Get real, reliable value out of large language models — grounded in your own knowledge, tuned to your use case, and kept accurate, fast and affordable. We make GPT-class models actually work for your business, not just demo well.
Last updated: 11 June 2026
A raw LLM is impressive and unreliable in equal measure — it makes things up, drifts off-topic, and the bills add up fast. LLM optimisation is the work that turns that raw capability into something dependable: grounded in your own documents, constrained to your domain, evaluated for accuracy, and tuned for cost and speed.
Whether you're building a customer-facing assistant, an internal knowledge tool or a content workflow, we make the model behave — and prove that it does.
Connect the model to your own documents so answers are based on your truth — with sources.
Carefully engineered prompts and guardrails that keep the model on-task and on-brand.
Tune a model on your own data for the cases where prompting alone isn't enough.
Automated testing for accuracy, hallucination and safety, before and after launch.
Smaller models, caching and routing to cut spend and speed up responses.
Chatbots, copilots and agents wired into your tools and your data.
We pin down the use case, the knowledge sources and what "correct" means.
We connect your data with RAG and shape the prompts and guardrails.
We test for accuracy, hallucination, cost and speed against real questions.
We tune, cut cost and harden it for production — then keep watching.
For general questions, do. For your business, a raw model doesn't know your data and will confidently get things wrong. Grounding and guardrails are what make it reliable enough to put in front of customers.
Usually RAG (grounding in your documents) first — it's cheaper, faster to update and more transparent. We fine-tune when the use case genuinely needs it.
We ground answers in your sources, add guardrails, and run automated evaluations so we can measure and reduce wrong answers rather than just hope.
Whatever fits — OpenAI, Anthropic, open-source models like Llama, or a mix — chosen for accuracy, cost, privacy and where the data is allowed to go.
Yes. Where data can't leave your environment, we can use private or self-hosted models so nothing sensitive goes to a third party.
Only if you choose to use a cloud API. We can build entirely on open-weights models running in your own infrastructure if data must stay on-premises. We recommend the architecture that fits your security and cost requirements.
Through source citation in every answer, multi-step output validation, task-specific fine-tuning where needed, and automated evaluation suites we run before and after deployment.
We classify each LLM application against EU AI Act risk tiers at scoping. Most business-assistant use cases are low or limited risk — we document the basis, implement the required transparency notices and stay current as the regulation evolves.
Crux Digits is a boutique applied-AI firm based in the Utrecht region, working with clients across the Netherlands and the EU. Because we are a small senior team, the engineers who scope your LLM work are the ones who build it, with no offshore hand-off. If you want a hands-on Dutch or EU partner to make a model accurate and grounded rather than a large consultancy, we are worth a conversation.
We start with a short paid discovery to scope the use case, your data and the risks, then quote a fixed price so there are no surprises. Senior engineers do the build, you own the source code and IP, and we work EU AI Act and GDPR aware by default. A proof of concept can start from a few thousand euros; larger production builds are scoped after that first step.
Tell us what you want it to do — we'll show you how to make it accurate and affordable, in a free consultation.
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