Smart Chatbot for Instant Shipping Cost Checking

In today's digital era, the trend of online shopping has increased significantly, directly driving the rise of shipping activities through various courier services. In this context, the ability to quickly and accurately determine shipping costs becomes a crucial component in the world of e-commerce and logistics. One innovative solution that addresses this need is the AI-powered Shipping Price Checker chatbot application.

What is the Shipping Price Checker?

Shipping Price Checker is a chat-based application that utilizes AI technology powered by Large Language Models (LLM), specifically using Retrieval-Augmented Generation (RAG) techniques. It is also integrated with the RajaOngkir API to retrieve up-to-date shipping cost data from various courier services in Indonesia. This application helps users check shipping fees from one location to another across Indonesia easily, intelligently, and with relevant output.

 

Challenge: Navigating the Complexity of Shipping Costs

Users looking to estimate shipping costs often face several common challenges:

• Shipping rates frequently change

• Many courier options (JNE, SiCepat, NINJA, etc.)

• Input formats vary across platforms

• Typos in city names can lead to errors

• Complicated weight and value conversions

• No direct comparison between courier services

Solution: Smart Chat-Based Price Checker

Using a RAG-based chatbot approach, these challenges can be effectively addressed. The system is designed to respond to natural language questions such as, “What’s the shipping cost from Jakarta to Surabaya for a 2kg package using Regular service?”

The application workflow is smooth. Users submit a query through the chatbot, which is then sent to the LLM. The LLM uses function calling to interact with external logistics APIs and retrieve information from its data sources. The LLM then processes this information to generate a comprehensive response for the user.

Key features of this AI solution include the ability to access real-time data from external sources, intelligently handle city name typos, and smartly process package weights, such as automatically converting from kilograms to grams or estimating weight for general terms like "small package." Thanks to LLM technology, all this information is presented in a natural and easy-to-understand response.

 

Conclusion

With the introduction of the Chatbot-based Shipping Price Checker, users can now easily get accurate, fast, and interactive shipping cost estimates without the need to open multiple courier websites or apps. The integration of Large Language Models, Retrieval-Augmented Generation techniques, and the RajaOngkir API enables this chatbot to understand the context of questions, handle input errors like typos, and automatically provide cost comparisons from various courier services. This solution not only enhances the user experience in accessing shipping cost information but also supports operational efficiency in the ever-growing world of e-commerce and logistics.

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