Client
A mid-sized US-based service company handling a high volume of inbound customer calls related to product inquiries, order status, and support requests. The company was looking to modernize its support operations and reduce dependency on human agents for repetitive interactions.
Objective
Automate first-line customer support and lead qualification using a real-time AI voice agent, reduce operator workload, and improve response speed while maintaining a seamless customer experience.
Location
United States
Development Time
2 months
Cooperation Period
2025
Work Approach
Technical Architecture
AI & voice processing layer
Speech-to-Text (real-time transcription)
LLM-based reasoning and dialog management
Text-to-Speech (natural voice responses)
Decision & control logic
Intent detection with confidence scoring
Rule-based escalation to human agents
Context-aware response generation
Infrastructure
Cloud-based scalable voice AI platform
Near real-time latency (<200 ms response time)
Integrations
CRM systems (lead capture and updates)
Knowledge Base and FAQ system
Call center infrastructure
Key Results
Operational efficiency
Up to 50% reduction in operator workload
Faster response time with 24/7 availability
Reduced average call handling time
Customer experience
Immediate responses without waiting in queue
Consistent answers across all standard requests
Seamless escalation to human agents when needed
Business impact
Improved lead qualification from inbound calls
Lower operational costs
Scalable support without increasing headcount
