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Network outages don't wait for convenient moments. When customers face connectivity issues, they expect swift resolution, not endless hold times or the frustration of explaining their problem multiple times to different agents.
A major telecom provider knew their clients felt this pain every day. That's why they partnered with Master of Code Global to build a Gen AI Data Collection Flow that actually listens, learns, and gets problems solved faster. Our solution reshapes the entire support dynamic, turning routine diagnostic work into an intelligent collaboration between AI and human expertise.
Consumer frustration was mounting as network troubleshooting became increasingly time-consuming. Agents found themselves asking the same diagnostic questions repeatedly, creating bottlenecks that delayed resolution times.
The telecom provider faced a critical decision: either expand their support team significantly or find a smarter way to handle the growing volume of technical inquiries. Traditional approaches weren’t scaling with customer demands, and the company needed a solution that would streamline operations without sacrificing service quality. Manual data collection was eating into valuable agent time that could be better spent solving complex problems requiring human insight.
Master of Code Global built an AI assistant that actually gets how real conversations work: no robotic scripts, just natural problem-solving.
Our team built a sophisticated AI agent that transforms network troubleshooting from a manual, time-intensive process into a streamlined, intelligent conversation. The solution operates directly within existing messaging channels, allowing human agents to deploy AI assistance with a single click during live user interactions. This isn’t just another chatbot – it’s a smart diagnostic partner that adapts to each customer’s unique situation.
The AI agent intelligently skips questions already answered during the conversation, demonstrating contextual awareness that clients appreciate. It interprets unstructured responses naturally, understanding when a consumer says “it’s been acting up since yesterday” instead of requiring rigid, predefined answers. The system guides users through targeted troubleshooting steps while collecting comprehensive diagnostic data in real-time.
What sets this solution apart is its ability to work alongside human agents rather than replacing them. The AI handles routine data collection and basic troubleshooting, then delivers structured insights to agents who can focus on complex problem-solving. This collaborative approach maximizes both efficiency and customer satisfaction, creating a win-win scenario for all stakeholders.
Tracks how many network issues get resolved without requiring human escalation
Measures cases solved during the initial user interaction
Monitors how often consumers give up before getting help
Shows how efficiently agents work with pre-collected diagnostic information
Captures real feedback through NPS and CSAT measurements
