Call Whisperer
An AI quality analyst for contact centres. It transcribes recorded calls, Arabic included, scores each agent against a QA rubric with quoted evidence, and turns batches of calls into reports in Arabic or English.
- Arabic speech-to-text
- QA scoring
- Sentiment
- Arabic & English reports
01
Overview
Call Whisperer takes recorded calls, organised in folders and batches, converts them to a clean audio format and transcribes them with Azure Speech, set to Egyptian Arabic by default. A language model then labels who said each line, extracts the call type, reason and resolution, and scores the agent on six areas from greeting to closing.
- Category
- Call quality analytics
- Industries
- Contact centres · Enterprise
02 · Business value
What it changes for the business
Call Whisperer
Score every recorded call on one rubric, with evidence, without adding reviewers.
- 01
Wider QA coverage
Calls are scored once they are uploaded, so quality checks no longer depend on how many calls a reviewer can listen to.
- 02
Fairer agent coaching
A fixed rubric and quoted evidence make feedback consistent between reviewers and easier for agents to accept.
- 03
Less reporting effort
Score sheets and batch reports come out of the app instead of being assembled by hand in spreadsheets.
- 04
Clearer compliance risk
Each call gets a compliance score and a resolution status, so risky calls and unresolved issues stand out early.
03 · The Problem → The Outcome
Challenges → Solutions
- 01
Challenges: Reviewing calls means hours of listening and manual note-taking
Solutions: Uploaded calls are transcribed and analysed in the background
- 02
Challenges: Agent scores vary from one reviewer to the next
Solutions: Every call is scored on the same six areas, each with a 1-10 score and quoted evidence
- 03
Challenges: Arabic calls are hard to transcribe and review at volume
Solutions: Arabic speech recognition with speaker-labelled transcripts and evidence translated into English
- 04
Challenges: Team leads have no per-agent view of tone and sentiment
Solutions: Agent sentiment profiles show sentiment distribution and average tone per agent
- 05
Challenges: Reporting on a batch of calls means building spreadsheets by hand
Solutions: Excel score sheets and PDF batch reports in Arabic or English, generated from the app
04 · Use cases
Where it fits, by industry
Contact centres
AI that reviews every conversation and helps contact-centre teams act on it.
- 01
Quality assurance at scale
QA teams upload a day's calls as a batch and get a scored, evidence-backed review of each one, with Arabic calls handled natively.
- 02
Agent coaching
Team leads open an agent's sentiment profile and tone breakdown to plan coaching from real moments in real calls.
Enterprise
Scalable AI solutions for large organizations.
- 01
Sales and booking lines
Each call is tagged with lead status, interest level, whether pricing came up and the follow-up actions, so sales teams know who to call back.
05
Enterprise Capabilities
Intended Users
Contact-centre quality analysts, team leads and supervisors.
Call Whisperer
Call quality analytics
- 01Arabic-first call transcription
- 02Speaker-labelled transcripts
- 03Six-area QA scorecard with evidence quotes
- 04Customer and agent tone breakdown
- 05Configurable score sheets exported to Excel
- 06Batch reports and a chat assistant over call folders
06 · Highlights
Key Proof Point
Call Whisperer
Each QA score comes with the exact words from the call that justify it, so agents and supervisors can check the judgement instead of taking it on trust.
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- Arabic-first AI