T Textura

Glossary term

What is an AI secretary?

An AI secretary is a voice AI agent for inbound calls that answers the call, holds a dialogue, qualifies the request and routes it to the right person. Unlike a simple answering machine, it understands speech and acts on a scenario rather than playing back fixed recordings.

An AI secretary works the first line: it answers the call, greets the caller, finds out the reason for the contact, collects the needed data (with configurable fields, required flags and validation) and routes the conversation — hands off to a live agent, to another scenario, or handles the request itself. This relieves people of routine contacts and provides a consistent level of intake at any time of day.

A distinguishing trait of an advanced AI secretary is the ability to stay on the line after handing off to a human. It keeps listening to the conversation and switches into prompter mode: it suggests the next action to the agent, takes notes, fills in records and watches for adherence to the script and standards. This way the secretary helps not instead of the human but alongside them, raising the quality of the live conversation.

For a business, an AI secretary solves the problem of missed and poorly handled calls: no request goes unanswered, intake is standardized, and conversation data lands in the system immediately. This is especially valuable for 24/7 intake, peak load (overflow) and scenarios where consistency and completeness of collected data matter.

Like any voice agent, an AI secretary is linked to speech analytics: its calls are recorded and analyzed, and the assembled knowledge base and quality metrics help improve intake scenarios and the prompts given to agents.

How it works in the platform

In the platform, the AI secretary handles inbound calls on a configured scenario, collects fields with validation and hands off to an agent by conditions, staying on the line as a real-time prompter — with on-screen hints, a summary and the collected fields. All its calls are recorded and go through the standard analysis loop: transcription, metrics, tags, automations.

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