How GenAI Support Assistants Are Reducing Call Center Load for Telecom Operators

The Challenge

Call center agents spent much of their time on simple, high-volume queries, which increased wait times and reduced capacity for complex customer issues.

The existing approach made it difficult to:

• Handle high volumes of repetitive billing, plan, and usage queries.
• Give subscribers accurate, account-specific answers without agent involvement.
• Provide consistent support across app, web, and messaging channels.
• Guide subscribers through basic connectivity troubleshooting.
• Hand over complex cases to agents with full conversation context.
• Keep answers accurate as plans, offers, and policies changed.

From a technical perspective, the assistant needed to answer from the operator's own knowledge and live account data rather than general model knowledge. This required RAG over plan and policy documents, secure function calling into billing and CRM systems, and guardrails to keep responses accurate and compliant.

The Solution

InnovationM developed an AI agent-powered support assistant that understands subscriber questions in natural language and resolves them using the operator's knowledge base and account systems. The solution included:

• Conversational AI: Using large language models to understand subscriber intent across varied phrasing and languages.
• Retrieval-augmented generation: Grounding answers in current plans, tariffs, policies, and troubleshooting guides.
• Account-aware responses: Fetching bills, usage, plan details, and service status through secure function calling.
• Self-service actions: Supporting plan changes, add-on purchases, and service requests within the conversation.
• Guided troubleshooting: Walking subscribers through step-by-step fixes for common connectivity issues.
• Agent handover: Transferring complex cases to human agents with the conversation summary and account context.
• Omnichannel deployment: Delivering the assistant across the mobile app, website, and messaging platforms.
• Guardrails & monitoring: Applying response validation, data protection, and quality monitoring to every conversation.

The key focus was not simply launching a chatbot, but creating a GenAI support capability connected to real telecom software development work across billing and CRM systems.

The Impact

The implementation helped the operator move from agent-dependent support toward AI-assisted self-service for routine queries. Agents gained more time for complex cases, and subscribers gained faster answers. The solution supported:

• Reduced call center load for routine queries.
• Shorter wait times for subscribers.
• Accurate, account-specific answers in self-service channels.
• Consistent support across app, web, and messaging.
• Smooth handover to agents with full context.
• A foundation for proactive, AI-driven customer engagement.

By bringing together GenAI, RAG, account integration, and omnichannel delivery, InnovationM demonstrated how telecom software engineering services can modernize customer support at scale. The project also reflects the role of software development in turning AI models into dependable customer-facing products.