Custom LLM Fine-Tuning
Don't rely on generic AI. We fine-tune open-source Large Language Models exclusively on your proprietary data to perform highly specialized business tasks.
General-purpose models are capable but generic. They do not know your terminology, your document conventions, or the format your downstream systems expect, and prompt engineering can only compensate so far before prompts become unmaintainable and costs rise with every call.
Impact
Teams either accept output that needs constant human correction, or maintain increasingly elaborate prompts that break whenever the underlying model updates. Both consume engineering time indefinitely without the system getting better.
We fine-tune open-weight models on your domain data so the behaviour you need is in the model rather than in a fragile prompt. That usually starts with establishing whether fine-tuning is actually warranted — frequently retrieval or better prompting solves the problem at a fraction of the cost.
Technical Approach
Training uses parameter-efficient methods such as LoRA on open-weight models, which keeps cost and iteration time manageable compared with full fine-tuning. Evaluation is built before training starts, with a held-out set representing real inputs, so improvement is measured rather than assumed. Serving runs on vLLM or a managed equivalent depending on your latency and volume requirements.
Increasingly long prompts attempt to force a general model into domain-specific behaviour, with quality varying between runs and breaking whenever the provider updates the model.
A model that behaves consistently on your domain from a short prompt, with measurable evaluation against real examples and no dependency on a third party's release schedule.
Strict adherence to global data privacy laws. We never train public AI models on your proprietary data.
Architecture designed to meet rigorous healthcare and enterprise security compliance standards natively.
Scalable cloud-native deployments via AWS and Vercel Edge networks ensuring 99.99% uptime.
Everything you need to know about our Custom LLM Fine-Tuning process.
You do. When we fine-tune an open-source model (like Llama 3), the resulting model weights belong entirely to your company. You are not locked into any vendor API.
No. With modern Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA, we can often achieve excellent results with as few as 1,000 to 5,000 highly curated examples.
Frequently not, and it is worth establishing that before spending money. If the problem is the model lacking your information, retrieval-augmented generation is cheaper and easier to keep current. If it is output format or tone, structured prompting often suffices. Fine-tuning genuinely earns its cost when you need consistent domain-specific behaviour that prompting cannot reliably produce, or when per-call costs at volume justify a smaller specialised model.
Less than most teams expect, but quality matters far more than volume. Parameter-efficient fine-tuning can produce meaningful improvement from a few hundred to a few thousand well-constructed examples. A thousand carefully curated examples will outperform ten thousand noisy ones. Most of the real work is in dataset construction, not training.
Yes. Fine-tuning runs on open-weight models in infrastructure you control or in an isolated environment, so training data is not sent to a third-party provider or used to improve anyone else's model. The resulting weights are yours. This is one of the main reasons organisations with sensitive data choose open-weight fine-tuning over hosted alternatives.
Your dataset and evaluation harness are the durable assets, not the trained weights. When a stronger base model appears, re-running training on your existing dataset is comparatively quick. This is precisely why we build evaluation before training — it is what lets you compare a new base model against your current one objectively.
Let's discuss your fine-tuning project.
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