Artificial Intelligence that earns its place in your workflow

The company started when a mid-size dairy processor in County Antrim asked whether a machine-learning model could predict equipment failures before they halted production. We said yes, built a proof of concept in five weeks, and the plant avoided two unplanned shutdowns in the first quarter. That single contract taught us something important: the hardest part of AI is not the algorithm. It is understanding the physical process well enough to choose the right data.

By the end of 2019 we had three clients, all in manufacturing. Our toolkit was narrow — time-series forecasting and anomaly detection — but the results were concrete and measurable.

Lockdowns pushed paper-heavy businesses online faster than they planned. We built document-extraction pipelines for legal firms and insurance brokers, pulling structured data from scanned PDFs, handwritten claim forms, and email threads. One broker in Derry cut claims-processing time by 60% within three months.

We also started working with natural language processing: sentiment classifiers for customer-support queues, entity extraction for compliance teams, and a custom search engine for a medical-devices catalogue with 18,000 SKUs.

A packaging company in Newry asked us to detect label defects on a production line running at 120 units per minute. We deployed an edge-inference model on an NVIDIA Jetson device, connected to two industrial cameras. Defect detection accuracy reached 99.1% after eight weeks of training on their own reject samples. The system paid for itself in four months by eliminating customer returns.

That year we also delivered a vehicle-damage assessment model for a fleet-management company and a weed-detection prototype for an agri-tech startup near Enniskillen.

Large language models changed the landscape quickly. We helped clients integrate retrieval-augmented generation into internal knowledge bases so staff could query policy documents, engineering manuals, and regulatory guidance in plain English. One housing association reduced the time tenants waited for accurate policy answers from two days to under ten minutes.

We also built fine-tuned summarisation models for a Belfast law practice that processes 200+ case files per month. The solicitors review AI-generated summaries and flag corrections; the model improves with every batch.

Deploying a model is half the job. Keeping it accurate over months is the other half. In 2024 we invested heavily in monitoring infrastructure: automated drift detection, retraining triggers, and model-performance dashboards that clients can read without a data-science degree.

We delivered end-to-end MLOps pipelines for three enterprise clients, including a logistics company whose demand-forecasting model now retrains weekly on fresh shipment data. Forecast error dropped from 14% to 6.2% over six months.

Our team has grown to nine people: five ML engineers, two data engineers, a project manager, and a domain analyst who spends the first week of every engagement embedded with the client's team. We work across manufacturing, logistics, legal, healthcare administration, agriculture, and financial services.

Current focus areas include multi-modal AI (combining vision and language models), on-device inference for environments without reliable internet, and responsible-AI auditing for regulated industries. We are also piloting an AI-readiness diagnostic — a structured assessment that tells organisations exactly where they stand before committing to a build.