Perceptron announced that it has completed a $6.5 million strategic funding round, with participants including Sigma Capital, Selini Capital, QCP Capital, P2 Ventures, CoinDCX Ventures, and Aethir. The company stated that the funds will be used to launch a data task platform, expand contributor tools and rewards infrastructure, and drive the network toward its goal of 5 million nodes.

The funds will be invested in the data task platform.

Perceptron positions itself as a decentralized AI data network, aiming to enable AI companies to directly submit high-value data requests to the community, rather than primarily relying on centralized data scraping tools or closed data collaborations. The company states that after the new platform launches, AI companies can target datasets from the community, shortening the cycle from submitting a request to delivering data.

Peter Anthony, co-founder and CEO of Perceptron, stated that the company had previously naturally expanded its network to hundreds of thousands of nodes. Following this funding round, the team will launch a data task platform that will allow AI companies to directly access specific datasets from the community.

The network now covers over 800,000 nodes.

Company disclosures indicate that Perceptron's early network had already achieved a certain user base. Its first phase of online proxies reached over 200,000 users on communities such as Telegram and Discord. Subsequently, the network's daily active users increased to over 300,000, with a total of over 807,000 nodes.

These data reflect the platform's desire to attract individual contributors who possess both data and expertise to the network, and to connect data supply with the needs of AI companies through a reward mechanism. The company states that contributors retain control over their data and earnings, and can withdraw their earnings independently.

Targeting decentralized AI data supply

Perceptron believes that current AI data acquisition faces two main challenges: one is the limited coverage of centralized data scraping tools, and the other is the high barrier to entry for collaborating with closed data providers, making it difficult for many AI startups to access data. The company aims to integrate idle bandwidth, datasets, and expertise through a distributed network to create a verifiable data supply layer.

Investors also linked this funding to changes in AI training methods. P2 Ventures stated that Perceptron's model leverages distributed workforce and niche expertise. Aethir co-founder Mark Rydon noted that the cost and quality returns of centralized data scraping are declining.

Additional information:The company stated that its immediate focus remains on launching the data task platform, with further progress expected to be announced in the next quarter. The long-term goal is to build an integrated network covering 5 million nodes.