AI Development Company in the UK

Look at what they ask before they quote. A credible partner wants to know the task, the volume, what a wrong answer costs and what data is involved; proposes a pilot rather than a programme; explains how accuracy will be measured; puts running costs in writing; and confirms that code, prompts, data and accounts will be yours. Be wary of anyone who leads with a platform or a single favourite model.

Because most of the risk in AI stays hidden until it meets your data. A pilot on one workflow tests accuracy, cost per task and how your team works with the output, for a small fraction of a programme budget. If it works, you scale with evidence; if not, you have spent a bounded amount to find out — and you keep the evaluation data and findings.

Running costs are mainly model API fees, which grow with volume and prompt size, plus hosting, search infrastructure, monitoring and a share of support time. We measure the real cost per task during the pilot and give you a monthly range at your forecast volumes before you decide to scale. Model fees are paid to the provider, on an account in your name.

If the AI will process personal data, quite possibly. UK GDPR requires a data protection impact assessment where processing is likely to result in a high risk to individuals, and the ICO’s guidance on AI indicates that many AI uses meet that bar. Your DPO decides; we supply the data-flow map, provider terms and technical controls the assessment needs.

That is the normal state of the market, so we plan for it. The model sits behind one interface in your code, and your evaluation set lets us test a new model against the current one on your own examples. If it is better, or cheaper at the same quality, switching is a measured change rather than a rebuild.

You do. The code, prompts, evaluation sets, documentation and provider accounts belong to you from the start, and we will sign an NDA before discovery if you need one. That matters more with AI than with most software: without the prompts and evaluation data, nobody else can safely maintain or improve the system.

Early and in writing. We share the data-flow diagram, provider terms, processing region and access controls before the build, answer their security questionnaire, and design within their policies on identity, logging and retention. Where data must not leave your environment, we can use open models hosted in your own cloud account.

We tell you, and explain why. Sometimes simple rules or a better form beat a language model on cost and reliability; sometimes the data is not ready yet. A pilot that ends with a clear “not like this” still leaves you with a measured baseline, an evaluation set and a better-informed plan.

Agents that carry out multi-step work across your systems, automations that read and route documents, knowledge assistants built on retrieval (RAG), customer-facing chatbots, and LLM features inside existing products. Our AI development services page covers each in detail; this page is about how we run those projects so they reach production.

Often, yes. We start by building an evaluation set from real usage and measuring how the current system actually performs, then review prompts, retrieval, running costs and data handling. You get an honest view of what to keep, what to fix first and what it costs to run — before any rewrite is discussed.