Polychain Capital Leads $10M Seed for VeryAI’s Human-Centric Internet Vision

Proof of reality platform, VeryAI, said it has raised $10 million in seed funding led by Polychain Capital to scale its infrastructure.

The Death of the CAPTCHA?

VeryAI, a “proof of reality” platform, announced March 12 that it has raised $10 million in seed funding. The round was led by Polychain Capital, with participation from the Berggruen Institute and Anagram. With this fresh capital, VeryAI plans to scale its proof of reality infrastructure and develop further tools to ensure the future of the internet remains human-centric.

The capital injection coincides with the debut of VeryAI’s biometric verification system, which turns a standard smartphone camera into a high-precision palm scanner. Unlike legacy systems that require specialized hardware, VeryAI’s software-only approach aims to provide a frictionless, “human-only” layer for the internet.

The rise of sophisticated AI has rendered traditional security measures like facial recognition, two-factor codes, and CAPTCHAs increasingly vulnerable. Industry data highlights a grim trend: While attackers are facing more complex defenses, their efficiency is rising. The average time to compromise a system has dropped significantly, with breaches reportedly now occurring in as little as 48 minutes.

“Privacy is a human right. But deepfakes and synthetic content present weaknesses that current systems simply can’t keep up with,” said Zach Meltzer, founder and CEO of VeryAI. “We are replacing outdated methods with solutions that are accurate, private and frictionless.”

Meltzer, who previously scaled the identity platform Galxe to 34 million users, is joined by Chief Science Officer Hua Yang, a leading researcher in palm biometrics with more than 50 patents.

Unparalleled Privacy and Security

VeryAI’s technology relies on the unique, rarely exposed patterns of the human palm. As per a media release, a single hand scan provides a false acceptance rate of 1 in 10 million, which is 10 times more accurate than most facial recognition systems. When a user scans both hands, the false acceptance rate drops to approximately 1 in 100 trillion.

Crucially, the system does not store actual images of palms. Instead, it generates irreversible feature representations that cannot be reconstructed into an image, ensuring user data remains private even in the event of a security breach.

The platform is built on the Solana blockchain, leveraging the network’s high speed and low transaction costs. Solana co-founder Anatoly Yakovenko has also joined the venture as an angel investor. To maintain total anonymity, VeryAI utilizes zero-knowledge proofs (ZKP). This allows a user to prove they are a unique human to a decentralized app ( dApp) or a fintech platform without revealing personal identity or sensitive biometric data.

The B2B model is already targeting crypto exchanges and fintech firms that face the highest risks from AI identity theft. Olaf Carlson-Wee of Polychain Capital noted that VeryAI closes the gap in accuracy and accessibility that previous biometric solutions missed.

To stay ahead of the AI arms race, VeryAI is also partnering with Northwestern University. Under the guidance of advisor Matthew Groh, the company is launching a research collaboration to study human resilience to deepfakes and improve the detection of synthetic media.

FAQ ❓

• What is VeryAI’s recent funding achievement? VeryAI has raised $10 million in seed funding to enhance its proof of reality infrastructure.
• What innovative technology has VeryAI introduced? The company launched a biometric verification system that transforms smartphone cameras into high-precision palm scanners.
• How does VeryAI’s approach differ from traditional security measures? Unlike outdated systems, VeryAI’s software-only method aims to provide a frictionless, human-only layer for online security.
• What is the accuracy rate of VeryAI’s palm scanning technology? A single hand scan offers a false acceptance rate of 1 in 10 million, significantly outperforming most facial recognition systems.