A model trained on your domain beats a bigger model guessing.
General models plateau on specialized work. Post-training on a smaller, targeted dataset makes a model measurably better at your vertical task: your taxonomy, your tone, your edge cases. We handle data prep, training, evaluation, and deployment. The dataset, the weights, and the evaluation harness are yours.
General models plateau on specialized work.
You have written the ten-page prompt. You have stuffed the context window with examples. The model still misclassifies your edge cases, drifts off your taxonomy, and writes in a tone that is almost, but not quite, yours. Past a certain point no amount of prompting closes the gap, and paying frontier-model prices on every call does not either.
Fine-tuning closes it differently: train the behavior in instead of describing it every time. A smaller model post-trained on a few thousand of your best examples gets your task right more often, runs faster, and costs a fraction per call. The work is mostly data and evaluation, both of which become durable assets on your side of the table rather than a vendor's.
Fine-tuning end to end.
From use-case selection through deployment and retraining. You get the model, the dataset, the evaluation harness, and the numbers that justify all three.
The full training pipeline
We pick the use case and base model with you, curate the dataset from your real examples, run the training, and measure the result against the untuned baseline on your actual tasks. If tuning does not beat the baseline you will know before it ships, not after.
Scope your use case →- Use-case and base-model selection
- Dataset curation and preparation
- Fine-tuning runs
- Evaluation harness with before and after benchmarks
- Deployment and monitoring
- Retraining cadence
Evaluation before enthusiasm
The evaluation harness comes first. We benchmark the base model on your task, tune, and benchmark again using the same tasks and the same scoring. Every claim about improvement is a number you can re-run yourself, and the harness keeps working long after we leave.
Ask about evaluation →Deployment and retraining built in
A tuned model that never retrains slowly falls behind your data. We deploy with monitoring, define what triggers a retrain, and set the cadence, so the model keeps up as your taxonomy, products, and edge cases change.
Ask about operations →If this sounds familiar, this is for you.
Fine-tuning pays off when the task is specialized, repeatable, and running at volume.
Where this fits in the stack.
Fine-tuning works best on clean data with a governed route to production. These services cover both.
Stop prompting around the problem. Train it out.
A scoping call takes 30 minutes. You leave knowing whether your task is a fine-tuning candidate and what the benchmark would look like.
