LLM Fine-Tuning
“A model that knows your business by name.”
We fine-tune open-source and commercial LLMs on your proprietary data. The result: a model that knows your products, writes in your brand voice, understands your domain, and doesn't hallucinate about things outside it.
We handle data prep, training, evaluation, and deployment.
Start with a focused first build.
Send the workflow, the tools involved, and where the handoff breaks. We will map the smallest build that can prove value before you commit to a larger system.
The useful parts of this build.
These are the pieces buyers usually need when the workflow has to run inside a real product, CRM, dashboard, or internal operation.
Training examples cleaned into a format the model can learn from
Model behavior tuned for repeated classification, extraction, or writing tasks
Before-and-after evaluation so improvement is measurable
Private API endpoint with versioning, rollback, and usage tracking
Improvement loop based on reviewed misses, not noisy raw logs
How this moves from audit to production.
The first version stays narrow enough to ship, but includes the architecture, integrations, model layer, review path, and observability needed by a real team.
Collect domain examples, failed prompts, desired outputs, style constraints, and safety exclusions into a training inventory.
Clean and format datasets for instruction tuning, classification, extraction, or tone adaptation with train-validation splits.
Run baseline evaluations against hosted and local candidate models before committing to a fine-tuning path.
Deploy the tuned model behind an API endpoint with versioning, rollback, cost tracking, and regression tests.
Review production misses and add curated examples to improvement cycles instead of retraining on noisy raw logs.
Questions before building this workflow.
When is fine-tuning better than prompt engineering?
Fine-tuning helps when the model must follow a repeated output style, classify niche examples, extract domain-specific fields, or behave consistently across high-volume tasks.
Can you fine-tune open-source models locally?
Yes. We can prepare datasets for Mistral, Llama, and similar models, then evaluate local or private deployments when data control is more important than hosted convenience.
How do you know the tuned model improved?
We compare it against a baseline using held-out examples, task-specific scoring, regression prompts, latency, cost, and human review on edge cases.
Related services in this category.
Send one workflow.
Send the workflow. We will show what to build first.