The Problem GraphAI Solves
The injection of AI models and agents into DeFi has led to the birth of the DeFAI industry where complex automated financial strategies can be executed with minimal human involvement. However, the DeFAI sector will always remain a niche concern until it can effectively integrate real world assets into its automated on-chain yield strategies. However, doing so requires a level of data availability, diversity, integrity and granularity far beyond that which is available to current DeFAI protocols and apps.
Current on-chain data solutions suffer from:
Fragmented and Unstructured Data: Raw blockchain data is scattered across chains, lacking structure or context, making it nearly impossible for AI models to extract the real-time trading, asset valuation, and compliance insights required to integrate RWAs into DeFAI protocols.
Limited Integration of RWAs into DeFAI Protocols: DeFAI agents and RWA platforms need pre-processed, queryable data to execute autonomous strategies or manage tokenized assets, but current tools fall short, slowing innovation and increasing costs.
Lack of Contextual Understanding: Blockchain data, even when accessible, lacks the relational context needed for meaningful AI analysis, especially as pertaining to RWAs. Transactions appear as isolated events, asset flows are hard to trace, and anomalies go unnoticed, leaving users with shallow insights that fail to capture the full picture.
These gaps leave developers, traders, and institutions reliant on slow, manual processes or incomplete insights, stunting the integration of RWAs into DeFAI protocols.
Last updated