In crypto finance, prices are usually just numbers. But when we bring real-world assets (RWA) onto the chain, things become much more complicated.

Take commercial real estate as an example. The value of a building depends not only on its location but also on whether the lease agreement includes an "early termination clause." For startup equity valuation, looking at financial statements isn't enough; you also need to consider risk warnings in audit reports. Traditional oracles can only carry digital data but can't interpret deeper information.

APRO Oracle is trying to change this situation—it aims to improve the "resolution" of on-chain asset pricing.

Its core weapon is semantic analysis using large language models (LLMs). APRO doesn't just capture a final valuation; it understands the logic behind that number. Upload a PDF legal document? Its node network will break it down line by line, identifying hidden clauses that might affect risk levels. This way, on-chain synthetic assets or lending protocols can perform multi-dimensional risk control instead of relying solely on a single price signal.

This approach effectively upgrades the oracle from a simple "quote tool" to an "assessment tool." In the APRO network, multiple AI models with different algorithms analyze a document simultaneously, and only when their understanding reaches consensus does the data get uploaded to the chain. This consensus mechanism is much more reliable than traditional single-point data feeds.