Everlyn

Avalanche

Crystalfall

Solana

NEAR

PublicAI

MH Ventures

Nesa means miracle, in tribute to the golden age of AI that we are living in. Sufficiently advanced technology - like trusted AI - is often indistinguishable from magic.

Nesa is the lightweight Layer-1 executing critical AI inference on queries that require a high degree of privacy, security, and trust using advanced methods on-chain, including zero-knowledge machine learning (ZKML), split learning (SL), and more.

Nesa was created as an alternative to ChatGPT and other inference platforms today that are centralized and controlled by major players. These platforms have zero visibility or accountability in their output on your critical transactions and provide zero privacy in your data and results. This is a big problem in AI. 

Limitations in Centralized Architecture. While commonly used, centralized processing architectures pose significant risks related to data privacy, computational bottlenecks, deterministic output, and single points of failure. The prohibitive cost and scarcity of high-performance computing resources, such as advanced GPUs, prevent the mass adoption of the only counter option to centralization, which is open-source, decentralized AI. These challenges further limit the ability to contribute to developing, training, fine-tuning, and executing AI models. This severely interdicts the adoption of state-of-the-art (SOTA) AI models for enterprises and developers, as well as shared research worldwide.

The challenges around security further impede collaborative efforts to execute AI today. Techniques like continuous adaptation and domain adaptation offer partial solutions by enabling model sharing without direct data exchange. However, these methods are susceptible to backdoor attacks and often result in suboptimal model performance due to the limitations of semi-supervised or unsupervised fine-tuning. Collectively, these issues have real-world implications for businesses that require critical inference. In the financial domain, for example, where institutions analyze vast amounts of sensitive transactional data, traditional centralized systems fail to meet the strict requirements for data privacy and security, while existing decentralized systems fail to achieve the need for both confidentiality and verifiability.

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Nesa

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