Service · Custom LLMs

Custom LLM development: a private model that knows your business

We set up large language models that run on your infrastructure, learn from your documents and follow your rules — so your teams get the power of generative AI without sending a single file to a public chatbot.

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01 The challenge

Why public chatbots are not enough

Most organisations have already tried a public AI assistant and hit the same walls. Confidential documents cannot be pasted into a third-party service. Answers sound confident but are not grounded in your policies, contracts or product data. And nobody can say which model version produced which answer when an auditor asks.

A custom LLM fixes this at the root. The model is selected for your tasks, hosted where your data already lives, connected to your knowledge through retrieval-augmented generation (RAG), and wrapped in access controls and logging that your security and compliance teams can sign off.

02 Capabilities

What we build

  • 01

    Private LLM setup

    Benchmarking open-weight models against your real tasks, then deploying the best fit on your servers, private cloud or a fully air-gapped network.

  • 02

    Fine-tuning on company data

    Supervised fine-tuning and preference tuning on approved examples, so the model writes in your terminology, formats and tone.

  • 03

    RAG over your documents

    Secure indexing of wikis, PDFs, tickets, contracts and databases, with every answer citing the source passages it used.

  • 04

    Copilots for specific teams

    Assistants for legal, engineering, operations, customer service or field staff, embedded in the tools they already use.

  • 05

    Evaluation and guardrails

    Task-specific test sets, hallucination checks, PII redaction, topic restrictions and red-team testing before anything goes live.

  • 06

    LLM operations

    Monitoring, cost and latency tuning, model upgrades and retraining as your documents and needs change.

03 How we deliver

From first conversation to systems in production

  1. 01

    Discover

    We map the questions people actually ask, the documents that hold the answers and the data that must never leave your perimeter.

  2. 02

    Prototype

    A working private assistant on a sample of your documents, measured against a test set your experts help write.

  3. 03

    Deploy

    Hardened deployment on-premise, in your VPC or air-gapped, with single sign-on, role-based access and full audit logs.

  4. 04

    Evolve

    Ongoing evaluation, new data sources, better models as they appear, and new teams onboarded one by one.

Every engagement can run in the cloud, on-premise or fully air-gapped. Your data stays under your control, and humans stay in command of every significant decision.

04 Outcomes

What you get

  • A model you own and control, running where your data already is
  • Answers grounded in cited internal sources instead of guesswork
  • Clear governance: who asked what, which model answered, and from which documents
  • No dependency on a single AI vendor — models can be swapped as the field moves
  • A platform that later powers agents, search and automation across the business

→ Two ways to work with us

Enhance what you have — or let us build and run it

Path A

Enhance what you have

Add AI and automation to the systems, products and operations you already run — no rip-and-replace, and your team stays in control.

Start with an enhancement

Path B

Build & manage for you

We design, build and launch it, then host, monitor, secure and improve it — or hand it over to your team.

Have us build it

05 Related

Industries we bring this to

06 FAQ

Custom LLMs: frequently asked questions

Can a custom LLM run fully on-premise or air-gapped?

Yes. We regularly deploy open-weight models on local GPU servers with no internet connection. Retrieval, logging and the user interface all run inside your network.

Do we need to fine-tune, or is RAG enough?

For most knowledge questions, retrieval over your documents is the fastest and most transparent route. Fine-tuning helps when you need a specific style, format or specialist vocabulary. We test both on your data and recommend the simplest option that meets the bar.

Which models do you use?

We are model-agnostic. We benchmark several open-weight and commercial models on your tasks and pick on accuracy, cost, latency and licensing — and we keep the option to switch later.

How do you stop the model making things up?

Grounded answers with citations, refusal rules when sources are missing, automated evaluation sets and human review of edge cases. We report measured quality, not impressions.

Is our data used to train anyone else's model?

No. Your data stays in your environment and is only used for your model, under your control.

Ready for an LLM that is actually yours?

Tell us what you want AI to take over. A senior engineer will get back to you — under NDA if you need it.

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