SOLUTIONS / KNOWLEDGE ASSISTANTS
Make your organisation’s knowledge easier to use.
Turn approved documents and records into a focused search and answer experience. Help people find relevant evidence, inspect the source and prepare useful work without starting every task from scratch.
Company-specific AI, from focused pilot to practical delivery.
FICTIONAL COMPANY / NORTHLINE
A question. An answer.
The source behind it.
Include the supplier name, the service needed and the budget owner. Attach the proposed start date before submitting for review.
“Supplier requests must include the supplier name, service description, budget owner and proposed start date.”Fictional procurement guide, section 2
Scripted example using synthetic material. No live model is connected; important outputs need review.
When this is useful
Your team works across a large document set. Answers need to be traced back to evidence. The same questions recur. Existing search returns files, but not the passage or context people need.
What we build
A system designed around the sources and questions that matter: ingestion of agreed material, retrieval, an answer or summary interface, source references and a clear response when the available evidence is insufficient. Access rules, hosting and record retention are defined for the project rather than assumed.
What the first pilot includes
One defined use case, a bounded set of sources, representative questions and an agreed evaluation. We test both useful answers and failure cases, including questions the system should not answer. You receive a demonstration, findings and a recommendation for what comes next.
The evidence behind the answer
Source links help users inspect the basis of an answer. They do not remove the need to check important outputs. We design the interface and evaluation around that reality.
A few useful questions.
Is it trained on our company data?
It is tailored to your use case, but the technical method matters. Retrieval lets it consult approved information when answering. Fine-tuning changes model behaviour using a reviewed training set. We choose the method for the task and agree data use before doing either.
Where will the data run?
Hosting and model-provider choices are agreed against the data, performance and operating requirements. We do not assume every project should use the same deployment.
Start with the questions your team already asks.
Tell us about the information, workflow and outcome you have in mind.