Skip to content

Every conversation reviewed, not a two-percent sample.

Voice IntELIEgence transcribes the call, works out who said what, reads sentiment and topic as it goes, and scores the whole thing against the rubric your QA team already uses.

  • Speaker personas
  • Rubric grading
  • Topic & sentiment analytics
EliehubVoice IntELIEgenceconv_8842 · 06:14
ConversationGradingPersonasTopicsAnalytics
AgentCustomer02:41 / 06:14
A

"I can see the payment bounced on the third — let me check what happened there."

empathybilling
C

"This is the second time. I'm getting charged a late fee for your system failing."

frustratedchurn risk
A

"…I can waive the late fee this once, but the direct debit has to go on today."

resolutioncommitment
C

"Fine. Set it up now and email me the confirmation."

accepted

RUBRIC SCORE

7/ 9
Identity verified
Empathy shown
Resolution offered
Fee policy quoted
Complaint logged

TOPICS

direct debitlate feerepeat failure

What it does

Speech in, evidence out.

Transcribes with the audio attached

Turns are time-coded against the recording, so any line in the transcript plays back the moment it came from rather than being taken on trust.

Knows who was speaking

Enrolled personas attribute turns to the right speaker, so coaching notes and quality scores land on the right person.

Scores against your rubric

Your existing scorecard, with its own weightings, applied to every conversation — not to whichever handful someone found time to review.

Use cases

Where teams point it first.

  1. 01

    QA reviews a small sample of calls and coaching is argued from anecdotes.

    Every conversation is scored on the same rubric, so a trend is something you can point at instead of something you sense.

  2. 02

    A complaint arrives about a call that happened three weeks ago.

    The transcript, the speaker attribution and the audio are together in one place, with an audit trail of any edit.

  3. 03

    Nobody can say which topics are actually driving contact volume.

    Topics and sentiment are read as the conversation is processed, so volume and mood become a chart rather than a guess.

  4. 04

    Quality data lives in a spreadsheet that someone maintains by hand.

    Scores and analysis export on a schedule to the system that already runs your reporting.

How to use it

Four steps from first recording to a trend line.

  1. /elie-voice/new

    Create a voice app

    One app per conversation type — collections calls behave nothing like new-business calls, and they shouldn't share a rubric.

  2. /personas · /grading

    Describe the speakers and the scorecard

    Name who you expect on each side of the call, then paste in the rubric your QA team already argues about. Weight the criteria that matter.

  3. /conversations

    Feed it conversations

    Upload recordings or connect your telephony store through IntELIEKonnect. The pipeline reports progress per conversation, and failures stay visible.

  4. /topics-sentiment · /analytics · /export

    Read the trend, then send it on

    Score distribution by team, topic volume over time, sentiment against outcome. Export the whole thing when someone asks for it in a spreadsheet.

Send us one recorded call and your scorecard.

We'll transcribe it, attribute the speakers and grade it against your own rubric — then show you what the same thing looks like across a month.