When AI Meets On-chain, Let the Model Understand the Language of Transactions
In the past few years, the most common embarrassment for AI in the cryptocurrency field is that the model can answer questions, but the content is often hollow or fabricated. Why? Because of a lack of reliable on-chain data sources.
The advantage of Chainbase lies in organizing blocks, logs, and transaction events into structured data, providing an aggregable and traceable interface. For AI applications, this means that the model can not only guess but also check. For example, if a user asks what a certain address has been doing recently, AI can first call Chainbase to pull this address's cross-chain operations and transactions from the last 30 days, then generate a natural language interpretation, and even present the flow of funds with charts.
Furthermore, developers can organize certain on-chain events into knowledge cards, serving as a reference fact database for AI assistants. This way, when a customer service robot answers how the trading volume trend of this NFT series is, there is real query support behind it, rather than random generation.
This model truly implements the combination of AI and blockchain, enhancing user experience by making answers more credible; on the other hand, it also reduces operational risks for businesses. After all, in financial and asset-related scenarios, the losses caused by fabrications are far more severe than simply not being able to answer.
