When it fits.
Fine-tuning can help with recurring requirements such as response format, classification, specialized language, or task-specific behavior. It is usually not the first choice for keeping factual answers current as company documents change.
What we build together.
- A task definition and baseline against which model changes can be measured.
- Dataset review, deduplication, training examples, and separate evaluation data.
- A scoped training experiment using full or parameter-efficient adaptation where appropriate.
- Model comparison, deployment planning, and monitoring for regressions.
Start with the right questions.
We ask for examples of current failures and desired outputs, then assess whether you have the rights and sufficient coverage to use the data for training. We choose a model and training approach based on task quality, hosting constraints, latency, and operating cost.
Engineering for the real world.
Training can introduce regressions or memorize sensitive examples. Data quality and evaluation coverage matter more than simply increasing dataset size. A custom model trained from scratch is a separate investment and needs a clear reason beyond what an adapted existing model can deliver.
Questions before we begin.
Should we use RAG or fine-tuning?
Use retrieval when the main need is access to source knowledge. Consider fine-tuning when the main gap is model behavior on a defined task. We test a baseline before recommending either or both.
How many training examples do we need?
There is no universal minimum that guarantees quality. The task, model, example consistency, and coverage of difficult cases determine what is useful. We assess your dataset before proposing a training run.
Can the model run privately?
Deployment depends on the model license, hardware needs, and your security requirements. We evaluate self-hosted and managed options as part of the agreed scope.
Explore the thinking. See the work.
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What are you looking to solve?
Tell us about the workflow, users, and constraints. We’ll define a practical next step together.