LLM Applications
Use language models for specific tasks such as summarisation, document assistance and structured information extraction.
Build assistants, copilots and language model applications that help people find information, draft work and use approved business knowledge.

A language model application needs more than a chat window. It needs reliable source material, permission controls and a way to test whether its answers are supported. We help define the use case, connect the right information and build a review process around the situations where the system may be wrong.
Discuss your requirementThe scope is shaped around your systems, your information and the work you need to do.
Use language models for specific tasks such as summarisation, document assistance and structured information extraction.
Give colleagues or customers a conversational way to reach approved information with clear escalation routes.
Retrieve relevant source material before generating a response, with attention to document quality, citations and access.
Help people search policies, manuals and other maintained reference material without losing the source context.
Place drafting, search or analysis assistance inside a workflow while keeping the user responsible for review.
Define a focused assistant around your terminology, tools and working practices rather than a generic chat experience.
Examples of suitable use cases, to help you think through your own requirements and decide whether there is a worthwhile starting point.
Find guidance across approved documents and point the reader back to the source.
Prepare a response using current product information and route exceptions to a person.
Draft summaries or extract fields for review before the information is used elsewhere.
Deliverables are agreed before work begins. A typical engagement may include the following, adjusted to the scope and complexity of your project:
Choose a narrow audience and task, then identify the documents that should inform an answer. Include their owners, versions and access restrictions. Better source material is often the first useful improvement.
Talk through your ideaRetrieval-augmented generation retrieves relevant information from a source collection and provides it to a language model when it answers a question. It can improve grounding, but still needs evaluation and appropriate access controls.
No. Retrieval can miss relevant information and a model can still generate an unsupported answer. We test source retrieval and response quality separately, including questions the assistant should decline.
Yes, when the hosting, provider terms, data handling and permissions meet the agreed requirements. Private source material should not become visible to people who are not authorised to read it.
Build AI applications around a clear business task, with the right data, practical controls and a useful place in your existing workflow.
Explore serviceConnect AI agents, business tools and approval steps to handle multi-step work within clear operational boundaries.
Explore serviceUnderstand where your applications, networks and working practices are exposed, then turn the findings into a clear plan of action.
Explore serviceTell us what is slowing you down, or what you want to do next. A short description of your business question is all it takes to begin.