Decentralized Network AI-Powered Infrastructure

Revolutionizing AI Infrastructure
Through Decentralization

Run, Train, and Monetize AI Securely — Without Gatekeepers.

The Future of AI Infrastructure

See the difference between centralized and decentralized AI infrastructure

Centralized AI

Traditional
Data Center
  • Single point of failure
  • Vendor lock-in
  • Limited scalability
  • Data privacy concerns

Decentralized AI

ShoniX AI
GPU Node AI Node Data Node Train Compute
  • Distributed & Resilient
  • Truly decentralized
  • Unlimited scalability
  • Full data ownership

The AI Monopoly Crisis

Current AI infrastructure is controlled by a few tech giants, creating barriers that limit innovation and accessibility.

High Costs

Expensive compute and API costs limit accessibility for smaller organizations

Vendor Lock-in

Proprietary systems trap users in closed ecosystems

Privacy Concerns

Sensitive data processed on centralized servers without control

Compliance Issues

Regulatory challenges with data sovereignty and governance

Introducing ShoniX AI

A decentralized AI infrastructure that puts control back in your hands

Private AI Workloads

Run AI models on your own infrastructure with complete privacy and security

Train AI on Own Data

Keep your proprietary data secure while training custom AI models

GPU Marketplace

Access distributed GPU resources or monetize your unused compute power

Microservices API

Modular AI services that scale with your needs

Model & Data Monetization

Earn revenue by sharing your AI models and datasets securely

Personal AI Assistant

Deploy your own AI assistant with complete data ownership

Technology Advantage

Built on cutting-edge decentralized technologies for maximum security and efficiency

Smart Contracts

Automated, trustless execution of AI workloads and payments

Federated Learning

Collaborative AI training without exposing raw data

Privacy Layer

Zero-knowledge proofs and encryption for complete privacy

How Decentralized AI Works

Leveraging Federated Learning and Blockchain for Privacy-Preserving AI

1

Train Locally

AI models train on your own data across distributed nodes without exposing raw data to anyone

2

Aggregate Securely

Model updates are aggregated using encrypted computation without revealing individual data patterns

3

Privacy Preserved

Your data never leaves your infrastructure. Zero-knowledge proofs ensure computation integrity without data exposure

Why Choose ShoniX AI?

Feature ShoniX AI AWS Open AI Akash/Golem
Decentralized
AI-Focused
Data Privacy
Cost Efficiency

Revenue & Ecosystem

Multiple revenue streams for all ecosystem participants

GPU Providers

Earn passive income by sharing unused compute resources

Up to 80% APY

Developers

Monetize AI models and applications on the marketplace

Revenue Share

Enterprises

Reduce AI infrastructure costs by up to 70%

Cost Savings

Roadmap Timeline

1

Foundation

Core infrastructure development and MVP launch

2

Market Entry

Beta testing with enterprise partners and community growth

3

Ecosystem

Full marketplace launch and developer onboarding

4

Dominance

Global expansion and enterprise adoption at scale

Meet the Founder

Shubham Saraswat

Shubham Saraswat

Founder & CEO, ShoniX AI Pvt. Ltd.

Shubham Saraswat is a new-age entrepreneur shaping the next digital revolution through AI, Blockchain, and Web 4.0 convergence. As the Founder and CEO of SN Digitech Pvt. Ltd., he has built and scaled the company from the ground up into a global technology player known for innovation and excellence. With a strong foundation in software product engineering, digital transformation, and AI-driven innovation, Shubham has led successful deliveries for clients across the US, UK, Europe and Canada. His leadership blends technical expertise with strategic vision, focusing on building scalable, decentralized AI systems that empower enterprises and creators alike.

The Future is Decentralized

Join the revolution and be part of the next generation of AI infrastructure. Early partners get exclusive access and preferential terms.