Dataline Raises $10 Million to Fuel Decentralized Data Network for Artificial Intelligence Agents

Dataline has successfully secured $10.6 million in new funding to expand its decentralized data network tailored specifically for artificial intelligence agents. The recent financing round was co-led by venture capital firms Lattice and Dispersion Capital, with additional backing from Symbolic Capital. The capital infusion will support the ongoing expansion of the company marketplace and routing architecture designed to supply machine-readable feeds to autonomous software applications.

Building a Marketplace for Agent-Payable Data

Operating as a two-sided marketplace, Dataline allows publishers to offer data feeds that autonomous agents can access on a pay-per-call basis using the x402 payment protocol. The platform functions as a routing layer, supplying application programming interface responses that feature an artificial intelligence confidence score. This score evaluates source reliability, data freshness, and overall consistency to help automated systems make informed decisions.

According to company figures, the platform has processed 19.4 million on-chain transactions to date while building a global community exceeding 300,000 participants. The startup also introduced its inaugural cohort of artificial intelligence data launch partners, bringing together various decentralized web and software projects aimed at establishing high standards for machine learning information sources.

Expanding Access for Ecosystem Builders

As part of its growth strategy, Dataline is offering complimentary network access to developers building on Base and Robinhood Chain. Company communications highlight that this infrastructure provides scheme-normalized feeds encompassing pricing, funding data, user positions, and prediction markets to streamline agent-native execution.

• Co-Founder Nuo Shi noted that clean and reliable data remains the essential foundation for autonomous software operations.
• The routing layer helps machine learning agents request external information when standard access methods fall short.
• Industry research indicates that data readiness continues to represent a primary hurdle for enterprise adoption.

Market analysts note that clean pipeline management and structured inputs are increasingly critical as organizations transition from experimental projects to scaled deployments. By addressing these foundational information barriers, the firm aims to position its infrastructure as a standard utility for autonomous digital agents navigating complex digital ecosystems.