Hotel reservation and mystery calls were manually reviewed by auditors, requiring repeated listening to the same conversations to assign scores and fill structured audit forms.

This approach was accurate but time-consuming, fatiguing, and difficult to scale as call volumes increased.

Auditors spent hours transcribing and interpreting calls, leading to:

• Human error from long listening hours
• Slow turnaround for escalated cases 
• Inconsistent scoring across auditors
   

We designed an AI-first audit architecture where voice became structured data. The system introduced:

• Automated speech-to-text pipelines
• NLP-based answer extraction
• Mapping of conversations directly to audit questionnaires

To support real-world hospitality audits, the platform allowed IFH to:

• Design flexible questionnaires
• Define sections, instructions, and scoring logic
• Apply the same audit framework consistently across calls

Audits became repeatable, objective, and policy-driven.

Outputs were designed for trust and traceability:

• Concise summaries for quick reviews
• Detailed reasoning with timestamps for escalations
• Downloadable transcripts and audio for verification

Auditors shifted from listening to interpreting insights.

The system was built for:

• On-premise deployment to meet regional data requirements
• Multilingual call processing 
• Growth from pilot volumes to enterprise-scale audits
   

Voice auditing evolved into a sustainable, scalable QA operation.