Definition
What is a domain-specific LLM?
A domain-specific language model (DSLM) is trained to be an expert in your field, not a generalist that knows a little about everything.
In short
A domain-specific LLM (DSLM) is a large language model post-trained on a particular organisation's or industry's data, documents, and workflows so it reasons like an expert in that domain, often outperforming much larger general-purpose models on the tasks that domain cares about.
Why specialised beats general
General models are trained to know a little about everything from the public internet. A domain-specific model is trained deeply on your world, your terminology, your precedents, your processes, so it gives answers that are correct in your context, not just plausible in general.
Crucially, specialisation lets a smaller, cheaper-to-run model beat a much larger generalist on your tasks, which makes owning and deploying it inside your perimeter practical.
How Locai builds a DSLM without breaking it
Naive fine-tuning causes catastrophic forgetting: the model gains your domain but loses general reasoning. Locai's Forget-Me-Not™ framework adds deep domain expertise while preserving the base model's general capability, so you get an expert that still reasons well, and can keep learning continually.
What this looks like with Locai
What sovereign AI actually looks like in production is the part most marketing skips, so here is the short version.
Locai Labs believes organisations should own their intelligence. Renting access to a general-purpose model that lives on someone else's servers is fine for low-stakes work; for the AI that touches your data, your customers and your decisions, the model itself should be yours. That is the bet behind everything we build.
It is also a bet that an expert model beats a generalist on the work that actually matters to your business. A smaller model trained on your data, your language, your workflows and your edge cases routinely outperforms much larger generalists on the tasks you care about, and it does so on infrastructure you control. The goal is not the biggest model; the goal is the right model for your business.
And it is deployed sovereignly: an owned model that runs inside your perimeter, on-prem via Locai One, in your private cloud tenant, in a UK sovereign cloud, or fully air-gapped, depending on your residency and security requirements. Your prompts, your documents and your outputs stay inside your environment, under UK jurisdiction, with a data path designed to fit GDPR and the procurement standards regulated organisations are held to.
Frequently asked questions
How is a DSLM different from RAG?
Retrieval (RAG) bolts a search index onto a general model at query time; a DSLM bakes the domain into the model's weights through post-training. The two are complementary, but only post-training changes how the model actually reasons.
What data do I need to build one?
Most structured and unstructured formats work, documents, PDFs, databases, logs, proprietary formats. You don't need clean, labelled data; data preparation is part of the process.
Do I own the resulting model?
Yes. With Locai the domain-specific model, its weights, and the post-training pipeline are yours.
Book a sovereign AI briefing
A 30-minute session on owning your model: deployment options, the data path, and a clear cost range for your use case.