Stop auditing 2% of your calls.There's a better way.
Mira scores listens to your team's conversations, answers your own scorecard from the transcript, and shows an evaluator exactly which words earned each point. You review and sign. Nothing is scored behind your back.
What changes in the first month
What the top plan allows; the entry plan covers 50. Either way it is not the three or four an auditor gets through by hand.
An agent hears about a call while they still remember making it.
Your scorecard, your teams, your first calibrated results.
English and Spanish, and the AI reads far more than that.
The sample was never the problem. It was all you could afford.
A traditional quality programme reviews a handful of calls per agent per month, three weeks late, scored slightly differently by each auditor. Everyone knows it. Nobody has the hours to fix it.
A sample nobody believes
Four calls a month out of six hundred is not a measurement, it is an anecdote. The agent knows it too, which is why the score never changes behaviour.
Feedback that arrives too late
By the time the review lands, the call is three weeks old and the agent barely remembers it. That is a report, not coaching.
Two auditors, two scores
The same call scored by two people lands five points apart, and nobody finds out until an agent disputes it.
No idea why contacts happen
You know the score. You do not know what customers actually called about, or which of those reasons is growing.
One platform, the whole quality programme
From the first recording to the coaching conversation it leads to — and every audit trail in between.
How a conversation becomes coaching
Five steps. The AI does the listening and the first pass; your team does the judging.
The conversation arrives
Upload a recording, paste a chat, or have your dialer push them in through the API. Calls, chats and email all become the same kind of record.
It becomes text
Speaker-labelled, in the language it was spoken, with card numbers and identifiers masked before the text is stored. Chats and emails skip this step — they are already text.
The model answers your scorecard
Question by question, quoting the passage behind each answer, and flagging anything the conversation did not actually settle.
An evaluator signs it
They read the draft, change what they disagree with, and submit. The evaluation is theirs, and the changes are recorded.
It turns into something
A number on the dashboard, a dispute if the agent objects, a data point in the next calibration, and a line in that agent's coaching plan.
Two engines, and we are specific about which does what
Language runs on Google Gemini Enterprise. Everything acoustic, and every masking rule, runs on infrastructure we operate — no second AI vendor, no third party holding your audio.
Multimodal
It reads audio and text in the same pass, so transcription and analysis come from the same understanding of the call.
Strong reasoning
It follows a scorecard the way a person does — weighing what was said against what your criteria actually ask for.
Answers from evidence
It is asked to answer only from what the transcript shows, and to mark a question as unverified rather than guess. Low hallucination, and honest when it doesn't know.
Always the latest model
We move to the newest model as soon as it's available to us, so accuracy improves with no work on your side.
Teams running quality on Mira
Where the teams evaluating on the platform are, as a share of the total.
Built for the way your floor actually runs
Same platform, different definition of a good conversation. Pick the one that sounds like your operation.
Your conversations do not leave your control
Sensitive data is masked the moment a transcript exists — before it is stored, before it is scored, before anyone sees it — and the untouched text is never written down at all.
Your data is yours
The audio of your calls, the transcripts produced from it and the resulting analysis belong exclusively to your organisation.
Never used for training
We guarantee your data is NOT used to train Google's AI models or anyone else's. Your conversations do not improve the public models other customers use.
Isolated and encrypted
Everything is processed in an isolated environment and travels encrypted, both in transit and at rest, meeting the industry's highest security standards.
An evaluator with AI is worth more than a faster evaluator
The point isn't to review the same calls in less time. It's to review far more of them, act on the same day, and free the hours that actually change behaviour.
Several times more evaluations
Teams using AI-assisted scoring review a multiple of what they could by hand in the same hours — a sample that finally represents the floor instead of a handful of calls a month.
Feedback the same day
When scoring stops being the bottleneck, an agent hears about a call while they still remember it. Feedback three weeks later is a report; feedback today is coaching.
Hours back for coaching
The time an evaluator spends listening is time not spent with the agent. Giving that back is where the return actually is — coaching changes behaviour, scoring only measures it.
A human always decides
Every AI-scored evaluation is reviewed by a person before it is submitted, and the model flags anything the transcript didn't prove. The AI drafts; your team signs.
Gains depend on your call volume, scorecard length and how much of your audio is transcribable. We'll size it with you honestly before you commit.
See it on your own calls
Send us a handful of recordings and your current scorecard. We will score them, show you the result next to what your team scored, and tell you honestly what you would and would not gain.
No automated demo. A real conversation, usually within one business day.