Fine-tuning & Custom Models
Your knowledge deserves its own model
Efficient fine-tuning (LoRA/PEFT), distillation and on-premise deployment: models that are more accurate in your domain, cheaper per query, and never send your data outside.
Efficient Fine-tuning (LoRA/PEFT)
We adapt open models — Llama, Mistral, Granite — with your data using efficient tuning techniques that don't require GPU farms. The model learns your format, your tone and your business rules.
Model Distillation
We train a small model on the outputs of a large one: dramatically lower latency and cost per query, with comparable quality within your domain. Ideal for high volume.
Training Data & Synthetic Data
Half of fine-tuning success lives in the data. We curate your history, clean it, and generate quality synthetic data to cover the rare cases your operation hasn't documented yet.
On-Premise Deployment & Optimized Inference
vLLM, quantization and batching on OpenShift AI, or watsonx.ai with Granite models: the model runs on your infrastructure with token costs under control.
🔗 Related services: Fine-tuning powers AI Agents when prompting hits its limits, and AI Evaluation decides with evidence whether your case warrants it. Harness Engineering takes it to production.
Process
How We Work
We Listen
Your context and objectives.
We Design
Clear scope and costs.
We Execute
Short sprints, frequent demos.
We Support
Continuous support and evolution.
FAQ
Frequently Asked Questions
Fine-tuning or RAG?
They're complementary, not rivals. RAG gives the model up-to-date knowledge from your documents; fine-tuning teaches it format, tone and behavior. Our AI Evaluation decides with data which combination fits your case.
How much data is needed?
With modern efficient tuning techniques, from hundreds to a few thousand quality examples. We help you build and clean that set, complementing it with synthetic data when needed.
What does it cost versus using an API?
Fine-tuning is an upfront investment; afterwards, cost per query drops dramatically — especially with distillation and small models served on your own infrastructure.
Where does the model run?
On-premise or private cloud: Ollama and vLLM on OpenShift AI for open stacks, or watsonx.ai with Granite models for governed enterprise environments.