When should we fine-tune an LLM?

When you need a consistent style, format or domain vocabulary that prompting and retrieval cannot deliver. For most knowledge tasks, RAG is cheaper and easier to keep current.

Can you build a custom LLM for us?

We usually adapt an existing open model to your data rather than train from scratch, which gives similar quality at a fraction of the cost.

Where will the model run?

In your cloud account, on dedicated EU infrastructure or through an EU-hosted API, depending on your data requirements.

LLM Development Services

Engineer, adapt and deploy large language models for your domain. LLM applications, RAG, fine-tuning and evaluation, with multi-provider routing so you are never locked in.

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What it is

LLMs, engineered for your domain

A general model rarely fits a specific domain out of the box. LLM development is the work of grounding, adapting and evaluating a model until it is accurate and reliable for your task.

We build LLM-powered applications, add retrieval and fine-tuning where they earn their keep, and route across providers so you keep control of quality, cost and data residency.

LLM applications grounded in your data and systems.
Fine-tuning and evaluation where they measurably help.
Multi-provider routing so you avoid lock-in.

Fine-tuning vs RAG

Fine-tuning and retrieval-augmented generation solve different problems, and choosing the wrong one is a common and expensive mistake.

Use RAG: when the model needs facts from your documents, product data or policies. Knowledge stays current because you update the source, not the model, and answers can cite where they come from.
Use fine-tuning: when you need a consistent format, tone or domain vocabulary that prompting cannot deliver, or a smaller, faster model for a narrow task.
Combine both: when a domain-adapted model also needs current facts. This is less common than vendors suggest.

We test the simplest option first. Many projects that start with a plan to fine-tune end up with good prompting and retrieval.

LLM evaluation and monitoring

LLM evaluation is what turns a promising model into one you can trust. Before release we build a test set from real inputs with known good answers and score every version against it on accuracy, faithfulness to the source, format and safety.

In production we monitor quality on sampled outputs, cost and latency per request, and drift when your data or users change. When a new model version comes out, the same test set shows within hours whether switching is worth it.

What we do