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Off-the-shelf LLMs are general-purpose. We finetune them on your data — your documents, your terminology, your standards — so they perform like a domain expert instead of a generalist. You get a model that gives better answers, makes fewer mistakes, and fits your workflow.
General-purpose LLMs get you 80% of the way but fall short on domain-specific tasks. They hallucinate industry terms, miss your internal conventions, and give generic answers when you need precision. The gap between "impressive demo" and "reliable in production" is finetuning.
We review what training data you have — documents, examples, conversation logs — and define exactly what the finetuned model needs to do well.
We clean, structure, and format your data into high-quality training examples. This is where most finetuning projects succeed or fail — we get it right.
We run finetuning experiments across model sizes and configurations, evaluating against your real-world tasks — not just benchmark scores.
You get a finetuned model ready to deploy — via API, on your infrastructure, or hosted by us. As your data evolves, we retrain to keep it sharp.
Send us a sample dataset. We'll show you what's predictable.
Book a call