Fine-tuning & Evaluation
Custom fine-tuning of open-weight models on your domain data. Includes data prep, training, evaluation harness, and continuous-improvement workflows.
What it is
Fine-tuning adapts an open-weight base model (Llama, Mistral, Qwen, or similar) to your specific domain. Done right, it improves quality, reduces cost, and creates intellectual property you own — the fine-tuned weights are yours, deployed in your environment.
When you'd use it
- When prompt engineering and retrieval are not enough to hit your accuracy bar
- When you have domain-specific patterns the base model doesn't capture
- When you need consistent style or output structure that's hard to prompt for
- When you want to reduce inference cost by using a smaller, fine-tuned model instead of a large general-purpose one
Technical depth
- Data preparation, deduplication, and synthetic-data augmentation
- LoRA, full-parameter, and continued-pretraining options
- Evaluation harness: domain-specific eval datasets and metrics
- Regression testing against quality bars before deployment
- Continuous improvement: feedback loop from production data into training
Why this matters
In regulated industries, fine-tuned models are a strategic asset. They live inside your environment, they capture your accumulated domain expertise, and they're not subject to silent model-provider changes. The IP is yours.
Where it ships.
Get started
Ready to ship this inside your environment?
Bring your use case to a 30-minute discovery call. We'll tell you whether this technology fits and how it gets deployed.