TokenGPT

Our web application aims to democratize the complex world of Web3 investing. The process of analyzing a new Web3 investment or trade, especially for beginners, can be daunting and time-consuming. It requires a deep understanding of smart contract auditing, tokenomics, emission schedules, market analysis, and target group analysis.

To resolve this, we leverage the power of artificial intelligence and the latest GPT-3.5 model (GPT-4 is coming) to create a user-friendly tool that simplifies this process. Users merely need to input the project's smart contract address. Our system then takes over, pulling and combining data to execute a comprehensive due diligence analysis. For our submitted version it includes reviewing the smart contract, highlighting its risks and generate an overview of the projects market data. In the next stage we will enable the understanding of the tokenomics design, emission schedules as well as allowing a product, market, and target group analysis.

The result is a smooth, easily digestible chat interface that users can use to understand the project's potential in a matter of seconds. But it doesn't stop there. We also offer an interactive layer that simulates a perfect assistant, allowing users to ask specific questions related to their Web3 investment.

This approach to Web3 investment analysis democratizes the process, making it accessible to both experienced investors and beginners alike. It saves time, improves understanding, and ultimately facilitates more informed, sound decision-making.

At the start of our application, we use the gopluslabs.io API, which is fundamental for our smart contract analysis process. When a user inputs a project's smart contract, the GoPlus API is leveraged to analyze the contract and returns a JSON output.

Next, to gather metadata and market data for the token in question, we utilize the Coinmarketcap API. Once we have the outputs from both APIs, we feed these data into the OpenAI API. Here's where we add a proper prompt to enable the GPT model to parse the information efficiently. This AI-powered step is the key to converting the raw data into a user-friendly, digestible analytics report.

This processed output is then displayed on the user's frontend, which we have designed to be interactive and easy to navigate. At this point, the user can interact with the application, ask specific questions, and make informed decisions based on the comprehensive analysis provided.