsing: pick a problem, we will solve it.

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Real-time AI data visualisation on a monitor in a modern dark office
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Models in production
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Data points processed daily
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Average uptime last 12 months
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Weeks to first deployment

What we build

Every project starts with a real business question. Here are the categories we work in most often.

Threat detection

Our anomaly detection pipelines monitor network traffic, transaction logs and access events in real time. They flag suspicious patterns within 200 milliseconds, giving your security team a head start before damage spreads. We train on your own historical incident data so false-positive rates stay below 0.3%.

Predictive analytics

Demand forecasting, churn prediction, equipment failure probability: we build regression and classification models tuned to your domain. A logistics client reduced empty-mile costs by 18% in four months using our route optimisation model. Your data tells a story; we make it readable.

Document intelligence

Contracts, invoices, medical records, regulatory filings: our OCR and NLP pipeline extracts structured data from messy PDFs and scanned images. One insurance broker cut claims processing time from 14 minutes per document to under 90 seconds after we deployed a custom extraction model for them.

Internal AI assistants

We build retrieval-augmented generation (RAG) systems grounded in your internal knowledge base. Staff get accurate answers sourced from your own policies and documentation rather than generic web content. All queries stay on your infrastructure; nothing leaks to third-party APIs unless you choose otherwise.

Model monitoring and drift detection

Models degrade. Input distributions shift, upstream schemas change, seasonal patterns evolve. We set up automated monitoring that alerts you when prediction quality drops, and we retrain on a schedule that fits your data cadence, whether that is weekly or quarterly.

How a project runs

Week 1: scoping call and data audit

We spend two hours understanding your problem, then review a sample of your data for quality, volume and bias risks. You get a written feasibility memo within three working days.

Weeks 2–3: prototype

A working model on a subset of your data, tested against a hold-out set. We share precision, recall and F1 scores, not vague accuracy percentages. If the numbers disappoint, we stop here and you owe nothing beyond the scoping fee.

Weeks 3–5: production build

Containerised model, API endpoints, logging, authentication. We deploy to your preferred cloud or on-premise environment. Infrastructure-as-code so nothing is click-configured.

Week 6: handover and training

Your team gets documentation, a recorded walkthrough and a two-hour live session. We transfer all code and model artefacts to your repository.

Ongoing: support retainer (optional)

Monthly model health reports, retraining runs, and a four-hour response SLA for production incidents. Most clients stay on this plan for the first year.

Results from recent projects

Automated warehouse with conveyor belts and robotic arms

Fulfilment centre, Midlands

Predictive demand model trained on 18 months of order data. The warehouse now pre-stages stock 48 hours ahead of spikes, reducing overtime labour costs.

22% drop in pick-and-pack labour spend
Cybersecurity operations centre with network traffic displays

Financial services firm, Belfast

Real-time fraud detection model processing card transactions. Integrated into their existing Kafka pipeline with zero downtime migration.

Fraud losses down 41% in first quarter

Questions we hear often

Most engagements fall between £15,000 and £60,000 depending on data complexity and deployment requirements. The scoping call is free. We quote a fixed price after the data audit, so there are no surprises at invoice time.
Not necessarily. We can work inside your VPN or cloud tenancy so data never leaves your perimeter. For the initial feasibility check we only need a representative sample, which can be anonymised or synthetic.
Python (scikit-learn, PyTorch, Hugging Face Transformers), containerised with Docker, orchestrated via Kubernetes or AWS ECS. We use MLflow for experiment tracking and DVC for data versioning. If you already have a preferred stack, we adapt.
Yes. About half our projects are collaborative: we handle the ML engineering while your developers own the application layer and CI/CD pipeline. We join your standups and use your issue tracker.
We set a minimum performance threshold at the start. If the prototype cannot meet it, we share our findings and you pay only the scoping fee. We would rather be honest early than deliver a model that sits unused.
Every project includes a data protection impact assessment. We follow the UK GDPR framework and ICO guidance on automated decision-making. Where a model influences decisions about individuals, we build in explainability features so you can demonstrate compliance.

Talk to us about your data

Describe the problem you want to solve. We will reply within one working day with an honest assessment of whether AI is the right tool.

35 The Ridgeway, Bins-Block-upon-Wilderman, Northern Ireland, DV4 2TI, United Kingdom