Two things only you know, and the analysis needs both.
A conversation is evidence of what was said. It is not evidence of which campaign the agent was on, which offer was in force that week, or what was typed into your CRM afterwards. Elite AI lets you define your own reported metrics and your own context blocks, so the model answers the questions your operation actually asks and scores against the rules that were actually in force.
What you get
Your own metrics, reported on every conversation
Define up to three questions of your own — contact reason, outcome, product mentioned — and the model answers each one from a closed list you write. A closed list is what makes the answer countable: it charts, it filters, and it stays comparable across a year.
Context the recording cannot contain
Up to three named boxes per conversation — the campaign, the disposition, the notes pasted from a ticket — filled in by the evaluator and read by the analysis alongside the transcript.
Questions that used to be unanswerable
'Did the agent record the parameters correctly?' cannot be answered from a transcript, so the model used to mark it unverified on every call. With the context in front of it, the question gets a definite answer.
Treated as background, never as evidence
The prompt states that context is somebody's account of what happened, not a record of it: where it disagrees with the conversation, the conversation wins. Context is masked exactly like a transcript, and the agent can read what the analysis read.
How it works
Define them once, for the whole company
Metrics and context blocks are set at company level rather than per scorecard, so a scorecard being rewritten never changes what a stored answer meant.
Fill the context in as you go
The evaluator completes the boxes on the conversation. Retiring a block later does not erase what was already recorded.
Read them back as data
Your metrics appear on the conversation, on the evaluation, and as a breakdown in the dashboard, where every value is a cross-filter like any other.
Questions people ask about this
Each one is added to every analysis of every conversation. A long list would cost tokens on each call and compete with your scorecard for the model's attention, which would quietly make the scoring worse in order to answer questions few people read. Three is the number that stays useful without doing that.
That is precisely the failure we design against. Context is presented to the model as background rather than as evidence, the conversation takes precedence wherever the two disagree, and every text field written in our forms is screened for instructions aimed at the model before it is saved.
Related capabilities
See it on your own calls
Book a walkthrough of the software. Send us a few recordings and your current evaluation form; we load them into Mira scores so you can watch the platform score your own calls, side by side with the scores your team assigned.
A live walkthrough of the platform with a person, usually within one business day.