02I — AI architecture and model engineering

Fine-tuning, LoRA/QLoRA and model distillation

Technical problem

General-purpose models fall short against the jargon of a given sector — law, medicine, finance, manufacturing — or against business logic specific to one company; and running general models in production brings high API and hardware costs.

Architectural solution

We train advanced open-source base models (Llama, Mistral and others) on data specific to your organisation, using parameter-efficient fine-tuning techniques such as LoRA and QLoRA. We apply distillation to carry the capability of large models into smaller, faster ones. To raise hardware efficiency we quantise models down to INT8/INT4 and configure them on dedicated inference servers (vLLM, TensorRT-LLM).

Operational outcome

A customised model infrastructure that depends on no external API, works in complete agreement with in-house language, runs fast, and cuts server and hardware costs by as much as 70%.

AI architecture and model engineering

Enterprise RAG architecture and vector database integrationStandard large language models cannot reach private in-house data, fall out of date, and produce confident answers with no basis in fact when questioned.Metric-driven evaluation and comparative model test infrastructureModel performance is judged by surface-level observation, and once systems are live there is no analytical way to track accuracy and safety rates.

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Which one shall we start with?

See it on your own data in one session, or write first and ask what we do.

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