Most projects start with a story like this: release a white paper first, draw up a roadmap, then announce funding numbers. APRO doesn’t follow this routine.

Before this project took shape, there was an unshakable curse in the community. The code logic was sound, yet the system still crashed. Anyone who experienced it never forgets that feeling—suddenly the price froze, data sources disconnected, someone secretly tampered somewhere, and then all problems exploded simultaneously. Liquidations, panic selling, confidence evaporated in an instant.

The post-mortem analysis is cold and clear: the code was correct, but the data was fundamentally unreliable.

APRO was born out of this pain point. It’s not driven by the idea of “can we design a cooler product,” but by an inescapable question: since blockchain can trust mathematics, how far must we go to trust the data itself?

The people coming together to do this are not the type to love storytelling. Some have years of experience in on-chain infrastructure, some specialize in quantitative trading, and others come from AI and traditional financial data pipelines. Their only commonality is that they have all seen firsthand how “bad data” can bring down an entire system under real market pressure.

Therefore, in its early days, APRO had little external voice. No marketing hype, no public opinion manipulation—mainly focusing on internal rigorous validation: how does data flow from the real world into the blockchain? Where are the typical points of manipulation? How does latency gradually accumulate and suddenly trigger a crisis? And how do seemingly reasonable incentive mechanisms gradually erode the original design intent?