Previously we discussed Velvet cross-chain and the usefulness of an AI Copilot. Now let’s go deeper into its underlying data sources.

The core of the Velvet Unicorn AI Copilot is its own crypto-native model (Velvet-1), trained specifically on-chain and using crypto data.
It’s not powered by a general-purpose large model—instead, it directly pulls real-time on-chain data from multiple chains.
For example, trading volume, changes in open positions, and liquidity depth on Base, Solana, and BNB Chain.
In addition to on-chain data, it also combines social buzz, wallet activity, and historical price trend data. When you ask about a token, it can simultaneously look at on-chain fund flows, social sentiment, and statistical model predictions, outputting a structured analysis report rather than generic advice.
I tried asking about the risks and potential of a certain new token. It directly listed active addresses, liquidity conditions, and simple signals—much faster than manually checking multiple explorers, and more intuitive too.
All data is pulled in real time from the blockchain, with high transparency, so you don’t have to worry about it being second-hand information.
This underlying design makes the Copilot especially effective in a multi-chain environment. It can quickly integrate data scattered across different chains, helps regular traders reduce the time spent looking up data, and makes it easier to spot early signals.
Overall, Velvet brings cross-chain convenience and AI capability together, helping users avoid detours in real execution.
If you want to try it, you can open the Copilot directly in the app to experience the results.