01
Zk Ecosystem
The current ZK ecosystem faces a few hurdles related to infrastructure that make generating and verifying ZK proofs inefficient. Furthermore verifying Zk proofs on-chain is very in-efficient
02
Computational Complexity
Generating ZK proofs, especially for complex statements, can be very computationally expensive. This means it can take a long time and require powerful hardware, which can be a bottleneck for widespread adoption.
03
Limited Scalability
Current infrastructure might not handle the demands of large-scale ZK-proof generation and verification. If many users are trying to use ZK proofs on a blockchain, the system could slow down significantly.
04
Expertise Required
Implementing ZK-proof systems securely requires a deep understanding of cryptography. This expertise can be scarce, making it difficult for developers to build applications that leverage ZK proofs effectively.
05
Centralized Bottleneck Censorship
Centralized proof markets undermine the core principles of blockchain technology by introducing a single point of failure and potential censorship for ZK proof generation.
ZKML
Current Challenges
01
ZKML is hard for Machine Learning (ML) Folks:
Using ZK to prove ML models is complex and requires a lot of extra effort for developers who aren't familiar with ZK technology. We need easier tools!
02
Missing Python Libraries:
There are no simple tools (like libraries for Python) that make ZKML easier to use. This makes it difficult to connect ML models to the world of web3.
03
Past ZKML Dapps Struggled:
Some attempts have been made to create ZKML applications (like RockyBot) but they haven't been successful because of the above challenges.
04
Verification Needs Work:
Even promising projects like ZKaggle, which enable decentralized computing, still have issues with reliably verifying ZK proofs. This is a hurdle that needs to be overcome.
Easier ZK-proof tools are essential to bridge the gap between machine learning and the decentralized web3 world.
Our Journey
Verisync Labs is tackling a key bottleneck hindering the integration of Machine Learning (ML) with web3 technology. Our approach is two-pronged: firstly, by developing a comprehensive ZKML library compatible with leading platforms like Circom, Noir, and SP1. This library will act as a user-friendly bridge for developers, simplifying the creation of Zero-Knowledge proofs for their ML models. Secondly, Verisync Labs recognizes the limitations of current on-chain ZK proof processing. To address this, we propose a specialized micro-rollup infrastructure dedicated solely to handling ZK proofs. This innovative solution promises to boost efficiency and adaptability by supporting multiple proving platforms, making it easier for developers to leverage the power of web3 for their ML projects. These advancements aim to unlock a new era of collaboration between the worlds of machine learning and decentralized applications.
Who is VeriSync For?
Who are our target audience?
Blockchain Developers
Developers working on ZK proofs within decentralized applications will benefit from Verisync Labs' solutions for enhanced efficiency and scalability.
ML Developers
Developers seeking to enter the web3 space and unlock the potential of decentralized applications for their models.
Blockchain companies
By streamlining ZK proofs, Verisync unlocks benefits for both blockchain companies and developers, paving the way for a future of powerful, secure blockchain applications.
Our Solutions
How does VeriSync Labs Work?
VeriSync tackles the complexities of Zero-Knowledge proofs (ZK proofs) with a three-tier framework designed for ease of use. This framework builds upon itself, offering progressively advanced features
01
VeriSync Micro rollup
This layer focuses on quickly generating and verifying Zero-Knowledge Proofs (ZKPs). Using a micro-rollup system helps pair ZKP requests with the right hardware providers. Updates about transactions are promptly shared on the main network (L1), so we can rebuild the micro-rollup system. Also, we gather proof markets in one place to offer the best prices for users' requests.
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02
Native ZKML Library
Verisync will be building a Python library to simplify ZK proofs for machine learning. This lets ML developers pick their algorithm and the library handles the complex ZK stuff. They can start with simple examples and use Verisync's micro-rollup for faster processing. These proofs can then be used in decentralized applications. Overall, it makes ZK proofs more accessible for ML developers.
03
ZKML Protocols
Verisync's ZK infrastructure bridges the gap between Web2 and Web3 for ML models. This unlocks innovative applications like verifiable DeFi bots with trusted ML models and on-chain credit scoring protocols using secure historical data. By seamlessly integrating Web2 ML into Web3, Verisync empowers a new era of decentralized ML innovation.
