What is LLM fine-tuning?

Fine-tuning adapts an existing trained model using additional examples so it behaves more effectively for a particular task, domain or response style.

Do we need thousands of GPUs to build a custom model?

Usually not. Fine-tuning an existing model or building a smaller specialised model can require dramatically less infrastructure than training a foundation model from scratch.

A Small Language Model is a language model designed with fewer parameters than very large foundation models. SLMs can be useful for focused tasks, local deployment and lower-cost inference.

Is fine-tuning better than RAG?

They solve different problems. RAG provides information at runtime, while fine-tuning changes model behaviour. Some systems benefit from both.

Can you train models using our existing data?

Potentially, yes. The first step is evaluating whether the data is relevant, sufficiently clean, legally usable and representative of the task the model needs to perform.

Can the final model run privately?

Yes. Depending on the selected model and architecture, the resulting system can be deployed into private cloud, on-premise infrastructure or edge environments.

Can models be updated later?

Yes. We prefer versioned datasets, reproducible training pipelines and measurable evaluations so subsequent model versions can be compared reliably.

What we build

Custom AI model engineering

Custom models

Train and adapt models for jobs generic APIs miss — specialised LLMs, SLMs and domain models using your data and workflows.

At a glance

How we engage

Service: Custom models
Engagement: Scoped delivery
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The pressure

Off-the-shelf models miss domain language, formats and edge cases. Fine-tuning and specialised models earn their keep when accuracy on your work matters more than generic fluency.

What we build

Fine-tuning, domain adaptation and evaluation loops for models that must perform on your documents, codes and decisions.

Pipelines that keep training data, versions and rollout under control.

How we work

Prove lift against a baseline before scaling. Keep humans in the loop where mistakes are expensive.

When to engage

APIs are close but not good enough — and you have data and a clear job that justifies a specialised model.

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