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LLM integration and fine-tuning · United Kingdom Language models, integrated into the software you already run.

LLM integration services for UK software teams: connect OpenAI, Anthropic, Google or open-weight models to existing applications, with fine-tuning, evaluation, guardrails, observability and cost control.

Discuss your project

UK-registered company. International engineering team.

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LLM integration and fine-tuning for UK businesses.

You already have the product. The question is how to add language-model capability to it without a rewrite, a runaway bill or an unpredictable feature. Krapton integrates large language models into existing web, mobile and enterprise applications for UK software teams, and fine-tunes or adapts models where a general model is not enough.

We bring the engineering discipline around the model: prompt management, structured outputs, guardrails, evaluation, observability, caching and fallbacks between providers. Your team ends up with a capability they can operate, not a dependency they cannot see into.

  • OpenAI and Azure OpenAI
  • Anthropic Claude
  • Google Gemini
  • Llama and Mistral
  • Vercel AI SDK
  • LangSmith and observability tools
  • Node.js and Python
  • PostgreSQL

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Where it earns its keep.

  • 01 /

    Model integration

    OpenAI, Anthropic, Google or open-weight models connected to your application through a managed abstraction layer.

  • 02 /

    Fine-tuning and adaptation

    Fine-tuned or instruction-tuned models for narrow tasks where consistency, format or cost matter.

  • 03 /

    Guardrails and validation

    Input filtering, output schemas, policy checks and fallbacks that keep behaviour inside bounds.

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    Observability and cost

    Tracing, quality metrics and spend per feature, with routing to cheaper models where quality allows.

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Built to be trusted in production.

Evaluation before release, guardrails by design and people in control of the decisions that matter.

AI development overview

  1. 01

    Integration assessment

    Where the capability fits, what data it needs, latency and cost constraints and the risks to manage.

  2. 02

    Abstraction and guardrails

    A provider-agnostic layer, structured outputs, validation and safe fallbacks designed before features are built.

  3. 03

    Evaluation and tuning

    A test set for the task; prompt engineering first, fine-tuning where evidence shows it pays.

  4. 04

    Operate

    Tracing, dashboards, cost alerts and a process for upgrading models without regressions.

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The decisions that shape the build.

Fine-tuning needs curated data and clear evaluation to be worth it; most tasks are better served by prompt design and retrieval first. Provider terms, data residency and model deprecation cycles all affect the design.

What every AI engagement includes

  • A use case with a measurable result and representative examples
  • An evaluation set rerun on every prompt, model or data change
  • UK GDPR-aware data flows, with UK or EU hosting where residency matters
  • Human review and defined permissions for anything consequential
  • Cost and quality monitoring after launch

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Applied AI, connected to a real product.

Krapton’s own AI products, then a success story from our international portfolio describing the work delivered.

Dental.AI project visual

Healthcare AI

Dental.AI

Dental.AI set out to give patients instant, AI-driven insight into their oral health from a single dental image — which meant pairing a clinical-grade analysis pipeline with a web experience patients actually trust.

Read the success story

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LLM integration and fine-tuning: questions UK teams ask.

Should we fine-tune a model or use prompts and retrieval?

Start with prompts and retrieval; they cover most needs and are easier to maintain. Fine-tune when you need consistent format or style at scale, lower latency or cost on a narrow task, and you have enough good examples. We test both against your evaluation set.

Can you integrate AI into a legacy application?

Yes. We add the capability through APIs and an integration layer so the existing system changes as little as possible, and we handle the cases where the legacy stack cannot call a model directly.

How do you avoid being locked into one AI provider?

With a provider-agnostic layer, standard prompts and evaluation sets, so switching or routing between OpenAI, Anthropic, Google and open-weight models is a configuration change with measured impact.

How do you keep costs predictable?

Token budgets per feature, caching, routing simple requests to smaller models, usage limits and cost dashboards with alerts.

Can models be hosted privately in the UK?

Yes. Open-weight models can run in your UK cloud environment, and the major providers offer UK or EU regions with enterprise terms. We recommend based on your data and compliance needs.

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Connect the capabilities.

Let’s scope your LLM integration.

Bring the task, the examples and the systems involved. We’ll tell you what an evaluation set, a prototype and a first release would look like.

What are you thinking?