Make your knowledge easier to use.
AI assistants that help customers and teams find answers in approved business information.
Start with the problem.
People lose time searching scattered documents or answering the same questions. An assistant can help with retrieval and drafting, provided its sources, permissions, and limits are clearly defined.
How we approach it
We begin with a contained question set and approved sources. The system is tested against realistic questions, missing information, and misleading inputs. Human review remains part of important decisions. We discuss model providers, data handling, and usage costs before choosing an architecture.
What to bring to the conversation
Identify who needs answers, where the trusted information lives, and what the assistant must never do. Representative questions are more useful than a long feature list. We can first assess whether AI is the right fit at all.
A few useful answers.
How do you reduce inaccurate AI answers?
We use approved sources, access controls, realistic evaluation examples, clear uncertainty, and human escalation. Retrieval can improve the information available to a model, but it does not guarantee a correct answer. Both custom and commercial tools must be tested for the intended workflow.
How is private data handled in an AI assistant?
We review source permissions, provider terms, retention settings, and hosting requirements before agreeing the design. Training exclusions and zero-retention arrangements depend on the provider, endpoint, and contract; they must be confirmed for the actual implementation.
Will every answer be correct?
No AI system can guarantee that. Source grounding, evaluation, clear uncertainty, and human escalation help manage the risk.
Can the assistant use private documents?
Potentially, with suitable access controls and an agreed data-handling setup. We review provider terms and security requirements before implementation.
Can it take actions in other tools?
Where appropriate, actions can be integrated with scoped permissions and confirmation steps. High-impact actions should require review.
How are running costs handled?
We identify likely model, hosting, and retrieval costs during scoping. Actual costs depend on usage and provider pricing.