Omeiro AI Automation

LLM Fine-Tuning Services for Your Business

LLM fine tuning services adapt an open model to your domain, with your data staying under your control. Omeiro fine-tunes models using LoRA on your examples, evaluates the result and, on higher tiers, deploys it privately.

Sample run

How LLM fine tuning works, step by step

A made-up example of one run, from start to finish. Each step is logged so you can see what happened.

  1. Something happens

    A form, an email, a payment or a new row.

  2. The workflow runs

    Steps you agreed, in the right order.

  3. Errors are caught

    Failed steps retry or alert a person.

  4. You see what ran

    A simple log of every run.

Sample  Made-up names, data and steps to show how a run is logged. Your steps, tools and rules follow your business.

What you get

What you get with LLM fine tuning

Your data stays yours; the model learns your domain without leaking it.

Basic Package

Fine-tune 1 open model (LoRA) on up to 2,000 examples; evaluation report. 7-day delivery, 1 round + 30-day fixes.

Standard Package

Up to 10,000 examples, data cleaning, 2 training runs; private deployment. 14-day delivery, 1 round + 30-day fixes.

Premium Package

Up to 50,000 examples, custom evals, quantised model; 30 days support. 21-day delivery, 1 round + 60-day fixes.

Clear brief first

We confirm what you need before work starts, so the scope is clear on both sides.

Revisions listed

Each package shows its revision rounds. A round is one list of changes sent together.

Yours once paid

Copyright of the final work passes to you once the order is paid in full.

Third-party tools, API usage and hosting are billed to your own accounts, so the setup stays with your business.

How it works

How our LLM fine tuning works

1

Review your data

We check quality, format and gaps in your examples.

2

Prepare the set

Data is cleaned and split for training and testing.

3

Train

The model is fine-tuned and compared with the base model.

4

Deliver

You receive the evaluation and, where included, a private deployment.

Why Omeiro

Why choose Omeiro for LLM fine tuning

You get a model that speaks your terminology without sending training data to a public service.

In short

  • Delivery in 7 to 21 days, by package.
  • Fixed price, shown before you order.
  • Teams whose prompts have become long lists of rules a model still forgets.
  • Copyright yours after full payment.
FAQ

LLM Fine Tuning: common questions

How much does LLM fine tuning cost?

Prices are fixed and shown by package on our LLM Fine Tuning pricing page, so you see the full price before you order. You can also ask for a custom quote.

How much does LLM fine tuning cost?

Prices are fixed and shown by package on our LLM Fine Tuning pricing page, so you see the full price before you order. You can also ask for a custom quote.

How long does custom LLM development take?

Delivery depends on the package: Basic 7-day delivery; Standard 14-day delivery; Premium 21-day delivery.

What is included in the Basic Package?

The Basic Package includes: Fine-tune 1 open model (LoRA) on up to 2,000 examples; evaluation report. Higher packages add more scope, revisions and support.

Can I get a custom fine tune language model for business package?

Yes. If none of the packages fits, use the custom package form on our pricing page to describe what you need, and our team sends you a fixed price.

Should I fine-tune or use RAG?

RAG suits changing facts, while fine-tuning suits style and task skill. Many projects use both.

Does my data leave my control?

Open models can be trained and deployed privately. Tell us your constraints.

How do we check it worked?

Every tier includes evaluation.

Who owns the final work?

Copyright of the final work passes to you once the order is paid in full. Paid fonts, stock files or licences, if used, are licensed in your name.

Ready to Start

Ready to start your LLM fine tuning?

Compare the packages and pick the one that fits. Not sure which? Write to us and we will help you choose.

View Pricing