How Netsmartz designed & development e-commerce websites & Social tracking app for Marine Supplies Business

The Solution

Our team provided the following solutions to the problems:

• E-commerce Websites:Our team focused on creating user-friendly interfaces, incorporating robust features such as secure payment gateways, inventory management, order processing, and shipping integration.
• Performance: Netsmartz team conducted a complete performance analysis of the e-commerce websites, with a specific focus on the product pages. We identified the underlying factors contributing to the slow page speed. We also Minified the CSS and JavaScript files, reducing unnecessary HTTP requests.
• Integration of Social Tracking Application:Our team developed a custom social tracking application that integrates with social media platforms’ APIs to collect and analyze data related to social ads. We designed a user-friendly interface to present meaningful insights and performance metrics to the client.

• Implementation of Generative AI using GPT-3.5 and GPT-4:We used prompt engineering techniques, carefully crafting prompts and inputs to guide the AI models (GPT-3.5 and GPT-4) toward generating accurate and relevant responses.

  Below is an overview of Generative AI implementation process:

  a. Data Collection: We collected a significant amount of data from different sources such as Jamestown’s support tickets, safety data sheets, and technical data sheets. These documents were in PDF format and contained valuable information about products and support inquiries.

  b. Pinecone Vector Database: The collected data was fed into the Pinecone vector database. Pinecone is a powerful vector database that allows for efficient indexing and searching of vectorized data. This indexing enables quick retrieval of relevant information based on user queries.

  c. Slack Wrapper: We developed a wrapper around Slack, an instant messaging and collaboration platform. This integration allowed users to interact with the system by asking questions related to support tickets or product knowledge directly within the Slack environment. This streamlined user experience facilitated easy and convenient access to information.

  d. GPT-3.5 for Content Summarization: We utilized GPT-3.5, a state-of-the-art language model, for content summarization. Given the extensive information contained in support tickets, safety data sheets, and technical data sheets, GPT-3.5 was used to generate concise and relevant summaries of the content. This summarization process helped provide users with condensed and meaningful answers to their queries.

  e. GPT-4 for Response Generation: We incorporated GPT-4, a more advanced version of the language model, for response generation. By inputting the user’s query and prompt into GPT-4, the system was able to generate more sophisticated and contextually appropriate responses. GPT-4’s improved capabilities were expected to enhance the quality and accuracy of the generated responses compared to GPT-3.5.