Problem:

The customer service team faced several critical challenges that affected operational efficiency and customer satisfaction:

Slow Response Times: Agents struggled with high volumes of customer queries, leading to prolonged response times and increased backlog.
Repetitive Manual Effort: Agents spent excessive time searching for relevant information across multiple databases and documentation sources.
High Workload & Burnout: The manual nature of customer inquiries processing contributed to agent fatigue, increasing turnover and reducing overall service quality.
Lack of Intelligent Query Resolution: Existing systems lacked AI-driven capabilities to automatically retrieve and summarize relevant responses, requiring agents to craft answers from scratch each time.

The goal was to leverage AI technologies to streamline customer support, optimize response efficiency, and reduce agent workload.

Solution:

To overcome these challenges, I spearheaded the development and deployment of an AI-driven customer service solution, incorporating OCR and Retrieval-Augmented Generation (RAG) to improve efficiency:

AI-Powered Large Language Model (LLM) Integration:
- Implemented a Large Language Model (LLM)-based assistant to help customer service agents instantly retrieve accurate and contextually relevant responses.
- The model was trained on historical customer service interactions, company FAQs, and product documentation to generate reliable responses.
OCR for Document Processing & Query Resolution:
- Integrated Optical Character Recognition (OCR) technology to extract key information from scanned invoices, contracts, and user-submitted documents.
- Allowed agents to search and retrieve information within seconds instead of manually sifting through PDFs and physical records.
Retrieval-Augmented Generation (RAG) for Faster Responses:
- Designed a RAG-based system that combined the power of a search engine with a context-aware AI model to retrieve the most relevant response to customer inquiries.
- Instead of agents composing responses manually, the system would generate suggested answers that agents could quickly verify and send.
Automated Query Classification & Routing:
- Used machine learning models to classify incoming queries based on urgency and complexity, ensuring faster resolution of critical requests.
- Implemented an intelligent routing system that automatically assigned queries to the appropriate department, reducing misdirection and processing time.
Continuous Learning & Optimization:
- Integrated a feedback loop mechanism, allowing agents to rate AI-generated responses and provide corrections to continuously improve accuracy.
- Conducted monthly performance evaluations to fine-tune AI models based on real-world usage.

Impact:

The AI-powered solution dramatically improved customer service operations and agent efficiency, delivering the following key benefits:

✅ 75% Reduction in Customer Response Time:

AI-driven query retrieval and automated suggestions allowed agents to respond to customer inquiries nearly four times faster than before.

✅ Significant Reduction in Agent Workload:

Automating repetitive information retrieval tasks allowed customer service agents to focus on complex and high-value interactions, improving job satisfaction.

✅ Higher Query Resolution Accuracy & Consistency:

AI-generated responses ensured greater accuracy and consistency in customer interactions, reducing errors and miscommunications.

✅ Streamlined Document Processing via OCR:

The integration of OCR technology cut document processing time by 60%, allowing agents to quickly access and verify contract details.

✅ Improved Overall Customer Satisfaction Scores:

Faster, more accurate responses led to an increase in customer satisfaction ratings, boosting brand trust and loyalty.

Role:

Project Lead

AI-Powered Customer Service Enhancement