Our customer is a leading telecommunications provider offering mobile, internet, and television services to thousands of customers. Their call center operations were handling tens of thousands of inbound calls daily, primarily related to service activation, billing questions, plan upgrades, and connectivity issues.
The telecom provider faced heavy call traffic from routine queries, overwhelming support teams, resulting in high turnover due to repetitive ticket handling. Customers received long wait times during peak hours, leading to high customer churn risk
We proposed to deploy a voice agent capable of autonomously handling Level 1 support calls. The solution used conversational AI and voice recognition to understand customer requests, retrieve relevant information from backend systems, and respond in real-time using natural speech.
Software Deployment
Use Case Discovery and Mapping
We began with in-depth discovery workshops alongside the client’s support operations and compliance teams. We mapped out their telephony infrastructure, system endpoints, and knowledge bases, while also defining key performance indicators.
Data Collection and Conversation Modeling
With use cases clearly defined, we moved into preparing the conversational intelligence framework. We collected anonymized call transcripts and support chat logs to prepare a detailed intent and entity recognition model specific to telecom support workflows.
Voice Agent Development and Integration
The core solution was developed using modular components, including speech-to-text, natural language understanding, and text-to-speech systems. We integrated the voice agent with the client’s systems and service dashboards to enable real-time information retrieval.
Controlled Rollout to Live Call Flows
Once the system was validated, we began a phased rollout into the live call center environment. Initially, the voice agent operated alongside the legacy system in an A/B testing mode. We closely monitored interaction quality, escalation rates, and user behavior in real-time.
Continuous Learning and Feature Expansion
Post-deployment, we focused on refining and expanding the voice agent’s capabilities. Key metrics like resolution time, customer satisfaction, and escalation frequency were continuously tracked. We fine-tuned prompts and adjusted voice output for clarity and tone.
Using advanced speech recognition and telecom-specific intent models, the system could understand free-form questions.
The voice agent could perform real actions directly to internal systems. It could check balances, activate services, reschedule appointments, or file issue reports.
When needed, the system gracefully handed off calls to live agents, passing along caller context, intent, and conversation history.
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