Custom Model Fine-Tuning
A general model knows a little about everything. A fine-tuned model knows your products, your terminology and your format. We curate the data, train efficiently, and prove the gain with evaluations built from your real work.
Why teams bring us in
Fine-tuning is often reached for too early or skipped when it would help most. We start by testing prompting and retrieval, then fine-tune only where it measurably improves accuracy, speed or cost, and a smaller tuned model can often replace a larger general one.
Every fine-tuning project starts with an evaluation set built from real examples of the task, scored the way your experts would score them. We then establish baselines with prompting and retrieval alone. Fine-tuning only proceeds if the baseline leaves a gap worth closing.
- Data curation: de-duplicated, labeled and PII-scrubbed training sets from your sources
- Baseline first: prompting and RAG benchmarks so gains are real
- Efficient training: LoRA/QLoRA on right-sized GPUs, in your environment
- Evaluation: task-specific scoring plus safety and refusal checks
- Distillation: smaller, cheaper models that keep the quality you need
Teams that get the most from this service
Teams with specialist language
Legal, clinical, engineering or financial text where general models miss terminology and format.
High-volume workflows
Classification, extraction or drafting at scale, where a smaller tuned model cuts cost sharply.
Product companies
You want model behaviour that competitors cannot copy by writing the same prompt.
Four phases, each with a sign-off.
Training runs happen inside your environment on right-sized GPUs, using parameter-efficient methods such as LoRA wherever they meet the quality bar. Every run is tracked with its data version, configuration and results, so you can reproduce it later or explain it to an auditor. When a smaller tuned model matches a larger general one, we recommend it, because it is cheaper and faster to serve.
| Phase | What happens | You receive |
|---|---|---|
| Scope | Target task, success metric, data audit | Eval spec + data plan |
| Prepare | Clean, label, split, redact | Versioned training dataset |
| Train | Iterative runs with tracked experiments | Candidate models |
| Prove | Head-to-head vs. baseline | Eval report + deployable weights |
What you keep when we're done.
Tuned model weights
Yours to keep and run anywhere, with a model card describing training data and limits.
Training dataset
Versioned, documented and reusable for future rounds.
Evaluation report
Side-by-side results against the baseline on your own tasks.
Tools we use
Chosen per client. We work with what your team already runs wherever it makes sense.
Questions about Custom Model Fine-Tuning
How much data do we need?
For many tasks, a few hundred to a few thousand high-quality examples are enough with parameter-efficient methods. We help you build or augment the dataset.
Can you fine-tune on sensitive data?
Yes, inside your environment. Data is PII-scrubbed where appropriate and never leaves your infrastructure.
Who owns the resulting model?
You do. Weights, datasets, training code and the model card are delivered to you.
What if fine-tuning doesn't help?
Then we say so, and you keep the evaluation set and baselines, which are valuable on their own.
Often paired with
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Explore service →SVC-02GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Explore service →SVC-03Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Explore service →Talk to an engineer about Custom Model Fine-Tuning.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.