AI-Powered Customer Support Chatbot

### Project Overview We developed an **AI-Powered Customer Support Chatbot** for a leading telecommunications provider. The goal was to reduce the load on human support agents and provide 24/7 assistance to millions of customers. The bot needed to handle complex queries regarding billing, technical support, and plan upgrades naturally and accurately. ### Problem Statement The client's support center was overwhelmed with repetitive queries, resulting in long wait times and low customer satisfaction. Traditional rule-based chatbots failed to understand context or handle nuances in customer language. ### Solution Provided NacroSoft leveraged Advanced NLP and Large Language Models (LLMs) to build a context-aware chatbot. The system was trained on years of historical chat logs to understand customer intent. It was integrated directly into the client’s CRM and billing systems to perform actions like resetting routers or upgrading data plans autonomously. ### Technologies Used * **Frontend:** React Native (Mobile App Integration), Web Widget * **Backend:** Python, FastAPI, LangChain * **Database:** MongoDB, Pinecone (Vector DB) * **AI/ML:** OpenAI GPT-4 API, Hugging Face Transformers * **Cloud:** Azure Cognitive Services ### Key Features * **Contextual Understanding:** Remembers previous interactions to provide personalized responses. * **Action Execution:** Can autonomously process refunds, change plans, and schedule technician visits. * **Sentiment Analysis:** Detects frustrated customers and seamlessly escalates them to human agents. * **Multi-Language Support:** Fluent in 12 languages to serve a diverse customer base. * **Analytics Dashboard:** Insights into common queries, bot performance, and customer sentiment. ### Business Impact * **Automated 80% of Level 1 support queries**, significantly reducing call center volume. * **Saved $1.5M annually** in operational costs. * **Increased customer satisfaction (CSAT) score by 18%**. ### Team Size 8 Members ### Role Breakdown * **AI Engineer:** Fine-tuned the LLM models and implemented RAG (Retrieval-Augmented Generation). * **Backend Developer:** Built the API infrastructure and CRM integrations. * **Frontend Developer:** Created the chat widget and mobile integration. * **Data Scientist:** Cleaned and prepared historical data for training. * **Project Manager:** Coordinated with the client to define intents and conversation flows. * **QA Engineer:** Tested conversational accuracy and edge cases.