What is Large Language Model (LLM)?

Large language models represent a step change in how businesses can process unstructured text, which makes up the majority of information in regulated industries. Insurance policy documents, legal contracts, compliance filings, and patient records are all predominantly text-based, and LLMs can read, summarise, and extract structured data from these at speeds no human team can match.

For mid-market UK businesses, the practical question is not whether LLMs are powerful but how to deploy them safely. A wealth management firm using an LLM to draft client communications needs confidence that the model will not fabricate regulatory references or misstate product terms. A healthcare provider using one to triage patient queries must ensure sensitive data never leaves a controlled environment.

This is why deployment architecture matters as much as model capability. Running an LLM through a public API means your data passes through third-party infrastructure, which may conflict with GDPR obligations or FCA expectations around data handling. Private deployments, where the model runs inside your own cloud tenancy, give you the control that regulators expect.

The cost of LLMs has fallen dramatically since 2023. Models that once required enterprise-scale budgets are now accessible to firms with fifty employees. The key is matching the right model to the right task. A smaller, fine-tuned model often outperforms a general-purpose giant for domain-specific work like regulatory classification or contract review, while costing a fraction to run.

Most mid-market firms start with LLMs in internal productivity use cases, such as summarising meeting notes, drafting first-pass reports, or searching internal knowledge bases, before moving to client-facing applications once governance frameworks are in place.