The Graph veterans set out again, Cambrian targets the financial intelligence layer.

Written by: KarenZ, Foresight News

In Web3, AI agents managing money for people must first have eyes that can understand the on-chain market.

Wallets, strategies, and execution modules are becoming easier to call, but the real trouble is whether the data the AI agent sees is fast enough, comprehensive enough, and can be verified if issues arise.

This is precisely the position Cambrian Network aims to enter.

12 million USD financing: Traditional asset management, crypto capital, and trading funds at the same table

On June 24, Cambrian announced a total financing of 12 million USD, with the latest 6 million USD seed round co-led by Polychain Capital and Franklin Templeton, along with participation from Flow Traders, Selini Capital, Paper Ventures, Proxima, GS Futures, Alumni Ventures, Daedalus, and Nomad Capital.

The Block reported that this round of financing was completed in the form of SAFE with token warrants, and the valuation was not disclosed; Franklin Templeton and Polychain each obtained a board observer seat.

Previously, Cambrian raised 5.9 million USD in a seed round led by a16z CSX in April 2025, with participation from Blockchain Builders and several angel investors from The Graph ecosystem.

What stands out about Cambrian's financing round is not just the amount. It brings together several types of capital: Polychain represents crypto-native capital, Franklin Templeton represents traditional asset management delving into on-chain finance, Flow Traders and Selini Capital are closer to trading and liquidity scenarios, while Nomad Capital (founded by former Binance research head and Launchpad leader Erick Zhang) contributes experience related to exchanges, issuance, and token economics.

The story Cambrian wants to tell is therefore very clear: when DeFi, institutional funds, and AI agents begin to share the same market, the data layer will transform from a backend pipeline to a frontend infrastructure.

A team emerging from The Graph's ecosystem

Cambrian was established at the end of 2024, with a concentrated team background, most of whom have worked in The Graph ecosystem. Founder and CEO Sam Green was a co-founder and CTO of Semiotic Labs, one of The Graph's core development teams, where he led AI and verifiability efforts, and also participated in the design of the trading aggregation platform Odos (developed by Semiotic Labs).

Other core members are certainly focused on "data, verifiability, and DeFi productization": Chief Marketing Officer Brian Berman worked in product marketing at Edge & Node, the founding development team of The Graph; the five technical personnel listed on the official website include those who participated in The Graph's first cryptographically verifiable solution, some who served as data scientists at Edge & Node, others who led The Graph's engineering teams, and one who is a co-founder and engineering head of Odos.

This explains why Cambrian is approaching from the data layer. The bottleneck for AI financial agents often lies not in whether the model can write strategies, but in whether the market state it reads is reliable. If an agent wants to compare yields and risks on Morpho, Aave, and Euler, track DEX liquidity on Solana and Base, and identify wallet behavior and market sentiment, what it needs is not just a few price lines, but a set of financial contexts that can be continuously called by machines.

What is Cambrian? How does it operate?

According to Cambrian's own definition, it is a financial intelligence layer for the digital asset era. The current product form mainly is a unified API that provides real-time and historical blockchain data for institutions, DeFi protocols, and AI agents, covering yields, liquidity positions, risks, trading activities, and market sentiment.

More specifically, the Cambrian API has already listed capabilities including: APY, utilization, borrowing limits, and treasury data on Morpho, Aave V3, and Euler; pool, LP, and trading analysis of DEXs like Aerodrome, Uniswap, Sushi, PancakeSwap, Orca, Meteora, and Raydium; historical wallet balance, holder distribution, OHLCV, and price data on Solana and Base; and off-chain signals provided through Deep42, including sentiment changes on X, alpha clues, and KOL accuracy assessments.

Cambrian plans to achieve low latency, comprehensiveness, and verifiability simultaneously. Currently, Cambrian provides real-time and historical on-chain data through a unified API, covering yields, risks, lending rates, trading activities, liquidity positions, and market sentiment, claiming to directly measure on-chain financial activities via its own blockchain indexing system.

As for the "verifiable" aspect, Cambrian states that the alpha version of its verifiable data network developed in collaboration with a16z researchers has been completed and plans to push for integration with public chains and DeFi protocols in the coming months.

What it truly bets on is the data repricing after machines participate in finance

The most noteworthy aspect of Cambrian is that it places "data services" within a larger change: users of on-chain finance are expanding from humans to machines, from retail and protocol teams to institutions.

Human traders can tolerate some noise in the interface and can filter information based on experience; once AI agents are authorized to adjust portfolios, seek yields, and assess liquidation risks, data errors can directly turn into financial losses and liability issues.

This is also a signal that Franklin Templeton, managing over 17 trillion USD in assets, appears on the leading list. Cambrian claims it will become a key partner in Franklin Templeton's institutional expansion. Franklin Templeton's investment in Cambrian seems less like betting on a standalone API product and more like seeking the underlying controls needed for traditional finance to enter the on-chain market: low latency, auditable, and data layers that can explain sources of risk.

Of course, Cambrian is still in its early stages. It needs to prove three things: first, whether the coverage can keep pace with the fragmentation speed of multi-chain DeFi; second, whether the verifiable network can land without sacrificing performance; third, whether institutions and AgentFi products will be willing to pay sufficiently high fees for such data. The most challenging aspect for data layer companies is often not obtaining the data, but ensuring clients cannot do without it in key decisions.

If the previous round of the crypto cycle fought for liquidity entry, this round of more fundamental competition may occur within the sensory systems of machines. Future financial agents searching for yields, identifying risks, and executing transactions on-chain may see a world of data networks as their first layer of reality.