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Bluwhale Announces $85M in Token Purchase Commitment for $100M Total Funding

January 28, 2025
in News
Reading Time: 2 mins read
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Quick take:

  • The fundraising follows Bluwhale’s $7 million seed round announced last March.
  • The company plans to use the new investment to expand its Web3 intelligence layer further across multiple L1 and L2 blockchains. 
  • Bluwhale is teaming up with Caldera to build Oceanum, a custom Zk-powered L3 built on Arbitrum to decentralise AI workloads.

Bluwhale has announced a new fundraising round, which includes $85 million from token purchase commitments and $8 million from grants and node sale proceeds. 

The fundraising attracted participation from Movement Labs and Arbitrum, as well as Cointelegraph, SwissBorg, Awaken Finance, CoinBureau, Altcoin Daily, Master Ventures, DWF Labs, Baboon Ventures, Cryptonite Capital, Hub71, NewTribe Capital, Vitality Ventures, Citizen Capital, Castrum Capital, Elevate Ventures, Common Wealth Fund, AlphaCrypto Capital, Faro, Kahuna, Moon Capital, EVO, and London Real Ventures, the company said in press material shared with NFTgators.

Bluwhale also boasts backing from leading Web3 VCs including SBI Holdings, Primal Capital, NxGen Ventures and Spire Capital, as well as, Decima Fund (the collaborative fund of Animoca Brands, Gumi Cryptos, SBI and MZ Crypto).

The fundraising brings the total raised to $100 million, following a $7 million Seed round announced last March.

Bluwhale is teaming up with Caldera to build Oceanum, a custom Zk-powered L3 built on Arbitrum to decentralise AI workloads. The company plans to use the new investment to expand its Web3 intelligence layer further across multiple L1 and L2 blockchains. 

Commenting on the announcement, Han Jin, CEO at Bluwhale said in a statement: “While individual building blocks to power decentralised AI on the blockchain are available, they are mostly fragmented across platforms and chains. Our L3 in collaboration with Caldera, will deliver data contextualisation and orchestration of the scattered blocks to create the most optimal and secure aggregation layer for AI agents, models, and LLMs to plug into similar to a multi-functional RAG system.”


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