Fine-tuning
Adapt open-source or hosted models to your domain, language, tone and tasks.
Service 04 · AI model training
When a general-purpose model isn’t enough, we fine-tune or train models on your own data — and prove the improvement with evaluations you can trust.
What we do
Training is only worth it when it beats the alternatives. We check that first, then make every improvement measurable.
Adapt open-source or hosted models to your domain, language, tone and tasks.
Collect, clean, label and version the data a model learns from, with clear labelling guidelines.
Benchmarks and test sets built from real cases, compared against a baseline before anything ships.
Classification, forecasting and recommendation models where they fit better than a language model.
Run models in your cloud or on your own servers, with monitoring and access control.
Retraining pipelines that keep the model sharp as your data grows.
How it works
You always know what we’re doing, why, and what comes next.
Is training needed at all? We set a baseline with existing models first.
Datasets, labelling guidelines and quality checks.
Iterate against the baseline until the gain is clear and repeatable.
Ship, watch for drift and retrain on a schedule.
Is it a fit?
Since 2013 we have built, launched and run our own products — DNSChecker.org, IPLocation.io and more. Client work gets the same team and the same standards.
See our own productsIt depends on the task. Some fine-tunes work with a few hundred good examples; others need far more. We audit your data first and tell you honestly whether training is worth it.
Yes — in an environment you control, with access limited to the people who need it.
No. Often better prompts or search over your documents do the job for less. We test those first and only train when it clearly wins.
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