AI assistants and conversational applications

An assistant should help people complete a task. We begin with the questions it should answer, the information it can use and the actions it is allowed to take. That scope shapes the conversation design and the integrations behind it.

Use your approved information

The assistant can retrieve information from your product materials, service documentation or internal knowledge base. We configure responses around those sources and test what happens when an answer is unavailable or a request is outside the supported workflow.

For actions such as creating a request or checking a record, we connect the relevant business system and define the required authentication and validation. Sensitive actions can include an approval step.

Make escalation part of the experience

A customer should be able to reach a person when the situation calls for one. We agree on escalation triggers, destination queues and the context passed to the team, so the customer does not have to restart the conversation.

Voice projects also account for recognition errors, interruptions and the practical limits of the connected telephony system. We evaluate the full call flow before expanding its responsibilities.

Review real outcomes

Testing covers task completion, answer quality, unsuccessful requests and handover behavior. Reporting helps your team identify gaps in the knowledge base and improve the workflow over time.

See RAG and enterprise search for the knowledge layer, or managed AI services for support after launch.