AI & LLM
We turn a general-purpose model into one that knows your business. We build the whole LLM training pipeline — from data collection and cleaning through choosing the right training method, evaluation and production rollout — on your data, on your hardware or in the cloud.
Every weight is updated — the highest-fidelity option. Your domain language, terminology and output format become part of the model itself. It needs a large dataset and a real GPU budget, and we help you decide when it is genuinely worth it.
Parameter-efficient fine-tuning: we train small adapter layers alongside the frozen model. Cost and turnaround are a fraction of full training, it fits on a single GPU, and you can serve several adapters over one base model. For most cases this is the right place to start.
We make the model follow your rules, tone of voice and procedures. We build instruction-response sets and use preference optimisation — DPO, ORPO — to teach the model which answers are good and which are not.
For legal, healthcare, finance or manufacturing domains we run continued pretraining over your document archive to raise the model's raw domain knowledge. Language adaptation for lower-resource languages such as Turkish happens at this stage too.
Training quality comes from data. We collect, clean and de-duplicate your raw records, mask personal data, augment with synthetic examples and split into train/validation/test. Provenance and licence tracking are documented throughout.
"It feels better" is not a result. We build an evaluation set specific to your work and compare the base and trained models on the same metrics: accuracy, format compliance, hallucination rate, latency and token cost.
We make the trained model production-ready: GGUF/AWQ/GPTQ quantisation cuts memory requirements, and distilling a large model's behaviour into a smaller one brings down both cost and latency substantially.
Every training run is tracked and reproducible: experiment logs, hyper-parameter records, model cards, adapter versions and a rollback plan. Updating a model becomes a release, not a surprise.
01
First we check whether fine-tuning is actually needed. Often prompt engineering or RAG is the cheaper, faster answer; when training is warranted, we pick between LoRA, full fine-tuning and continued pretraining.
02
We gather and clean your sources, mask personal data and convert everything into training format. The evaluation set is separated here and never used in training.
03
We run several configurations in parallel, tuning learning rate, adapter rank, epochs and data mix against measurements. Every run is logged.
04
We deliver a comparison against the base model: where it improved, by how much, and whether anything regressed. You sign off.
05
We quantise the model, deploy it to a serving stack and wire up monitoring and rollback. The model card, training scripts and documentation are yours.
In short: RAG adds knowledge, fine-tuning teaches behaviour. If the model needs current facts from your documents, that is RAG. If it needs to apply your tone, format and decision logic consistently, that is fine-tuning. In practice most serious systems use both, and we usually recommend starting with RAG.
For a narrow, well-defined task with LoRA, a few hundred to a few thousand high-quality examples is often enough. Broad domain adaptation or continued pretraining needs raw text in the millions of tokens. We look at what you have and give you a realistic number in the first call.
It does not have to. Training can run on your own servers, inside your own cloud account, or on an isolated rented GPU. We document exactly which data is processed where and design the setup around KVKK and GDPR requirements.
The training outputs, adapter weights, datasets and scripts are yours. The only constraint is the base model's own licence — we review the terms of families such as Llama and Qwen together at the start of the project.
Models that run on your own servers and keep data in-house
Answers grounded in your own documents, with citations
Where to start, what will pay off, and what will not
In a short call we listen, then tell you plainly which approaches fit your situation and which do not. We reply within 24 hours.