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CASE STUDIES
AI Call Quality Intelligence System for Al Arabi Company — Real-Time Multilingual Agent Performance Analytics
ZeluAI designed and deployed a custom AI Call Quality & Operations Intelligence platform for Al Arabi Company, one of Kuwait’s largest engineering and maintenance providers, to transform how their call center performance is monitored.
With over 500 inbound service calls per day, manual quality reviews were no longer realistic. Leadership lacked real-time visibility into agent performance, unresolved issues, customer sentiment, and operational risk. Our AI system now pulls call recordings live from their call center, transcribes conversations in Arabic and English, understands customer intent and emotion, evaluates agent responses, flags high-risk cases, and automatically generates structured summaries and performance metrics — all inside a centralized dashboard.
Instead of relying on random sampling or delayed manual audits, Al Arabi now operates with continuous, AI-driven call intelligence: every conversation is analyzed, every risk is surfaced, and every agent is scored — giving management instant insight into service quality, customer experience, and operational health.
This replaced hours of daily human review with real-time automation, enabling faster escalation, better coaching, higher accountability, and data-backed decision making across the entire call center.
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OVERVIEW
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CHALLENGES
Al Arabi Company operates a high-volume call center handling over 500 service calls per day across Arabic and English. These calls cover everything from urgent maintenance issues to customer complaints and follow-ups.
Historically, call quality monitoring relied on manual reviews and random sampling. This created several operational gaps:
Only a small fraction of calls were ever reviewed
Quality assessments were delayed and subjective
Critical customer issues were sometimes missed
Agent performance lacked consistent, data-driven measurement
Leadership had no real-time visibility into customer sentiment or unresolved risks
It was operationally impossible for human QA teams to listen to hundreds of calls daily, evaluate agent behavior, understand customer emotions, and detect systemic issues at scale.
How do you continuously monitor 100% of call center conversations, across two languages, while accurately measuring agent performance, customer sentiment, and operational risk—without adding massive QA headcount?
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SOLUTIONS
ZeluAI designed and deployed a custom AI Call Intelligence & Quality Management Platform tailored specifically for Al Arabi Company’s engineering and maintenance operations.
The system integrates directly with their call center infrastructure and automates the entire QA workflow:
Key capabilities delivered:
Multilingual Call Processing (Arabic + English)
All calls are ingested automatically and transcribed in real time, supporting both Arabic and English conversations.
AI Understanding of Each Conversation
For every call, the AI identifies:
Customer problem and intent
Emotional tone and frustration level
Agent response quality
Whether the issue was properly addressed
Follow-up commitments and resolution status
Automated Call Scoring & Agent Performance
Each interaction is scored based on:
Problem understanding
Empathy
Resolution effectiveness
Risk level
Agents receive objective quality scores, replacing manual and inconsistent evaluations.
High-Risk Detection & Escalation
The platform automatically flags:
Unresolved issues
Repeat complaints
Technical risks
Emotionally distressed customers
High-risk calls appear instantly on management dashboards for immediate action.
Operational Dashboards & Insights
Leadership gains real-time visibility into:
Total calls processed
Resolution rates
Agent performance rankings
Work order outcomes
High-risk alerts
Customer sentiment trends
All data is centralized into a single intelligent operations dashboard.
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RESULTS
Operational Efficiency
Reduced manual QA workload by approximately 85–90%
Eliminated the need for human review of hundreds of daily calls
Saved an estimated 6–8 hours per day of QA and supervisory labor
Full Call Coverage
Increased call quality monitoring from under 10% (random sampling) to 100% of calls analyzed
Every conversation is now evaluated automatically, with zero blind spots
Faster Risk Identification
High-risk customer issues detected in real time instead of days later
Improved escalation speed by over 70% for critical maintenance cases
Agent Performance Visibility
Objective quality scoring across all agents
Clear identification of top performers and coaching opportunities
Improved accountability and consistency across shifts
Business Impact
Higher first-call resolution rates
Reduced repeat complaints
Improved customer experience
Stronger operational control across engineering service workflows
Bottom Line
ZeluAI transformed Al Arabi Company’s call center from reactive, manual quality checks into a fully automated, AI-powered intelligence operation.
Instead of listening to calls, managers now see insights.
Instead of guessing performance, they measure it.
Instead of discovering problems late, they act in real time.
This system now operates continuously—analyzing hundreds of daily conversations, protecting service quality, and saving countless human hours every month.
85–90%
Reduced manual QA workload by approximately
6–8
Saved an estimated hours per day of QA and supervisory labor
100%
of calls analyzed with important KPI metrics
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CASE STUDIES
