The term oracle has already been overused in the crypto circle. Most people's understanding of it is still stuck on the "feeding price machine": Chainlink tells Uniswap how much Ethereum is worth, and Aave decides whether to liquidate your position based on this price.
This "feeding price" model, I call Oracle 2.0. It deals with structured data (numbers). However, if you are a deep DeFi user or pay attention to RWA (real-world assets on-chain), you will find a huge bottleneck: 90% of real-world data is not simple numbers, but unstructured.
For example, if you want to issue a bond on-chain based on 'accounts receivable'. The smart contract needs to know: is this invoice real? Has the goods been received? This information is all in PDF documents, scanned copies, or emails. Chainlink cannot understand PDFs, and neither can Pyth.
This is the most interesting aspect I found while researching APRO Oracle. It proposes a concept of 'Oracle 3.0', with the core mechanism being: off-chain AI computation + on-chain verification.
In simple terms, APRO does not attempt to have the blockchain understand documents by itself (that would be too expensive and slow), but introduces LLM (large language models) as part of the oracle nodes.[4]
Imagine such a process:
The on-chain protocol initiates a request: 'Verify whether the valuation of this property appraisal report exceeds 1 million dollars.'[2]
The APRO node network receives the request. The AI model built into the node begins to work, using OCR (optical character recognition) to read the uploaded PDF report and extract key data.
Note, there is an innovation here. To prevent a single node from making erroneous claims using AI (AI can have hallucinations), APRO adopts a multi-party verification mechanism. Multiple nodes independently run AI models to extract data.
This data is aggregated and compared off-chain through OCMP (off-chain message protocol). Only when the vast majority of nodes' AIs reach the same conclusion will this result be generated as 'proof' and pushed on-chain.
This solves a long-standing problem that has troubled the RWA track: the cost of trust. Previously, putting RWA on-chain either relied entirely on centralized institutions (whatever the trust company said was true) or could not be automated. APRO turns this process into a service that can be called with a single line of code using a 'decentralized AI' approach.
From the technical documents, APRO has specially designed a dual-layer architecture for this mechanism:
One layer is 'Data Push', suitable for low-frequency, high-value data (such as quarterly financial report verification);
One layer is 'Data Pull', suitable for high-frequency data (such as real-time exchange rate fluctuations).
This design is actually very pragmatic. It does not force AI just to ride the wave of AI hype, but treats AI as a 'parsing tool', expanding the oracle's functionality from 'transporting numbers' to 'understanding content'.
Of course, the challenges are also evident. AI
's reasoning costs are much higher than simple API calls, and the hardware threshold for nodes running these models will also increase. This involves$AT
the economic model design of tokens: how to incentivize nodes to run these expensive GPU tasks? According to current information, APRO seems to be trying to use high-value RWA
business revenue to cover these costs, which is a logically sound but executionally difficult path.
If Chainlink is the 'calculator' connecting on-chain and off-chain, then APRO is attempting to become the 'translator' between on-chain and off-chain. In 2025, when RWA is about to explode, this oracle that can 'understand' the real world may be the infrastructure we truly need.
