NeuralText Analytics

The Challenge

The client, an international telecommunications enterprise with over 5 million subscribers, received more than 10,000 text-based support tickets daily. Triage and routing were handled manually by support teams, which created ticket assignment delays and slowed response times.

Furthermore, critical customer complaints detailing service outages or account issues were mixed in with simple inquiries. Because there was no automated categorization mechanism in place, high-priority issues remained unassigned for hours, increasing customer churn rates.

The Solution

Ankur Weldtech India engineered an automated text categorization pipeline using Python, PyTorch, and HuggingFace transformer modules. To handle the high daily ticket volume, we selected a lightweight DistilBERT model. This model was fine-tuned on the client's historical support data to categorize tickets and evaluate customer sentiment flags dynamically.

To integrate the model with the client's existing Java-based ticket management system, we built a Python API using the FastAPI framework. The API is hosted in containerized microservices that load the PyTorch weights into memory. An active Kafka message broker routes incoming support tickets to the FastAPI model, which analyzes and returns category labels within 150 milliseconds.

Key Results

By deploying NeuralText Analytics, the client successfully automated support routing workflows. Outage reports and critical fraud complaints are now identified instantly and routed to high-priority resolution squads, significantly reducing overall customer churn.