Agent Efficiency in B2B Customer Support with Generative AI

Background

HeapTrace Technology, a leading IT services provider, partnered with a US-based B2B company that manages a complex ecosystem of customer support and business management systems. The company’s customer service agents were facing significant challenges in efficiently managing customer queries across multiple systems. They needed an intelligent assistant that could help streamline workflows, increase productivity, and provide accurate, contextually relevant information to customers.

The client approached HeapTrace Technology to develop an AI-powered agent assist system that could quickly address customer queries using a search-based questionnaire application. The goal was to improve response times, reduce manual efforts, and ensure that agents could provide accurate answers from a vast pool of information.

Challenges

1. Multiple Systems Integration: The B2B company operated across a range of business and support platforms, making it difficult for agents to find the necessary information quickly.
2. Data Volume: The client had a massive amount of customer data, product details, and documentation that needed to be searched and referenced efficiently.
3. Accuracy and Relevance: It was crucial for the AI-powered assistant to provide highly accurate and context-relevant answers to the agents' queries to maintain the company's high standards of customer service.
4. Latency and Response Time: The solution needed to deliver results in real time to ensure fast response times during live interactions.

Solution

HeapTrace Technology implemented a Generative AI Agent Assist System using cutting-edge language models and AI tools. The system was designed to enable customer service agents to input queries via a search-based questionnaire and receive contextually accurate, real-time responses. Key technologies used include:

1. Python Langchain: We used Python Langchain to facilitate the integration of language models, allowing the agents to pose complex, context-rich questions and receive natural language responses.
2. HuggingFace Models: HuggingFace's pre-trained models were incorporated to enhance the AI’s ability to understand and process language accurately.
3. ChatGPT and OpenAPI Integration: By integrating ChatGPT and OpenAPI, the system was able to generate human-like responses and provide the agents with highly accurate answers based on the context of the customer’s query.
4. FAISS for Efficient Data Storage and Retrieval: To manage the vast amounts of data, we employed FAISS (Facebook AI Similarity Search) for vector storage, ensuring quick access to relevant documents and data points.
5. AWS S3 for Scalable Storage: AWS S3 was used to store structured and unstructured data securely, providing scalable storage that could grow with the client’s needs.

Implementation

The Generative AI system was designed in several key steps:

1. Data Vectorization: All the customer and business data was converted into vector representations using multiple vectorization mechanisms. This allowed for faster and more accurate search and retrieval processes, ensuring that the AI system could navigate large datasets efficiently.
2. Search-based Questionnaire: We implemented a questionnaire interface where agents could input queries in natural language. The AI assistant would then parse the query, search the data, and return the most relevant responses.
3. Language Model Integration: The language models, including ChatGPT, were configured to understand complex queries and provide human-like, contextually accurate responses to agents’ questions.
4. Real-time Assistance: The system was designed to operate in real-time, providing near-instant responses to ensure that agents could address customer queries without delays.
5. Continuous Learning: The AI system was built with continuous learning capabilities, improving its performance over time as it processed more data and user interactions.

Results

The implementation of the Generative AI Agent Assist System led to significant improvements for the client, including:

1. Increased Efficiency: Agents were able to handle more customer queries in less time, thanks to the AI’s ability to retrieve accurate information quickly.
2. Improved Accuracy: The integration of ChatGPT and OpenAPI ensured that agents received highly accurate, context-relevant answers, reducing the risk of misinformation or incomplete responses.
3. Enhanced Customer Satisfaction: Faster, more accurate responses led to improved customer satisfaction and a reduction in query resolution times.
4. Scalability: The system was designed to scale with the client’s growing data needs, ensuring long-term usability as the company expands.
5. Cost Reduction: By reducing the manual effort required by agents and automating many repetitive tasks, the client experienced a noticeable reduction in operational costs.

Conclusion

HeapTrace Technology successfully implemented a generative AI-powered agent assist system for a B2B company, solving the key challenges of data retrieval, system integration, and response accuracy. By leveraging advanced AI models, vectorization techniques, and cloud storage, the solution transformed the client’s customer support operations, leading to faster query resolutions and higher customer satisfaction. This case study highlights HeapTrace’s expertise in using AI technologies to address real-world business challenges, offering scalable, intelligent solutions that deliver tangible results.

‍