AI & LLM systems
Make company knowledge easier to find and use.
Knowledge scattered across documents and systems makes reliable answers difficult. We scope assistants around approved sources, user permissions and the questions your team actually asks.
Discuss your requirements ↗- Knowledge retrieval with source references
- Model selection and task-specific evaluation
- Human approval for consequential actions
Representative questions, approved source material and access rules.
HOW WE APPROACH DELIVERYAgree an evaluation set before implementation. Review answer quality, unsupported answers, response time and operating cost.
Scope, duration and a fixed quote are agreed before each phase. Delivery depends on the requirements and access confirmed during scoping.
A service team needs the right answer
A colleague asks which approval is needed for a customer request. The assistant finds the relevant policy, shows its source and flags missing information. It hands an uncertain answer to the owner rather than inventing a rule.
Before we begin
Can we use our existing documents and tools?
Yes, the starting point is the information you already maintain. We review its format, quality, permissions and update process before choosing how to connect it.
How do we know whether the answers are useful?
Agree a set of real questions and expected evidence. Test correct answers, missing information, restricted records and questions the system should decline. Review results before widening access.
Do we need to choose an AI model first?
Start with the task, data requirements and operating budget. Model selection follows those constraints; a larger model is not automatically the right choice.