Artificial Intelligence that fits your business, not the other way around

We design, build and maintain AI systems for mid-size companies across the UK. No black-box magic: every model we deliver comes with documentation your team can actually read.

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Data science team reviewing Artificial Intelligence model outputs on a large display
127models deployed
96%client retention
14industry verticals
4.2×average ROI

What we do

Services built around your data

Each engagement starts with your actual problem, not a pre-packaged product. Here is where most of our clients find value.

Predictive analytics

We train regression and classification models on your historical data so you can forecast demand, churn or equipment failure weeks before it happens. Most projects reach production in under eight weeks.

Natural language processing

From ticket classification to sentiment tracking, we build NLP pipelines that process thousands of documents daily. Our models handle British English idiom well, because that is what we train on.

Computer vision

Quality inspection on a production line, document digitisation for archives, or object detection in satellite imagery. We pick the architecture that matches your volume and latency constraints.

AI strategy consulting

Not sure where machine learning would help most? We run a two-week discovery sprint: interview your team, audit your data estate and deliver a prioritised roadmap with cost estimates for each initiative.

Model governance and compliance

Regulators are catching up. We help you document model lineage, bias testing and data provenance so your AI systems meet the EU AI Act requirements before they bite.

Data pipeline engineering

A model is only as good as the data feeding it. We build extraction, transformation and loading pipelines that keep your training and inference data fresh, validated and version-controlled.

Whiteboard mapping out an Artificial Intelligence project workflow

Why most AI projects stall and how we prevent it

Research from Gartner suggests that roughly half of all enterprise AI projects never leave the proof-of-concept stage. The reasons are usually organisational, not technical. We have seen this pattern dozens of times and built our delivery model to counter it.

Here is what we do differently:

  • We define a measurable business outcome before writing any code.
  • Stakeholders see a working prototype within the first three weeks.
  • Every model ships with monitoring dashboards so drift gets caught early.
  • We train at least two people on your side to maintain the system after handover.

The result is AI that actually runs in production and keeps running, month after month.

How it works

From first call to live system in four stages

Every engagement follows this rhythm, though the duration of each stage depends on data readiness and regulatory requirements.

1

Discovery and scoping

We spend two weeks understanding your operations, data sources and success criteria. You get a written scope document with timelines, costs and risk factors. No commitment beyond this point.

2

Rapid prototyping

Our engineers build a minimum viable model using a representative sample of your data. You review accuracy metrics and user-interface mockups together with us. Iteration happens in weekly sprints.

3

Production deployment

Once the prototype meets the agreed performance threshold, we containerise the model, connect it to your existing systems via API and set up automated retraining schedules. Average deployment time: eleven days.

4

Monitoring and knowledge transfer

We install drift-detection alerts, hand over all source code and run training sessions for your team. Optional ongoing support is available on a monthly retainer, but the system is designed to work without us.

£8.3mclient savings in 2025
38%average error reduction
11 daysmedian time to deploy
23active retainer clients

Common questions

Things prospective clients ask us

How much data do we need before starting?

It depends on the task. For tabular predictive models, a few thousand rows is often enough to build a useful prototype. Image and NLP tasks typically need more. During discovery we audit what you have and tell you honestly whether it is sufficient or if we need to augment it first.

Do you work with companies outside Scotland?

Yes. Our office is in Newton Gusikowski, but roughly half our clients are based in England and Wales. We handle most collaboration remotely with fortnightly on-site visits when the project warrants it.

What does a typical project cost?

Discovery sprints start at £4,500. Full model-build-and-deploy projects range from £18,000 to £90,000 depending on complexity, data volume and integration requirements. We quote fixed price after discovery so there are no surprises.

Can you integrate with our existing software?

Almost always. We expose models through REST APIs that plug into most modern platforms. For legacy systems we have built custom connectors for SAP, Oracle and various in-house databases. If something unusual comes up we flag it during discovery.

Who owns the intellectual property?

You do. All code, trained model weights and documentation are yours once the final invoice is paid. We retain no licence to your data or models.

Get in touch

Let us talk about your data

Whether you have a clear project brief or just a hunch that AI could help, we are happy to chat. Initial assessments are free and confidential.

Address:
72 Chapel Hill, Newton Gusikowski, Scotland, VA21 2LY, United Kingdom

Phone:
+44 916 588 7433

Email:
[email protected]