ServicesI — AI Architecture and Model Engineering
General-purpose models fall short against the jargon of specific sectors (law, medicine, finance, manufacturing and so on) or against company-specific business logic, and running general models in production brings high API and hardware costs.
We train advanced open-source foundation models (Llama, Mistral and others) on your own data using parameter-efficient fine-tuning (PEFT) techniques such as LoRA and QLoRA. We apply model distillation to transfer the capabilities of large models to smaller, faster ones. To improve hardware efficiency we quantise models (compression to INT8/INT4) and configure them on dedicated inference servers (vLLM, TensorRT-LLM).
A customised model that speaks the organisation's own language and follows its own business logic, running on your hardware without depending on an external API. How much was gained is shown by comparing before and after on the same evaluation set — the number comes from the measurement at the end, not from us at the start.
This service applies when what is missing is behaviour, not knowledge: the model does not recognise your terminology, answers the same question differently each time, or does not follow the logic of the work itself. Two things have to be on your side: a set of real question-and-answer examples taken from actual operations, and the time of someone who can judge whether an answer is correct. Without both, training has nothing to learn from.
The first step is not to train: if the gap is knowledge, retrieval is cheaper and faster, and we say so. If training is the answer, the example set is split in two — training and evaluation — and the evaluation half never enters training. The current model is measured before training begins; a claim of "better" cannot be checked without a starting point. Then LoRA/QLoRA training, then the same comparison. Production only if the comparison favours it.
Training is not a knowledge source: a model does not reliably memorise new facts, which is what Enterprise RAG Architecture is for. Nor does it repair errors in your data — a model trained on wrong examples learns the error. We do not train on data you have no right to share or use. If the example set is too thin we say so and stop; training on insufficient data is a cost whose failure only becomes visible months later.