Intelligence
Custom Model Fine Tuning
Off the shelf models are a starting point. Fine tuning makes them yours. AI that speaks your language, understands your domain, and performs like it was built inside your company.
Why generic models are not enough
Foundation models like GPT and Claude are remarkably capable out of the box. But they are generalists. They do not know your product names, your internal processes, your compliance requirements, or the specific way your industry communicates. Fine tuning bridges that gap. It takes a world class language model and makes it an expert in your world.
The fine tuning process
We start by understanding what you need the model to do differently. Then we audit your existing data to find the best training examples. We clean, format, and curate that data into high quality training pairs. We set up the training pipeline with proper experiment tracking. We train, evaluate, iterate, and train again until performance meets your benchmarks. Then we deploy to production with monitoring that catches drift before it affects your users.
When to fine tune vs prompt engineer
Prompt engineering is free and fast. Fine tuning is an investment. Use prompt engineering when clear instructions plus a few examples get you 90% of the way. Fine tune when you need that last 10% of performance, when you need consistent outputs at scale, when long system prompts are costing you money, or when the model needs knowledge it was not trained on.
Models we work with
We fine tune Meta Llama for its flexibility and open licensing, Mistral for its exceptional performance relative to size, Google Gemma for specialized tasks, OpenAI GPT models via their fine tuning API, and various domain specific open source models. The right choice depends on your licensing needs, deployment constraints, and performance requirements.
Make AI speak your language
Talk to our ML team about fine tuning a model for your domain.
Get StartedFrequently asked questions
What is model fine tuning?
Model fine tuning is the process of taking a pre trained foundation model like GPT, Claude, Llama, or Mistral and training it further on your specific data to improve its performance for your use case. The base model already understands language. Fine tuning teaches it your terminology, your domain knowledge, your style, and your specific task requirements. The result is an AI model that performs dramatically better on your work than a generic model.
When should you fine tune vs use prompt engineering?
Use prompt engineering when your task can be accomplished with clear instructions and a few examples. Fine tune when you need consistent output format across thousands of requests, when the model needs to understand proprietary terminology or domain knowledge, when you want to reduce token usage and costs by eliminating long system prompts, or when you need performance that prompt engineering cannot achieve. Fine tuning is an investment. Prompt engineering is free but has limits.
What data do you need for fine tuning?
Effective fine tuning requires high quality examples of the input output pairs you want the model to produce. For most use cases, 500 to 5000 curated examples are sufficient. The quality of examples matters far more than quantity. We help organizations audit their existing data, identify the best training examples, clean and format them, and build data pipelines for continuous improvement. Common sources include customer support transcripts, internal documents, product descriptions, and domain specific content.
How much does model fine tuning cost?
Fine tuning costs include data preparation (the most labor intensive part), compute for training (GPU hours), evaluation and testing, and deployment infrastructure. The total varies based on model size, data volume, and complexity. A focused fine tuning project on a smaller model like Llama or Mistral costs less than fine tuning larger models. Realm by Rook provides detailed cost estimates for every fine tuning engagement and optimizes for the best performance per dollar.
Which models can be fine tuned?
In 2026, the most commonly fine tuned models include Meta Llama (open source, flexible licensing), Mistral (excellent performance to size ratio), Google Gemma (strong for specific tasks), OpenAI GPT models (via their fine tuning API), and various specialized open source models. The choice depends on your licensing requirements, deployment constraints, performance needs, and budget. We evaluate multiple options for every client and recommend the best fit.
Who provides custom model fine tuning services?
Realm by Rook fine tunes foundation models for enterprise businesses. Our process covers data curation and preparation, training pipeline setup with experiment tracking, evaluation benchmarking against your specific metrics, production deployment with monitoring, and ongoing model improvement. We have fine tuned models for customer support, legal document analysis, medical record processing, financial reporting, and technical documentation. We operate across the United Kingdom, United Arab Emirates, and India.