INTECH developed an advanced AI chatbot solution for a leading global logistics company. Our solution leverages cutting-edge Large Language Models (LLMs) to provide instant, accurate support responses to technical queries—dramatically reducing query resolution time and improving operational efficiency.
About the Client
The client, a major player in the global logistics and cargo domain, operates ports worldwide. They use a TOS’ which provides port logistics solutions to port authorities. When port users encounter technical or non-technical issues with the TOS, they contact the support team, who then investigates or works with the development team to find solutions. This process was slow and time-consuming, causing delays in port operations.
Client’s Challenge
The client faced significant operational challenges in their support system. They aimed to enhance the efficiency of technical support for their Zodiac system, ensuring uninterrupted port operations.
The absence of an automated solution led to the following challenges:
- Response Delays: Time-consuming manual support processes resulted in significant waiting times for issue resolution.
- Resource Strain: Support staff spent excessive time handling repetitive issues, limiting their ability to address complex problems.
- Knowledge Management: Inefficient retrieval and utilization of historical solution data hampered quick resolution of recurring queries.
- Consistency Concerns: Inconsistent response quality negatively impacted operational efficiency and user satisfaction.
The Solution
INTECH solved these challenges by implementing a sophisticated dual-LLM chatbot system, equipped with state-of-the-art AI technology.
Key Features
- Dual LLM Architecture: Leveraged the combined strengths of GPT-3.5 and Llama2 models to deliver accurate and contextually relevant responses.
- Intelligent Query Processing: Utilized a FAISS engine for semantic similarity matching, ensuring rapid and precise query handling.
- Vector-Based Search: Incorporated advanced embedding technology to enhance response retrieval accuracy.
- Adaptive Learning: Enabled continuous improvement through feedback integration, ensuring the chatbot evolves with user needs.
- Flexible Deployment: Provided options for both cloud-based and on-premise hosting to suit varying client requirements.
Tools and Technologies Used
- Python: For core development machine learning, data analytics, data visualization, and programing applications.
- LangChain Framework: Streamlined LLM integration for effective query handling and response generation.
- Streamlit: Delivered a lightweight and scalable user interface framework for intuitive interaction with the system.
- REST APIs: Ensured smooth integration with the Zodiac system, facilitating seamless communication across platforms.
- Vector Database: Enabled efficient query processing and fast information retrieval, enhancing the chatbot’s precision and speed.
Implementation Approach
Step 1: Data Preparation
The system begins by uploading historical data of resolved issues into the Chatbot for training purposes. This data is then extracted and broken down into manageable segments for efficient processing, where each text segment is converted into vector representations (Embeddings) for better processing.
Step 2: Core System Implementation
The system uses two distinct Large Language Models – GPT3.5 and Llama2.
When a user poses a question, the FAISS engine conducts a comparison against the stored text chunks to identify segments with the highest semantic similarity.
These selected segments are then processed by the chosen language model to generate appropriate responses based on the relevant information from the uploaded files.
Step 3: System Refinement
The Chatbot is designed to improve over time through two methods: continuous feeding of relevant data and human feedback mechanisms.
This allows the model to be refined incrementally, ensuring better accuracy and more relevant responses as the system matures.
Business Impact
By addressing critical pain points and delivering measurable improvements, INTECH has delivered an efficient solution that effectively supports the client’s unique needs.
The AI-powered chatbot delivered significant improvements across multiple dimensions:
- Ensured consistent, data-driven support to user queries.
- Provided 24/7 support, ensuring uninterrupted technical assistance.
- Delivered immediate answers for common queries, cutting response times dramatically.
- Allowed the support team to focus on more complex, high-priority issues by automating repetitive tasks.
This solution not only enhances client’s operational efficiency but it also prepares them for future growth through its ability to continuously learn and improve from data feedback mechanisms.