AI Call Auditing & Agent Coaching for Airlines
Score every traveller call and chat against your own QA scorecard, in Arabic and English, and turn the findings into coaching while the conversation is still fresh.
Typical outcomes for this kind of engagement. Results depend on your data and starting point.
Airline contact centres absorb enormous volume through disruption peaks, and quality assurance is a manual sample: a supervisor listens to a handful of calls per agent per month. Coaching arrives weeks after the conversation, systemic problems surface late, and exposure on fare rules, refunds and denied-boarding entitlements is judged from a fraction of the evidence.
- QA covers a small manual sample, so most interactions are never reviewed
- Coaching lands weeks after the call, when neither agent nor traveller recalls it
- Repeat failure patterns on refunds and rebooking entitlements surface late
- Disruption peaks are when quality matters most and sampling is thinnest
- Arabic and English reviewed by different people, against inconsistent standards
CloudGate transcribes and scores every call and chat against the airline's own QA scorecard using Azure AI Speech and Azure OpenAI, in Arabic and English including dialect. Each interaction carries a score, the transcript evidence behind it and a coaching note, and supervisors see the trend by agent, queue, topic and disruption event in Power BI.
- Speech-to-text and speaker separation on every call, Arabic and English including dialect
- Scored against your existing QA scorecard rather than a generic model
- Transcript evidence quoted for every score, so a result can be challenged and reviewed
- Coaching notes generated and routed to the agent and their supervisor the same day
- Trends by agent, queue, topic and disruption event in Power BI
- Sentiment and escalation-risk flags surfaced on live queues