Newcomers to blockchain are often captivated by decentralization and automation. They feel that putting things on the chain can make them worry-free. But after a while, they realize—blockchains actually know nothing.

What’s the price? Who won the race? What’s happening in reality? These are all black boxes for the chain. All data must be continuously fed from the human world.

That’s why oracles have been the most fragile yet indispensable part of the entire ecosystem since day one. Feed incorrect data, and the contract crashes on the spot; feed it too slowly, and the trading window slips away; feed fake data, and large positions could all be wiped out. Every step is a risk of bloodshed.

There’s a clear idea: rather than piling all the work onto the chain, it’s better to handle the dirty and tedious tasks off-chain. Data collection, cleaning, deduplication, and validation—these complex preprocessing steps—can be handled by AI to identify anomalies in text and images. Only the final clean results are written on-chain for stamping and proof of existence. This approach saves gas costs, reduces errors, and makes the architecture more stable.

In terms of data coverage, this kind of solution is indeed ambitious. It can handle not only token prices and lending rates but also real-world asset pricing, sports event data, and even AI model outputs. It connects thousands of data sources and runs on over forty chains including Ethereum, Solana, and Bitcoin ecosystems. Developers find it convenient—when data is needed, they can call it anytime or subscribe to push updates to contracts, all with just a line of code.

What’s most interesting is that it actually uses AI to do the work, not just hype. Detecting anomalies and blocking spam before it enters is a real application. Currently, it handles over 100,000 data requests per week, and it’s no longer just theoretical.