TransOrg’s CX-LLM

In the rapidly evolving AI world, chatbots are helping diverse business sectors enhance service delivery and customer interaction. Nowadays, LLMs empower chatbots that engage with users naturally. They provide personalized responses and assistance and improve user interactions.

This article explores the transformative impact of LLM chatbots compared to traditional chatbots and explains how TranOrg provided an LLM chatbot for an Airline company.

Chatbot for Businesses

A chatbot is a collaborative application programmed using artificial intelligence technology and specific rules. It is intended for interaction with humans over a textual conversation process and is incorporated with various messaging facilities, thus supporting users in various sectors.

A single bot is available 24/7 and can answer multiple enquiries simultaneously. Chatbots automate repetitive activities, distributing the burden and boosting efficiency. The chatbot cannot handle all types of queries; if necessary, it can transfer the discussion to a human agent. Chatbots are needed for every business website to provide fast and accurate responses.

 

The Chatbot market is increasing every year. According to Statista, the market was 198.8 million US dollars in 2016 and is expected to grow to 1250 million US dollars in 2025.

How are LLM Chatbots Different from Traditional Chatbots?

1. Contextual Understanding

Companies nowadays don’t prefer traditional chatbots as they provide irrelevant and generic responses by following predefined rules and patterns. In contrast, LLM chatbots use Natural language processing language to understand the context of the entire conversation and give more relevant and accurate answers.

According to a study by IBM, traditional chatbots achieve an average accuracy of 65% in understanding user intent, compared to 85% for LLM chatbots.

2. Personalization and Adaptability

The LLM chatbot can memorize past interactions and user preferences. We now have advanced LLM models like the Retrieval Augmented Generation (RAG) LLM model and MemGPT models, which provide more accurate answers through improved retrieval processes. MemGPT(MemoryGPT) also remembers the previous chats.

A survey by Accenture found that 63% of consumers expect personalized recommendations and experiences when interacting with businesses online.

3. Continuous Learning

The beauty of LLM models is that they continuously learn things. They learn from the user feedback and interaction and give the responses accordingly. They can be trained on large amounts of data. But now things have changed even further. LLM knowledge is wider than the knowledge base on which they are trained. They can now access any internet sources and provide sources in the conversations.

According to a Forrester report, organizations that leverage LLM chatbots experience a 30% increase in customer satisfaction over time, driven by continuous learning and adaptation.

4. New advancement

With the advancements in AI, it is expected that LLM chatbots will have text-to-speech or speech-to-speech chats in the future. So, with the help of these chatbots, customers just have to give speech input, and LLM will give answers accordingly. But we now have models like Gemini Pro 1.5 that can understand images and explain things. So, including these models in the chat can improve user experience and save customers time.

TransOrg’s Implementation of CX-LLM for a Leading Airline Company

Understanding the limitations of traditional chatbots, TransOrg provided an advanced GenAI chatbot, CX-LLM, for low-cost carrier airlines. The chatbot addresses general customer inquiries and facilitates flight ticket bookings. It integrates seamlessly with the airline’s existing customer service framework and extends to social media platforms.

To provide accurate and up-to-date data for customer flight bookings, adjustments, and cancellations, CX-LLM uses:

• API to interact with the Navitaire airline management system and pull real-time data.
• User inputs and social media interactions to facilitate bookings.

Continuous Improvement and Adaptation

Through periodic updates and improvements, the chatbot’s model remains current, reflective of user needs, and efficient in its operations.  TransOrg ensured clean data to be fed into the chatbot’s algorithms to enhance the model’s responses. Clean data means removing irrelevant information like special characters, typos, or nonsensical phrases from the data. Techniques like data preprocessing and transformation were applied to ensure the input data was optimally formatted for the chatbot’s algorithms.

Impact of CX-LLM

Significant improvements were noted in the airline’s customer service quality and operational efficiency. Quick responses and a more efficient booking process were the main improvements that enhanced customer satisfaction and engagement. Additionally, by integrating the chatbot with social media platforms, the airline expanded its reach, facilitating easier interaction for customers in the digital spaces where they are most active.

Transorg’s LLM Data Assistant empowers your organization to unlock the full potential of its data. With intuitive interfaces, versatile data processing, and advanced analytics, embrace the future of data analysis where intelligence meets simplicity. It is a distinctive tool for data-driven decision-making, outperforming traditional solutions by seamlessly integrating various data processing formats. It accommodates diverse business needs without compromising performance or user experience. 

When it comes to understanding user queries and facilitating ticket bookings, Transorg GPT is the undisputed champion. It’s the preferred solution among models like OpenAI’s LLM, ensuring efficient and accurate responses every time.

For further information, please write us at: info@transorg.com