LLM development & fine-tuning

Usually GPT-class models with good engineering are enough. The audit says which side of the line your task sits on.

Often only hundreds of good examples, which beat thousands of poor ones for fine-tuning. The first pass over your examples tells us how many yours are.

Yours. Tuned weights on open-weight bases are handed over like any other build, with the documentation to run them. The engagement covers access, handover and who runs it. The base model keeps its own licence.

Against humans doing the same task. We measure the baseline before any tuning starts, and the model has to beat it before rollout.

Not silently. Retraining is a deliberate, evaluated step: accuracy is tracked in production and tuning cycles are scheduled, not automatic.

A custom large language model is one adapted to your data, terminology, and workflows rather than the general internet. You build one when a general model keeps getting your domain wrong, or when your data cannot leave your environment. OpenKit scopes custom LLM development around a specific business problem: reading your documents, drafting in your house style, or answering with your policies.

A general assistant is trained on public data and, on the public tiers, your prompts may leave your control. A custom LLM is adapted to your terminology and runs where you choose: on your own infrastructure the data never leaves it, and where a build uses a hosted provider we contract on terms that keep your prompts out of anyone’s training data. For narrow, repeated tasks a tuned model is more accurate and more private; for open-ended work, a general model is often the right tool, and we will say so.

Almost always we adapt an existing open-weight model rather than train from scratch. Training a frontier model from zero costs millions and rarely beats fine-tuning or grounding an open model on your data. OpenKit fine-tunes, prompts and grounds open-weight models so your team can run and update the result under the arrangements we agree.

The strongest cases are document-heavy and language-heavy: extracting fields from contracts, classifying and routing correspondence, drafting first versions of routine documents, and answering questions from your own knowledge base. It understands your context, which is where general tools fall down, and where the BAiSICS legal platform, for example, surpassed general models on the firm’s own leases.

Client data is used only to deliver the engagement. Prompts and outputs are not retained afterwards and are never used to train external models. The model itself runs where your data already lives, in a UK-region cloud or on your own servers.

It can be, when built for it. OpenKit is ISO 27001 and ISO 9001 certified and holds Cyber Essentials. We add role-based access, data-residency controls and an audit trail of every prompt and response. We design to operate under UK GDPR.

Published UK market ranges put fine-tuning and adaptation work at roughly £20,000 to £80,000, with a knowledge or retrieval system from around £15,000 depending on how clean the data is (OpenKit AI development cost guide). Regulated environments add ten to twenty percent. OpenKit scopes each build against a defined outcome rather than a day rate, and our own fees are on the pricing page.

Yes. An open-weight model can run inside your own controlled environment, on hardware you own or in a private cloud, so prompts and documents stay on your network. That is the pattern OpenKit specified for an International Oil and Gas Service Provider. It takes more setting up than a hosted deployment, so we recommend it where the sensitivity or the volume justifies it and a UK region where they do not.