Artificial Intelligence built for your actual data, not a demo

We design, train and deploy AI systems that solve specific problems inside your organisation. No generic chatbots. No dashboards nobody opens. Models that run in production and earn their keep.

Talk to our engineers
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Measured results

What our models have done for clients

Numbers from real deployments over the past eighteen months, not projections.

37% Reduction in manual document review time for a Belfast insurance broker
4.2× Faster defect detection on a Derry manufacturing line using our vision model
£280k Annual savings in logistics costs for a distribution firm after route optimisation
91% Accuracy on a custom NLP classifier trained on 14,000 labelled support tickets
Our process

From messy data to a running system

Five stages. The first two usually take two weeks combined. The whole cycle, from first call to production deployment, runs eight to fourteen weeks depending on data readiness.

1

Data audit

We sit with your team, look at what data you actually have, where it lives, and what shape it is in. Spreadsheets, databases, PDFs, API logs: all fair game. We write a short report on what is usable today and what needs cleaning.

2

Problem framing

Most AI projects fail because they try to solve the wrong problem. We work with your domain experts to pick one high-value question the data can actually answer. Then we agree on a success metric before writing any code.

3

Model development

Our engineers build a baseline model within two weeks and share results in a notebook you can inspect. We iterate on architecture, features and hyperparameters until we hit the agreed metric or identify what additional data would get us there.

4

Integration and deployment

The model gets packaged as an API endpoint, a batch job, or an embedded module depending on your stack. We handle containerisation, monitoring and rollback. Your developers get documentation and a Slack channel for questions.

5

Ongoing monitoring

Data changes. Models drift. We set up automated performance tracking and retrain on a schedule that fits your data cadence: weekly, monthly, or on-demand. You get a dashboard showing prediction accuracy over time.

Capabilities

What we build

Each engagement is scoped to a single deliverable. We do not sell "AI strategy" as a standalone product.

Document classification

Trained on your own document types. Invoices, contracts, claims, compliance reports. The model labels, routes and extracts key fields so your staff spend time on exceptions, not routine sorting. Typical accuracy above 90% after two training rounds.

Demand forecasting

Time-series models that predict sales, foot traffic, call volumes or inventory needs. We combine your historical data with external signals like weather, holidays and local events. Outputs feed directly into your planning tools via API.

Computer vision

Object detection and quality inspection for manufacturing, agriculture and retail. We train on as few as 500 labelled images when the defect type is well-defined. Runs on edge hardware or cloud, depending on latency requirements.

Anomaly detection

Spot fraud, equipment failure or process drift before it costs you. We build unsupervised models that learn what "normal" looks like in your data and flag deviations with a confidence score. Alerts go to your existing monitoring stack.

Data pipeline engineering

Your AI is only as good as the data flowing into it. We design extraction, transformation and loading pipelines that keep your model fed with clean, timely data. Built on open-source tools so you are never locked in.

Common questions

Things clients ask us early on

It depends on the task. For tabular prediction problems, a few thousand rows with a clear target variable is often enough to build a useful baseline. Image classification usually needs at least 500 labelled examples per class. Text classification can work with fewer if the categories are distinct. During the data audit we will tell you honestly whether your volume is sufficient or whether we should collect more before spending engineering time.
When they are the right tool. For open-ended text generation, summarisation or conversational interfaces, a fine-tuned LLM makes sense. For structured prediction, classification or anomaly detection, smaller purpose-built models are cheaper to run and easier to audit. We pick the architecture that fits the problem, not the one that generates the most press coverage.
Your choice. We can deploy on AWS, Azure, GCP, or on-premises if data sovereignty is a concern. For real-time inference at the edge, we package models into lightweight containers that run on NVIDIA Jetson or similar hardware. Hosting costs are yours to control; we provide estimates before deployment so there are no surprises.
The data audit and problem framing phase is a fixed fee of £3,500. After that, model development and deployment is priced on a time-and-materials basis, typically between £15k and £60k depending on complexity. Ongoing monitoring starts at £800 per month. We quote each phase separately so you can stop at any point without wasting spend.
Yes. Every project comes with a technical handover document, a retraining script and a recorded walkthrough. If your team has Python experience and access to the training data, they can retrain and redeploy without us. Many clients prefer to hand off monitoring to us, but it is never a requirement.
Get in touch

Tell us what problem you are trying to solve

We read every message and reply within one working day. If your question is quick, call us directly.

642 Windmill Close, East Jacobiford, Northern Ireland, XK5 3SW, United Kingdom

056 7757 1750

[email protected]

Engineers discussing AI architecture on a whiteboard in a meeting room