Custom LLM & Model Fine-Tuning
Your data, your model — fine-tuned and evaluated for your domain, not a thin wrapper on someone else's API. We handle data prep, training, evaluation, and deployment.
What this is
Sometimes a generic model on someone else's API isn't enough — you need a model that speaks your domain, your tone, and your edge cases. We handle the full path: curating and cleaning training data from your own sources, fine-tuning for your tasks, evaluating rigorously against real examples, and deploying with versioning and rollback. And because fine-tuning is often the wrong tool, we'll tell you honestly when retrieval or prompting wins instead.
What this includes
- —Training-data curation and cleaning from your own sources
- —Fine-tuning tuned to your tasks and tone
- —Rigorous evaluation against real examples before launch
- —Deployment with versioning and rollback
How we approach it
- 1
Check it's the right call
We first test whether retrieval or prompting solves it — fine-tuning only happens when it genuinely earns its cost.
- 2
Curate the data
We clean and structure training data from your own sources, because model quality starts with data quality.
- 3
Fine-tune and evaluate
We tune for your tasks and tone, then evaluate against real examples before anything ships.
- 4
Deploy with safety nets
Versioning and rollback mean you can ship confidently and revert instantly if needed.
What makes us different
- ✓Honest about when you don't need fine-tuning at all
- ✓Rigorous evaluation against real examples before launch
- ✓Your data and your model — not a wrapper on a generic API
- ✓Versioned deployment with rollback built in
What you walk away with
- →A model tuned to your domain, tasks, and tone
- →Measurable quality gains over a generic model
- →Confident deployment with rollback safety
- →Clear evidence it was the right investment
Common questions
Do we actually need a fine-tuned model?
Often not — we'll tell you honestly when retrieval or prompting wins, and only fine-tune when it earns its cost.
Get your custom llm & model fine-tuning estimate
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Insights
More on custom llm & model fine-tuning
Do you actually need a fine-tuned model? An honest test
Most teams that think they need fine-tuning need retrieval. How to tell.
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What fine-tuning a custom model costs in 2026
The cost is in data prep and evaluation, not the training run itself.
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Fine-tuning vs. retrieval (RAG): how to actually decide
Fine-tuning teaches behavior; retrieval supplies facts. Most systems use both.
Read →Ready to start your custom llm & model fine-tuning?
Two minutes, a real estimate, and a straight answer on whether it's worth doing.