Industry solution
AI whisper coach for a support agent in a live call
A second-line agent in a live call does several things at once: listens to the customer, pulls up their contact history, searches the knowledge base for a solution and keeps the ticket in parallel. Something suffers — the card is filled in after the call from memory, the order number or promised deadline gets confused, and the right article is found only after the customer has hung up. And an ordinary voice bot doesn't fix this: it takes the call and disconnects at the moment of transfer, so the agent gets a "cold" customer and makes them retell what they already explained to the bot.
The AI receptionist takes the first line, qualifies the inquiry and transfers to an agent — but stays in the call as a whisper coach rather than dropping off. While the dialog runs, the agent sees on screen a summary of the context already collected, the customer's past-inquiry history, the relevant knowledge-base article for the current question, and a next-step prompt, while the captured fields and transcript flow into the ticket themselves. The quality of the conversation stops depending on a particular agent's memory and experience — a system stands behind it.
Which industry pains this solves
Queue and dropped calls
At peak and overnight customers can't get through: the call piles up in the queue, drops off and becomes a missed contact no one ever learns about.
Quality by gut feel
You only get to review 2–3% of conversations. Whether the script held, whether the agent was polite, whether it was solved on first contact — stays out of view.
How it works
A step-by-step scenario — the same steps as in the full use case, applied to your industry.
- 1
The secretary answers and qualifies
The AI secretary answers the inbound call to your scenario, runs the dialogue in the customer's language and collects the needed data into fields with required flags and validation.
- 2
Transfer to the operator — without losing context
Per your transfer rules the agent hands the call to a live operator and stays in the conversation (conference) instead of disconnecting: the context is already gathered.
- 3
The operator sees prompts in real time
On the operator's screen — a context summary, the next action, warnings and the already collected fields. The prompts update as the conversation goes, from the live transcript.
- 4
The recording is analyzed on the fly
The conversation is transcribed in real time; metrics, topics and data extraction feed the prompts, and after the call the recording flows into the shared analysis loop.
- 5
The outcome straight into the CRM
After the call the collected fields, notes and transcript have already been pushed to the CRM via the crmPush action — no need to fill in the card by hand.
Industry scenario
A transfer to the second line without "please explain again"
A customer calls with an order complaint. The AI receptionist takes the first line, identifies the customer by number, confirms the order number and the nature of the problem, surfaces that this customer already contacted support with a similar question last week — and by the routing rules transfers to a second-line agent. But it doesn't disconnect: it stays in the conference and keeps recording. The agent picks up the conversation with a ready summary on screen and doesn't make the customer explain a third time what happened.
While the conversation runs, the whisper coach works on the agent's screen: it shows the customer's inquiry history, pulls a knowledge-base article for the current symptom, prompts the next step per policy and warns of risk — for example, that the customer is already on the edge of churn or that a compensation can't be promised on this plan. The agent runs the conversation by voice, the AI takes the notes: captured fields, the essence of the complaint and the resolution are recorded in real time from the live transcript.
After the call there's nothing to fill in — the fields, notes and transcript have already gone into the CRM/help desk via crmPush: the contact found by number, the ticket updated with the conversation outcome. And the recording itself enters the shared analysis loop like all the others — the metrics, topics and auto-tags of the live call join the quality dashboards and later refine both the receptionist's scenarios and the whisper coach's prompts for the next conversations.
Key facts
- The key differentiator
- After the transfer the agent stays on the line in conference mode — it doesn't disconnect but switches to prompting the operator.
- What the operator sees
- A context summary, the next action, warnings and collected fields — in real time from the live transcript.
- After the call
- Collected fields, notes and the transcript are pushed to the CRM via the crmPush action — with no manual back-fill of the card.
- The mechanics
- Live call monitoring: real-time transcription, metrics and topics feed the on-screen prompts.
- Closes the flywheel
- Recordings of the conversations land in the shared analysis loop — the same data later improves the secretary's scenarios.
Related scenarios for this industry
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