Most AI projects stall in the proof-of-concept phase. Ours reach production. We work with UK businesses that have real data, real constraints, and no patience for demos that never deploy.
Talk to our engineers about your data
What we build
We build regression and classification models that forecast demand, flag risk, or score leads. Each model ships with a monitoring dashboard so you know when accuracy drifts. Typical build time: six to ten weeks from first data handoff to production API.
Invoices, contracts, inspection reports: if your staff spend hours copying data between PDFs and spreadsheets, we replace that with an extraction pipeline. Our latest deployment processes roughly 4,000 invoices per day for a logistics firm in Belfast.
Sensor streams, transaction logs, network traffic: we deploy models that flag outliers within seconds. One manufacturing client cut unplanned downtime by 37% in the first quarter after go-live.
Before any model can train, the data needs to be clean, joined, and versioned. We design ETL pipelines on cloud infrastructure, connect legacy databases, and set up data quality checks that run automatically every night.
How it works
We follow a fixed sequence. Skipping steps leads to rework, so we don't.
We spend two to five days reviewing your existing data sources, formats, and volumes. You get a written report that says, plainly, whether an AI approach is worth pursuing or whether a simpler rule-based system would do the job better.
We agree on a fixed scope, a fixed price, and a delivery date. No open-ended retainers. If the project grows, we re-scope before writing another line of code.
Every fortnight you see working software, not slide decks. We demo the model, walk through accuracy metrics, and adjust direction based on what the data actually shows.
We deploy to your cloud account, not ours. You own the infrastructure. We set up alerts for data drift and model degradation, and we stay available for three months of post-launch support at no extra charge.
Results so far
Questions
It depends on the problem. For a classification task like fraud detection, a few thousand labelled examples often suffice. For demand forecasting, we prefer at least two years of weekly data. During the data audit we tell you honestly whether your volume is enough or whether we need to augment it.
Yes. About half our clients are based elsewhere in the UK, and we have two in the Republic of Ireland. We work remotely for most of the project and travel for kickoff meetings and final deployment reviews when needed.
We define a minimum accuracy threshold at the start. If we cannot meet it after two full sprint cycles, we stop the project and you pay only for work completed. This has happened once in 23 projects. The root cause was insufficient label quality, which we now screen for during the audit phase.
You do. All code, trained model weights, and documentation belong to you upon final payment. We retain no licence to your data or models. We may use anonymised performance metrics in case studies, but only with your written consent.
We deploy models as REST APIs or containerised services, so they slot into most architectures. We have integrated with Salesforce, SAP, Dynamics 365, and various bespoke ERP systems. If your stack is unusual, we assess compatibility during the audit and flag any concerns early.
Reach us
52 Derwent Close, High Goyette Cross, Northern Ireland, SA6 3AE, United Kingdom
Phone: +44 337 688 7336
Email: [email protected]
We typically reply within one working day. If your enquiry is urgent, phone is fastest.
