Perceptron Secures $6.5M to Speed Up Decentralized AI Data Infrastructure

The $6.5M strategic round will accelerate what is possible at the intersection of AI and decentralized networks.

Dubai, United Arab Emirates, 30th July 2026 – Perceptron, a decentralized AI data network, today announced the successful close of its $6.5 million strategic round, bringing together leading Web3 investors, trading firms, ecosystem partners, and infrastructure leaders including Sigma Capital, Selini Capital, QCP Capital, P2 Ventures, CoinDCX Ventures, Momentum6, DeFi Capital, Walrus Foundation, Aethir, Colosseum, GuruDev Capital, Tempo Finance, NewTribe Capital, Digital Consensus Fund and CodeCraft Capital. 

The funding will support the launch of Perceptron’s data-questing platform, expand its contributor tooling and rewards infrastructure, and scale the network toward its target of 5 million nodes.

“Perceptron has demonstrated an impressive ability to mobilize a decentralized workforce and build a globally distributed network,” said Nathan Gurr, Investment Analyst of P2 Ventures. “Their model allows them to tap into niche expertise – from doctors and lawyers to native speakers – on demand, from anywhere in the world. We see a significant opportunity to turn that reach into a scalable data and intelligence layer for AI, positioning Perceptron to become a leader in the DeAI stack.”

“Our mission is to create the world’s first decentralized AI data mesh where user interactions directly fuel AI growth,” said Peter Anthony, Co-founder and CEO of Perceptron. “We’ve already shown that the mission can become a reality, as evidenced by our ability to scale to hundreds of thousands of nodes organically. Now, with this funding, we are launching our data-questing platform, which will allow AI companies to commission specific, high-value datasets directly from our community.”

Perceptron’s goal is to make sourcing real-world data fast and accessible enough that AI companies no longer need centralized scraping infrastructure. The network compresses global data collection into a single mesh of idle bandwidth, unique datasets, and domain expertise. This unified format allows both individual contributors and AI companies to generate complete, verified datasets, taking teams from data request to delivered dataset in days.

Perceptron sits between two extremes: centralized scraping tools that are too limited, and closed data partnerships that demand access most AI startups lack. It gives AI companies and everyday contributors data they can work with directly. With Perceptron, contributors own their data and earnings, so they can monetize or withdraw them at any time, without depending on third parties. Perceptron is built for continuous contribution and constant iteration, not a one-time scrape.

“We are witnessing a long-term shift in how AI models are trained. Centralized data scraping is hitting diminishing returns, both in terms of cost and quality,” says Mark Rydon, Co-Founder of Aethir. “Perceptron has solved the distribution problem that plagues the sector.”

The launch of the data-questing platform is central to delivering on this promise at scale. Rather than relying only on organically contributed data, Perceptron is enabling AI companies to commission data directly from its community. This approach shortens the timeline to sourcing high-value datasets and allows the team to focus resources on the community tooling and rewards infrastructure that contributors directly use.

Early traction on the network demonstrates strong demand. In its first phase, live agents reached more than 200,000 users across communities like Telegram and Discord. That number has since grown to more than 300,000 daily active users across a network of over 807,000 nodes. These figures highlight a large audience of users who want to monetize their data and expertise but previously lacked infrastructure designed for them.

Perceptron’s near-term focus is its data-questing platform launch, with further announcements expected next quarter. Longer term, Perceptron is building toward a fully integrated network of 5 million nodes, where everything an AI company needs lives in one place, with no gap between demand and supply.