Smarter AI. Real business impact.

A chatbot answers questions in a conversation. An assistant also helps with tasks such as drafting or finding information. An agent can use tools, for example to look up a record or create a task, within limits you set. Each step up adds capability and risk, so we usually start with answering before adding actions.

Retrieval-augmented generation (RAG) means the system first finds relevant passages in your approved documents, then a language model writes an answer from them. It lets the assistant answer from your material and point to its sources instead of relying only on what the model learned in training. It reduces wrong answers but does not remove them, which is why testing matters.

Yes. Any language model can produce a fluent answer that is wrong or unsupported. Grounding in your sources, showing citations, testing with awkward questions and a clear handover to a person all reduce the risk. None of them removes it, and we will not promise otherwise.

It depends on the design. Some setups send text to a hosted model provider, while others can keep more on infrastructure you control. We map the data path and agree what the system may see before building, and an assistant should only answer from what the person asking is allowed to read.

One narrow job, a set of approved documents and a person who owns them. If you are not sure where to begin, an Advisory conversation can help you choose the first job.