Summary
Custobar is a marketing platform tailored for e-commerce and omnichannel retail businesses, facilitating the integration of customer data from multiple touchpoints and automating personalized customer experiences across various channels.
The objective of this project is to create a service utilizing Large Language Models (LLMs) to convert user-provided natural language queries into Custobar's query language. This innovation will enable users to engage with Custobar's data retrieval system using everyday language, thereby improving accessibility and user experience.
Description
Key Objectives:
- Natural Language Processing Integration: Implement an LLM capable of understanding and processing user queries expressed in natural language.
- Query Translation: Develop a mechanism to accurately translate these natural language queries into Custobar's query language, ensuring that the translated queries are syntactically correct and semantically meaningful.
- System Integration: Seamlessly integrate the translation service with Custobar's existing data retrieval API, allowing for efficient execution of translated queries.
Expected Outcomes:
- Users will be able to retrieve customer data by inputting queries in natural language, which the system will translate into Custobar's query language for execution.
- The system will support a variety of query types, including filtering customer data based on attributes such as email, last name, and marketing permissions.
- The integration will enhance the usability of Custobar's data retrieval capabilities, making it more accessible to users without technical expertise in query languages.
By implementing this LLM-powered translation service, Custobar aims to make its data retrieval process more intuitive, thereby improving user engagement and satisfaction.