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A general model knows everything. Yours should know your business.

Models tuned and grounded on your domain's vocabulary, documents and risk rules, for answers specialists actually trust.

Let's Build Together

One call with a senior engineer. A straight answer on what it would take.

Illustration: Domain-specific / vertical LLM solutions

Where you are. Where you’ll be.

You need this if

  • General AI tools get your terminology wrong
  • Regulated content can't be sent to public model APIs
  • Inference costs are too high for the volume you need

What changes for your business

  • Answers your specialists trust, with citations
  • Lower inference cost from right-sized models
  • Sensitive data kept inside your boundary

What we hand over

  1. Domain evaluation sets written with your experts
  2. Retrieval-augmented generation over your knowledge base
  3. Fine-tuned or small domain models where justified
  4. Private deployment and ongoing evaluation

What it is

A domain-specific LLM is a language model adapted to one industry or company, through retrieval over its documents, fine-tuning or a smaller specialized model, so it uses the right terminology and rules. It matters wherever a general model's mistakes are costly, such as finance, healthcare, law or engineering.

General-purpose models stumble on industry jargon, internal product names and regulated wording. We close the gap with the cheapest method that works: retrieval over your documents first, then fine-tuning or small domain models where accuracy, latency or cost demand it. Every model is evaluated against test sets your experts write, and deployed where your data rules require, including private cloud or on-premises.

Why now
7% of global annual turnover: the maximum EU AI Act fine. EU AI Act (Regulation 2024/1689) (opens in a new tab)
Last reviewed

How it runs

  1. 01

    Diagnose

    Typically 2–4 weeks

    We map the problem, your data and your systems, and agree the one number that defines success.

  2. 02

    Prove

    Typically 4–8 weeks

    A working pilot on your real data, measured against that number. Not a slide demo.

  3. 03

    Ship

    Scoped to the outcome

    Production build with security, monitoring, cost controls and documentation included, not upsold.

  4. 04

    Run

    Ongoing, optional

    We operate what we built against clear service levels, or train your team to. Your call. No lock-in.

Questions you’ll ask

Should we fine-tune a model or use RAG?
Start with retrieval-augmented generation in most cases: it's cheaper, easier to update and cites its sources. Fine-tuning earns its place for consistent style, specialized formats, or when a smaller model must match a larger one's accuracy at lower cost.
Can we run models without sending data to a public API?
Yes. Open-weight models can run in your private cloud or on-premises, and major providers offer private deployments in specific regions. We choose based on your data classification, latency and cost requirements.
How do you prove a domain model is accurate?
With evaluation sets written by your own experts, covering real questions, edge cases and things the model must refuse. Every model version is scored against them before release and monitored after.

Sound familiar? Let’s fix it.

One call with a senior engineer. You’ll leave with a straight answer on what it would take.

Let's Build Together

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