AI for contact centers: voice agents, handoff and call QA

What does AI change in a contact center?

Three things, in order of how often they pay off. First, it takes repetitive calls: confirmations, reminders, qualification questions, simple order lookups. Second, it turns every call into data, so follow-ups, callbacks and coaching no longer depend on someone's notes. Third, it reviews calls at a scale no quality team can match by listening.

It does not remove the need for people. Callers still ask for a person, some calls are too sensitive to automate, and a voice agent that cannot hand over is a dead end. Every contact-center system Mirai Minds builds has a way out to a human.

Which calls should an AI agent take?

Calls with a clear goal and a short list of outcomes. An outbound campaign that confirms details, asks a few questions and books a next step is a good fit. So is an inbound line where most callers want the same few answers. Calls where the caller is upset, the stakes are high, or the answer depends on judgment should reach a person quickly.

We usually start by reading a sample of real call recordings or transcripts and sorting them into those buckets. That sample becomes the first test set.

How do people stay in the loop?

Through handoff during the call and review after it. On our Voice Agents platform, an agent that needs a person raises a handoff request, which appears on the team's dashboard. The first person to accept joins the same live call; the claim is atomic, so two people never grab one caller. If nobody accepts within 60 seconds, the agent tells the caller that no one is free and offers a callback, instead of leaving them on hold.

After the call, a model reads the recording and fills structured fields: whether a person answered, whether they engaged, whether they asked for a callback and when, whether a meeting was booked, and the exact words of any conflict. Supervisors work from those fields rather than from memory, and every field links back to a recording a person can check.

How do you measure quality?

With a rubric, applied to every call. CallAuditAI scores recorded calls against weighted categories, in one deployment opening and rapport, needs discovery, value and objection handling, and closing, and returns an Excel scorecard with any zero score flagged in red. Managers start with the red flags and listen to those recordings first.

The same scoring works for human and AI calls, which matters: it lets you compare an AI agent with your team on the same rubric before you move more calls to it. Before trusting the totals, we recommend a calibration round in which managers score a sample blind and compare with the model, criterion by criterion.

What have we shipped for contact centers?

• For an AI outbound-calling startup, a Pipecat voice bot over LiveKit SIP, three-layer turn detection, voicemail detection in English and Spanish, a campaign dialer with per-campaign and global live-call caps, and one recording per call.
• For an admissions counseling company, CallAuditAI for scoring recorded counseling calls.
• For the founder of Myva.ai and CallPaaS, live call transcription with sentiment and GPT-assisted virtual agents.
• Our own Voice Agents platform, with campaigns, live handoff, post-call data and Hindi calls.

What does a first project look like?

A short discovery call, then a week of reading your calls and agreeing what success means for each call type. Then a pilot on one call type with one script, one number and a handoff queue staffed by your team, followed by expansion once the scores and the handoff rate look right. The service pages for voice AI agents and custom LLM and document AI go deeper on the technology.