Artificial Intelligence that actually ships

We build machine learning systems for companies that need answers this quarter, not next year. Based in Northern Ireland, deployed worldwide.

Send us your toughest problem
Server room with green LED lighting representing AI infrastructure
0Models in production
0Terabytes processed monthly
0Active client organisations
0% average uptime

What we build

Computer vision pipelines

Object detection, segmentation, and classification models trained on your own imagery. We handle labelling, augmentation, and deployment to edge devices or cloud endpoints. Typical turnaround from raw dataset to production API: eight weeks.

Natural language processing

Document classification, entity extraction, sentiment scoring, and summarisation. We fine-tune transformer architectures on domain-specific corpora so the model understands your industry vocabulary from day one. Accuracy benchmarks provided before go-live.

Predictive analytics

Time-series forecasting, churn prediction, demand planning. We connect directly to your data warehouse, build feature stores, and deliver predictions via scheduled batch jobs or real-time streaming. Most clients see ROI within three months of deployment.

Data engineering

Before any model can work, your data needs to be clean, joined, and accessible. We design ETL pipelines, set up feature stores, and build monitoring dashboards so your team knows exactly what flows where. We use Apache Spark, dbt, and Airflow depending on scale.

MLOps and model monitoring

Models degrade. Data drifts. We set up continuous training pipelines with automated retraining triggers, A/B testing frameworks, and alerting when prediction quality drops below your threshold. Your models stay accurate without manual babysitting.

Measured results from real projects

Agricultural supply chain, County Antrim

34% fewer spoiled shipments

We built a temperature and humidity forecasting model that predicted cold-chain failures 18 hours before they happened. The client rerouted 12 lorries per week to backup storage, cutting spoilage by a third over six months.

Insurance claims processor, Belfast

4.2 seconds per document

Their team was manually reviewing 600 claim forms a day. Our NLP pipeline now extracts policy numbers, dates, incident types, and monetary amounts automatically. Human reviewers handle only the 8% of documents flagged as ambiguous.

Retail footfall analytics, Derry

£140k annual savings

A camera-based counting system replaced manual headcounts across 11 stores. The model runs on-premise on modest hardware, respects privacy by processing silhouettes only, and feeds data into their existing BI tool every 15 minutes.

How a typical engagement works

Week 1: problem definition

We sit with your team, look at the data you have (and the data you wish you had), and write a one-page scope document. No jargon, no 80-slide deck. If the problem is not a good fit for machine learning, we say so.

Weeks 2 to 4: prototype

A working proof-of-concept trained on a sample of your data. You see real predictions on real inputs. We measure accuracy, latency, and cost so there are no surprises later.

Weeks 5 to 8: production build

The prototype becomes a production system with proper error handling, logging, and API documentation. We deploy to your cloud account so you own the infrastructure.

Week 9 onward: monitoring and iteration

We watch model performance, retrain when data shifts, and add features as your needs evolve. Monthly reports show prediction accuracy and usage volume in plain language.

Northern Ireland landscape with modern office building representing Ai Pro Platform location

Questions we hear often

It depends on the task. Image classification can work well with a few hundred labelled examples per category if we use transfer learning. Tabular prediction models usually need at least a few thousand rows. We run a quick feasibility check in the first week so neither of us wastes time on a dataset that is too thin.
Yes. We have delivered projects where the training data never left the client's network. We bring our code to your environment, not the other way around. This is common in healthcare and financial services engagements.
We charge a fixed fee per project phase. The scoping week is £2,400. Prototype phases typically range from £8,000 to £18,000 depending on complexity. Production builds are quoted after the prototype proves the concept. No hourly billing, no open-ended retainers.
We do, when they solve a real problem. We build retrieval-augmented generation systems that answer questions from your internal documents, and we fine-tune smaller models for specific tasks where a general-purpose LLM would be too slow or expensive. We are honest about what LLMs are good at and where simpler approaches win.
You do. Every line of code, every trained model weight, every configuration file lives in your repository from day one. If you decide to part ways after the prototype, you keep everything and can continue with another team.

Talk to us

Describe the problem you are trying to solve. Include rough data volumes and any deadlines if you know them. We reply within one working day.

Address:
391 Sheryl Side, Schaden-on-Thiel, Northern Ireland, UZ90 5RN, United Kingdom

Phone:
+44 1599 663798

Email:
[email protected]