Cancore raises $7.2 million to develop AI-powered trading for tokenized assets
Cancore aims to revolutionize trading with AI-driven automation and cross-chain liquidity solutions for tokenized assets.
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Cancore, a cross-chain settlement infrastructure provider built for Canton Network, has secured $7.2 million in seed funding to develop infrastructure that connects tokenized assets on Canton with liquidity across public blockchain networks.
The round attracted backing from Paper Ventures, dao5, ArkStream Capital, X Ventures, MH Ventures and Proof of Talk.
Cancore is building cross-chain settlement infrastructure for Canton Network, allowing users to exchange Canton-based assets for assets on public blockchains without transferring custody to a centralized exchange or using wrapped tokens. Its atomic-swap mechanism is designed to ensure both sides of a trade settle together or neither does, while users retain control of their signing keys.
The platform supports Ethereum, BNB Chain, Arbitrum One and Robinhood Chain, with additional integrations under development. It has been live on Canton mainnet since April 2026 and is a Featured App on the network.
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By October 1, Cancore had recorded more than $53 million in cumulative volume from over 84,000 trades, alongside more than $538,000 in protocol revenue. ArkStream Capital General Partner Ye Su said Cancore’s existing activity and revenue demonstrated practical demand for its services.
The team is also developing AI-powered trading capabilities through an MCP server that enables users to automate actions and delegate execution to AI agents. Future plans include letting users create agents to run trading strategies, rebalance portfolios and allocate capital across tokenized assets and liquidity venues.
The new capital will support expansion to more blockchain networks and liquidity sources, as well as continued investment in security, settlement and agentic trading infrastructure across its Canton, EVM and Tron connections, the company said.
Disclosure: This article was edited by John Chen. For more information on how we create and review content, see our Editorial Policy.

