Services

AI development company for UK businesses

Getting an AI demo working takes an afternoon. Getting it right often enough, cheaply enough and safely enough for real customers is the actual work. That’s the part we focus on.

Discuss Your Project

Start with a problem that’s worth money, not with “we should do something with AI”. Good first projects tend to share a few traits: there’s a lot of text or documents involved, a person is currently doing something repetitive, and an occasional mistake can be caught by a review step. Answering questions from your own documents, extracting data from varied paperwork and drafting responses for staff to check all fit this pattern.

Poor first projects need perfect accuracy with nobody checking. Large language models produce wrong answers confidently, and no amount of prompt tuning fully removes that. So part of the design is deciding what happens when the model is wrong: who reviews, what gets shown to customers, and when it should hand over to a person.

Measuring whether it works

You can’t improve what you’re judging by eye. We create an evaluation set from real examples of your work, score the system against it, and run it again whenever a prompt or model changes. This is what separates a system you can trust from a demo that happened to go well. It also allows us to test cheaper models, as you often don’t need the most expensive one for every task.

Your data, UK GDPR and the EU AI Act

Sending personal data to an AI provider is a data protection decision that needs thinking through. Check whether the provider trains on your data, where it’s processed, and which contract terms apply. The ICO publishes guidance on AI and data protection, and if you do business in the EU, the EU AI Act may apply to you as well. We’ll design with this in mind, though you should take your own legal advice for high-risk uses.

AI Development services

AI AutomationAutomating work that rules can’t handle, such as messy emails and documents, with people checking what matters. Generative AIApplications built on large language models, including retrieval on your documents and content generation. AI ChatbotsCustomer and staff assistants that answer from your own content and hand over to people when they should. Machine LearningPrediction, classification and anomaly detection using your own data, deployed and monitored in production. AI IntegrationAdding AI to the software you already use, such as your CRM, helpdesk or internal systems, securely and measurably. AI Web AppsNew web products with AI at their core, built with streaming interfaces, usage limits and proper accounts.

Why AI projects stall

A great demo that’s unreliable

It handled the five examples it was shown, then fell over on real documents and odd questions.

Unpredictable costs

Token usage climbs with volume, and nobody modelled it before launch.

Data that isn’t ready

The knowledge the model needs is scattered, out of date or in contradictory documents.

No fallback

Nothing was planned for when the model is unsure or wrong, so customers see the mistakes.

What we build

Knowledge assistants

Answering questions from your documents using retrieval, with sources shown so answers can be checked.

Document processing

Extracting structured data from invoices, forms, contracts and emails.

AI features in your product

Summaries, classification, search and drafting tools inside your software.

AI agents

Systems that use your tools to complete multi-step tasks, with limits and human approval where it matters.

Evaluation and monitoring

Test sets, quality tracking and cost dashboards that stay in place after launch.

Feasibility studies

A short, honest test on your real data, before you commit to a full build.

How an AI project runs

  1. Pick the problem

    Find a use case with clear value and an acceptable cost of error.

  2. Prove it on real data

    Run a quick feasibility test using your own examples.

  3. Build with evaluation

    Develop the system alongside a test set, so that quality is measured.

  4. Launch with oversight

    Roll out gradually, keep people in the loop and watch quality and cost.

How all our projects run

Where it fits

  • Customer and staff support from your knowledge base
  • Processing high volumes of documents
  • Search that understands what people mean
  • Drafting first versions for people to review

Technology

  • Claude
  • OpenAI
  • Gemini
  • Llama
  • pgvector
  • LangChain
  • Python
  • Laravel
  • AWS Bedrock
  • Azure OpenAI

Sectors we think about here

AI Development questions

How accurate can an AI system be?
It depends on the task and your data, and we’d measure it on your own examples rather than promise a number. Language models do make mistakes, so important decisions should keep a person involved.
Will our data be used to train AI models?
That depends on the provider and plan. Business and API plans from the major providers generally state that customer data isn’t used for training by default, but always check the current terms.
Do we need to train our own model?
Rarely. Most business uses can be served by an existing model combined with your data through retrieval. Training or fine-tuning is occasionally worthwhile, but it’s usually not the place to start.
What does it cost to run?
There’s the build, then ongoing usage based on volume and model choice, plus monitoring and upkeep. We estimate usage costs on real examples before you commit.

Related services

Further reading

Interested in AI Development?

Outline what you need, and we will reply by email with questions or next steps.

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