LLM Integration & Fine-Tuning

LLM integration means connecting large language models to your software, so it can understand and generate text, call your systems and answer questions. Good integration covers prompts, tools, evaluation, cost control and safety, not only the API call.

It depends on the task, quality needed, speed, price and data rules. We test candidate models on your own examples, and design the system so that the model can be changed later.

A model gateway is a layer between your apps and the model providers. It handles routing, logging, limits, caching and filters in one place, so you can switch or mix models without rewriting applications.

Fine-tuning pays off when you need a consistent style or format, a specialist task, or shorter prompts at high volume. It does not add reliable up-to-date knowledge, which is better handled with retrieval. We test the cheaper options first.

Often a few hundred to a few thousand high-quality examples, depending on the task. Quality and consistency matter more than volume, and we help prepare and review the data.

We build a test set of real examples with agreed good answers, score outputs automatically and with human review, and rerun the tests whenever prompts or models change.

We use caching, route simple jobs to smaller models, shorten prompts, set per-user limits and track cost per task, so the bill never comes as a surprise.

Yes. Open models can run in your cloud account or on your own servers when data rules require it. We compare quality and cost against hosted options first.

We mask or remove sensitive fields before sending, control what is logged, choose providers with suitable data terms and, where needed, keep processing inside your environment.

A first integration with evaluation often takes two to six weeks. Fine-tuning projects add data preparation and training time, commonly one to three months in total.