Artificial Intelligence that solves the problem you actually have

Most AI projects fail because they start with technology instead of the business question. We work the other way round: you describe the friction, we figure out whether a model, an automation, or a simple rule engine fixes it fastest.

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Data scientist working on AI models at a multi-monitor workstation
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Ongoing retainer clients

What we actually build

Every engagement starts with a scoping call. If AI is the wrong tool, we will tell you before you spend a penny.

Predictive analytics

We build regression and classification models on your historical data. Typical use cases include demand forecasting for retail, churn prediction for subscription businesses, and equipment failure alerts for manufacturing. Training usually takes two to four weeks from clean data handover to a validated model you can query via API.

Document intelligence

Invoices, contracts, medical records, planning applications: if your staff spend hours copying data from PDFs into spreadsheets, we can train an extraction pipeline that does it in seconds. Our models handle handwritten text, tables, and multi-language documents. Output lands directly in your existing database or ERP.

Workflow automation

Not every problem needs a neural network. Sometimes a well-designed rule engine or a lightweight language model integration saves more time than a custom-trained model. We audit your processes, identify the bottlenecks, and wire up automations using the simplest tool that works: Python scripts, cloud functions, or off-the-shelf APIs stitched together with proper error handling.

On-site AI training

We run two-day workshops at your office. Day one covers the fundamentals: what models can and cannot do, how to evaluate vendor claims, and where the legal boundaries sit under the EU AI Act and UK frameworks. Day two is hands-on: your team builds a small working prototype using your own data. Groups of up to fifteen people work best.

Data engineering

A model is only as useful as the data feeding it. We design and maintain ETL pipelines that pull from your CRM, warehouse system, web analytics, and third-party APIs, then clean and version everything so retraining is reproducible. We prefer dbt, Airflow, and BigQuery, but we adapt to whatever stack you already run.

How a typical project runs

Most engagements follow four stages. Timelines vary, but the structure stays the same.

Scoping call

A 45-minute video call where you walk us through the problem. We ask about data availability, existing systems, and what a successful outcome looks like in concrete terms. No charge for this step.

Data audit

We review a sample of your data under NDA. Within five working days you receive a written feasibility report: can a model improve on your current process, what accuracy range is realistic, and what it will cost to build.

Build and validate

We train the model, test it against held-out data, and iterate until performance meets the threshold we agreed on. You get weekly progress updates with charts you can actually read.

Deploy and monitor

The model goes live behind an API endpoint or inside your existing application. We set up drift detection alerts so you know when retraining is needed, and we handle the first three months of monitoring at no extra cost.

Common questions

If yours is not listed, ask us directly through the contact form below.

It depends on the task. For tabular classification problems like churn prediction, a few thousand labelled rows often suffice. Image recognition tasks typically need tens of thousands of examples per category, though transfer learning can cut that number dramatically. During the scoping call we estimate sample size requirements specific to your situation.
No. About half our clients have fewer than fifty employees. The deciding factor is whether you have a clear, repeatable business problem and enough data to learn from. Company size matters less than problem clarity.
We agree on a minimum performance threshold before build begins. If we cannot reach it after two full iteration cycles, we stop the project and you pay only for the data audit phase. This has happened three times in five years, each time because the underlying data turned out to contain less signal than the initial sample suggested.
Yes. We regularly set up secure remote-desktop access to client servers or work on-site. For healthcare and financial services clients this is the norm rather than the exception. We also support federated learning setups where the data never leaves the source machines at all.
We quote fixed prices per project phase. The data audit is a flat fee (usually between £1,200 and £2,500). Build-and-validate pricing depends on complexity and is scoped after the audit. Ongoing monitoring retainers start at £400 per month. We do not bill by the hour.

Talk to us

Describe the problem in a few sentences. We will reply within one working day.

Office
7 Silvia Side, Koch-under-Turcotte, Scotland, NR0 4SF, United Kingdom

Phone
+44 7812 265177

Email
[email protected]

Aerial view of a Scottish town near our office