Language models, tuned to your domain.

Fine-tuned and private LLMs, adapted to your data, your domain and your voice, deployed where you need them, on-premise included.

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AI Division Scroll Fine-tune × Adapt × Deploy

Custom LLM development

Custom LLM development is the work of adapting a large language model to your own data, domain and tasks, then deploying it where you need it. In practice it usually means fine-tuning a strong existing model rather than training one from scratch, so it speaks your vocabulary, follows your format and keeps your data in-house. ZenkeiX builds custom LLMs on your data and your stack, for production.

Whether you need a model that writes in your voice, classifies with your taxonomy, returns clean structured output, or runs entirely offline, we handle the whole path: dataset preparation, fine-tuning, evaluation and deployment. When the goal is current, factual knowledge rather than a new skill, we will tell you when RAG is the better fit, and often the two are combined.

What we build

A model that is unmistakably yours, not a generic one.

Fine-tuning & adaptation

LoRA, QLoRA and full fine-tuning on your data, plus instruction tuning so the model follows your tone, format and rules.

Private & self-hosted deployment

Open-weight models running on your own infrastructure or private cloud, quantized to be fast and cost-efficient in production.

Domain & task specialization

Models that speak your industry's vocabulary and return reliable structured output for the specific job you need done.

Evaluation & guardrails

Eval suites, regression tests and safety guardrails, so you can prove the gain over the base model and keep it stable over time.

+60%Time saved on core processes

+86%ROI, measured on client KPIs

100%Private, local deployments available

How we work

01 · Map

Fine-tune, RAG, or both.

One working session on the task and the data behind it. We leave with the right approach and a dataset plan, not a fashionable one.

02 · Build

Trained and proven.

Dataset prepared, model fine-tuned, and evaluated against a held-out set, so the gain over the base model is measured before launch.

03 · Ship & scale

Deployed where you need it.

Served on your infrastructure or private cloud, monitored and refined. Your data and your model stay yours, always.

Frequently asked

What is custom LLM development?

Custom LLM development is adapting a large language model to your own data, domain and tasks, then deploying it where you need it. It usually means fine-tuning an existing model rather than training one from scratch, so it speaks your vocabulary and follows your rules.

Should I fine-tune a model or use RAG?

Fine-tuning changes how a model writes, reasons and follows your format. RAG gives it your current documents at answer time. Fine-tuning fits tone, structure and domain skills; RAG fits factual, changing knowledge. Many production systems use both, and we help you decide: see RAG vs fine-tuning.

Can an LLM run privately or self-hosted?

Yes. We fine-tune and deploy open-weight models on your own infrastructure or private cloud, quantized to run efficiently, so your data and the model stay in-house and nothing is sent to a third-party API.

Which base models can you fine-tune?

Open-weight families such as Llama, Mistral, Qwen and Gemma, using techniques like LoRA and QLoRA for efficient fine-tuning, plus full fine-tuning when a project calls for it.

How much data do I need to fine-tune a model?

Less than most people expect. A few hundred to a few thousand high-quality examples often move the needle, and we help you build, clean and structure that dataset before any training runs.

How do you evaluate a fine-tuned model?

With an evaluation suite built around your tasks: benchmarks, regression tests against a held-out set, and human review, so you can see the gain over the base model before it goes live.