Artificial Intelligence that actually ships

We are a small, focused team based in Northern Ireland. We build machine learning systems that run in production, not just in slide decks. If your data sits in spreadsheets or legacy databases, we turn it into models that make real decisions.

Talk to our engineers
Engineers collaborating on an AI model at Trust AI Vision
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Models in production
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Industries served
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Weeks to first deploy

What we build

Each engagement starts from your specific problem. We don't sell a platform; we design, train, and deploy models fitted to your data and your infrastructure.

Predictive analytics

Demand forecasting, churn prediction, pricing optimisation. We connect to your existing data warehouse, build regression or gradient-boosted models, and deliver predictions through a REST endpoint or a simple dashboard. Most projects reach production in four to six weeks.

Computer vision

Defect detection on production lines, document scanning, aerial image analysis. We fine-tune convolutional and transformer-based architectures on your labelled images. Typical datasets start at 500 annotated samples; we handle the labelling pipeline if you need it.

Natural language processing

Automated ticket routing, contract clause extraction, sentiment analysis across customer reviews. We fine-tune large language models on your domain vocabulary so the system understands terms specific to your industry rather than guessing from general knowledge.

Data engineering and MLOps

Before a model can learn, data needs to be clean, versioned, and flowing. We set up extraction pipelines, feature stores, and continuous training loops so your models improve automatically as new data arrives. Infrastructure runs on your cloud account; we never hold your data hostage.

AI strategy workshops

Not sure where machine learning fits? We run a two-day workshop with your leadership and technical teams. You leave with a prioritised list of use cases ranked by feasibility, expected ROI, and data readiness. No jargon-heavy slide decks; just a clear plan.

How a project moves forward

1. Discovery call

A 45-minute video call where we learn about your data sources, business goals, and existing tech stack. We ask pointed questions about volume, latency requirements, and who will use the output.

2. Data audit

We get read-only access to a sample of your data. Within a week we send back a written assessment: what is usable, what needs cleaning, and where the gaps are. This step is free for qualifying projects.

3. Proof of concept

A working prototype trained on your real data, tested against a holdout set. You see actual precision and recall numbers, not theoretical benchmarks. The PoC usually takes two to three weeks.

4. Production deployment

We containerise the model, set up monitoring for data drift, and integrate with your application through an API or batch pipeline. Deployment lands in your own cloud environment.

5. Ongoing support

Models degrade when the world changes. We offer monthly retrain cycles, performance dashboards, and on-call support. You can also bring the work in-house; we document everything and train your team.

Questions we hear often

It depends on the task. For tabular prediction problems like churn or demand forecasting, a few thousand rows of historical records is often enough. Computer vision tasks need at least 500 labelled images per class, though transfer learning can stretch smaller datasets further. During the data audit we tell you honestly whether your volume is sufficient or if we should collect more first.
Yes. About half our clients are based elsewhere in the UK, and a handful are in continental Europe. All collaboration happens over video calls and shared repositories. We visit on-site when a project requires access to physical infrastructure like factory cameras or edge devices.
A proof of concept runs between £8,000 and £18,000 depending on complexity. Full production builds range from £25,000 to £90,000. We price on deliverables, not hours, so you know the total before we start. The strategy workshop is a flat £3,500 for two days.
Absolutely. Every line of code, every trained weight file, and every pipeline configuration belongs to you upon final payment. We use your Git repositories from day one so there is never a handover bottleneck.
Python is the backbone: PyTorch for deep learning, scikit-learn and XGBoost for classical ML, FastAPI or Flask for serving. Infrastructure is typically Docker on AWS or Azure, with MLflow for experiment tracking. If you already use a different cloud or toolset, we adapt.

Talk to our engineers

Describe what you are trying to solve. We reply within one working day with an honest assessment of whether AI is the right approach.

5 Maggio Yard, Long Weimann-Stoltenberg Gardens, QT43 9UE, Northern Ireland, United Kingdom

[email protected]

+44 7574 940170

Northern Ireland countryside where Trust AI Vision is based