MOJO-CXAI contact centre experience
MOJO-CX transforms the contact centre experience for customers and agents alike
voice analytics platform
Client
MOJO-CX
Industry
AI
Headquarters
United Kingdom
Services
Web Development
About project
MOJO-CX is an AI-powered contact centre intelligence platform that transforms agent-customer interactions through real-time guidance, automated quality assurance, and structured performance analytics.
Human-Readable Translation
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Contact centres struggled with inconsistent agent performance, high training costs, and reactive quality management — impacting customer satisfaction and operational efficiency.
Zylo developed a real-time agent assistance platform with AI-driven conversation guidance, automated quality scoring, and structured coaching workflows for team leaders.
Process
Discovery
Contact centre workflow analysis
Agent performance baseline
Quality framework review
Integration audit
Compliance mapping
AI Architecture
Real-time processing pipeline
NLP guidance engine
Quality scoring model
Agent interface design
Analytics framework
Implementation
Integration testing
Agent training
Quality calibration
Production rollout
System Architecture
A real-time conversation processing engine analyzes live calls and chats — surfacing agent guidance, sentiment signals, and compliance triggers through a low-latency API layer.
Interface Engineering
Agent interfaces display contextual guidance, knowledge suggestions, and sentiment indicators without disrupting the natural flow of customer conversations.
Every agent empowered with real-time intelligence — every customer conversation elevated.
MOBILE-FIRST
EXECUTION
Team leaders access performance dashboards, coaching alerts, and live session monitoring from mobile with role-based access to sensitive conversation data.
UI Kit
Component library covers agent guidance cards, sentiment meters, quality score panels, coaching workflow forms, and performance trend charts.
AUTOMATION
FRAMEWORK
Conversation transcription, quality scoring, compliance flagging, coaching task assignment, and performance reporting are fully automated.
Design system
Results
Measurable outcomes from real-world AI and engineering deployments.
+67%
Improvement in first-call resolution rate
-54%
Reduction in average handle time
4.7/5
Customer satisfaction score improvement
-71%
Reduction in manual QA review overhead
Verified across 500+ enterprise deployments
Let's work together
3-day AI Engineering Collaboration
Sprint
AI-driven collaboration sprint with senior engineers to design, build, and refine real-world software solutions. Focused on execution, technical depth, AI capability, and product thinking—not just ideas, but working systems.