Fine-tuning when the evidence says it is worth it, and not before.
We help you choose between better prompts, retrieval, fine-tuning and training a custom model by testing each on your task. When adaptation pays off, we prepare the data, train, evaluate and deploy the adapted model with a clear view of cost and quality.
- Approach comparison
- Training dataset
- Adapted model
- Evaluation report
- Serving and retraining setup
Where this helps
What we deliver
How it works
- 01
Define the gap
We establish exactly where the current model falls short, using examples your experts have judged.
- 02
Try the cheaper options
Improved prompts, examples and retrieval are tested first. If they close the gap, we stop there.
- 03
Prepare data
When adaptation is justified, we build and review the training set and hold back a clean test set.
- 04
Train and compare
Training runs are tracked and compared, with attention to overfitting and loss of general ability.
- 05
Deploy and watch
The adapted model is released behind the same evaluation gates as any other change and monitored in production.
Design decisions we make with you
Is adaptation necessary?
Fine-tuning teaches style, format and narrow judgment well; it is a poor way to add changing facts, which retrieval handles better.
Hosted or open-weight
Provider fine-tuning is quicker to start; open-weight models give more control over hosting and data. Not every model can be fine-tuned or self-hosted.
Data rights and quality
Training data must be yours to use, representative of real inputs, and reviewed. A small clean set often beats a large noisy one.
Smaller, specialized models
Distilling a large model's behavior into a smaller one can cut latency and cost for high-volume tasks, at the price of generality.
Maintenance burden
An adapted model needs retraining when base models or your data change. We weigh that ongoing cost in the recommendation.
Applications
Related capabilities
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
- RAG & Enterprise Knowledge SystemsAnswers drawn from your own documents and data, with sources shown, permissions respected and content kept current.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- Enterprise & Private AIAI deployed with the data isolation, access control and operational ownership your security and compliance teams require.
Questions buyers ask
They solve different problems. Retrieval gives the model up-to-date facts from your documents; fine-tuning changes how the model behaves. Many systems need retrieval only. See RAG & Enterprise Knowledge Systems.
It depends on the task and how consistent the examples are. Quality matters more than volume, so we train on a subset first and check whether adding more data still improves results before committing.
No. Some providers do not offer fine-tuning for every model, and some licenses restrict it. We check what is possible for the models you are considering.
It can. We test for regressions on related tasks and keep the base model available for work outside the adapted scope.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.