Amernet AI quality assurance reviews calls the way a supervisor would — except it reviews all of them, and it knows what the agent was supposed to do.
The reviewer is given the agent’s own instructions and scores whether they were followed. Not a generic sense of a good call: the rules you wrote for this agent.
When a call breaks its rules, you get the specific turn where it went wrong and what the violation was — not a score out of ten.
Judged against your instructions, not a generic rubric.
Every agent runs on instructions you control — how to greet, when to stay quiet, what to do at a voicemail beep, how to handle somebody screening the call. Those are the rules the reviewer is given.
So the verdict is not “this was a 7”. It is whether this call followed the rules this agent had, and if not, which one it broke and at which turn.
It also records what the agent was actually talking to: a person, a touch-tone menu, a voicemail greeting, another AI receptionist, or several in sequence — and whether it ever reached a human at all.
That single field changes what your connection rate means, because “the call connected” and “a person answered” are very different numbers.
Specific enough to act on.
Human QA reviews one call in a hundred, and never the right one.
Traditional call QA is sampling. A supervisor listens to a handful of calls a week, chosen more or less at random, and forms an impression. It is better than nothing and it reliably misses the call that lost the customer.
Reviewing every call removes the sampling problem entirely. The bad calls do not have to be stumbled upon — they identify themselves.
It matters more with an AI agent than a human one, because an agent fails consistently. A person has an off day; a misconfigured agent mishandles the same moment on every single call until somebody notices.
Which is exactly the failure that sampling is worst at catching, and exhaustive review is best at.
What people ask about reviewing agent calls.
All of them. The point is to remove the sampling problem, because the call worth hearing is rarely the one a person would have picked.
The agent’s own instructions. The reviewer is given the rules that agent was operating under and judges whether they were followed, rather than applying a generic idea of a good call.
The turn where the agent first broke its rules, the reason, and a named violation — for example speaking before a human was heard, or mishandling a voicemail greeting.
Yes. Each call records what it reached — a person, a touch-tone menu, a voicemail greeting, another AI receptionist, or a sequence of them — and whether a human was reached at all.
Sentiment tells you how a call felt. This tells you whether the agent did what it was told to do, and where it stopped doing it.
Alongside the call itself, with the transcript and recording. See conversations, and analytics for the totals.
Run real calls; read the reviews together.
The useful half hour of any evaluation is reading the review of the call that went worst. Book a short session, run some real calls, and we will do exactly that.
