Over the past few years, many people have been talking about AI infrastructure, but the real issue isn't actually the number of models—it's how to connect these capabilities together. As models proliferate, fragmented interfaces and high integration costs paradoxically raise the barrier to entry for developers.

@dgrid_ai is precisely aiming to solve this problem. The project unifies AI RPC interfaces, integrating different models and AI Agents into a single network, allowing developers to call multiple AI capabilities within one system.

What's particularly interesting is the intelligent routing mechanism. When users request AI services, the system automatically selects the most suitable model based on cost, performance, and capability to execute the task. This approach is somewhat analogous to a traffic scheduling system in the AI world.

To ensure result reliability, the network introduces a Proof of Quality mechanism that verifies AI inference results, making the execution process traceable and auditable.

Looking at the entire structure from a Web3 perspective, DGrid is more like building an AI network layer. As increasingly more AI applications need to run on-chain, this decentralized inference network could become an important bridge connecting models and applications.

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