ZippyChain

Zippy fixes bad data so robots work. 73% of automation fails from bad data—we deliver reliable AI.

Traction & Status

Engine Built: Over 2 years, we’ve developed Zippy’s high-performance distributed data engine.

Prototype Complete: The AI-assisted annotation model is built and has been trained on 8 months of global datasets from our partner, a leading AI data company.

Funding Needs: We require funding to complete the MVP—estimated 3 months from funding—and launch the product in Q2 2026.

Design Partner: Our training partner is expected to be the first customer.

Pipeline: Early conversations are ongoing with Google DeepMind, NVIDIA (Project Groot), and Kepler Robotics.

ZippyChain is a high-performance, parallel EVM blockchain purpose-built to power the next wave of intelligent decentralized applications. By uniting Web2 enterprises with Web3 AI-native innovators, ZippyChain offers a scalable, cost-efficient infrastructure layer capable of supporting real-time autonomous agents, complex AI models, and large-scale enterprise data flows—all at 25,000 TPS and near-zero fees.

Outlined below is ZippyChain’s core market segments, strategic integrations, and the investment opportunity in enabling the AI-powered, decentralized future of the internet.

AI Agent Infrastructure: 1-Click Deployments for dApps & Enterprises
ZippyChain offers a unique proposition to both developers and enterprises: ready-made, AI-native agents that can be deployed directly on-chain with a single click.

Example Use Cases:
- E-Commerce: A customer support agent that learns from behavior and adapts its tone over time.
- Finance: An investment bot that reallocates funds based on market sentiment data.
- Logistics: A supply chain agent that tracks perishable goods and executes emergency rerouting if spoilage risk is detected.
- Entertainment: An AI screenwriter agent that generates short-form video concepts based on trending IP.

These agents can be monetized via ZippyChain’s micro-fee structure, creating new, sustainable business models across industries.

Strategic Advantage

Feature ZippyChain Advantage
Speed 25,000+ TPS parallel EVM execution
Cost Sub-cent gas fees allow true microtransactions
AI-Native Optimized for autonomous agents and ML model execution
Interoperability Bridges to Ethereum, Solana, Cosmos, and Layer 0 frameworks
Deployment One-click smart agents and dynamic NFT deployment

Developer Migration Opportunity
ZippyChain is actively recruiting Web3 developers, AI engineers, and dApp founders to migrate existing projects or launch new ones on the platform. Key incentives include:
- Developer grants & ecosystem funds
- Toolkits for AI agent deployment
- Parallel EVM compatibility with minimal migration friction
- Shared infrastructure with top AI projects (e.g., Fetch.ai, SingularityNET)
- Market Segments can Include

Strategic Opportunities and Possibililities:
ZippyChain is designed to address bottlenecks in today's blockchain landscape—particularly Ethereum congestion—by offering parallel execution and seamless interoperability across Web2 and Web3.

1. Enterprise Supply Chain & Logistics
Web2 Targets: Walmart, Nestlé, Unilever
Web3 Partners: Fetch.ai, Ocean Protocol

Use Cases:
- AI agents for autonomous sourcing and pricing negotiations
- Real-time IoT tracking with anomaly detection
- Tokenized supplier audits for compliance and ESG reporting

Opportunity: ZippyChain’s high throughput allows autonomous agents from Fetch.ai to operate at enterprise scale, reducing latency in B2B procurement systems.

2. Consumer Loyalty & Brand Engagement
Web2 Targets: Starbucks, Nike, Coca-Cola
Web3 Collaborators: ALI (Artificial Liquid Intelligence), SingularityNET

Use Cases:
-1-click deployable AI agents for brand interaction and NFT evolution
- Cross-chain loyalty points bridged to Ethereum and Solana
- AI-curated customer rewards and product suggestions

Opportunity: ZippyChain makes it possible to launch dynamic, AI-powered loyalty NFTs that evolve with customer behavior—driving higher engagement at lower cost.

3. Media & Entertainment
- Web2 Targets: Disney, Netflix, Spotify
- Web3 Collaborators: Cortex, Velas

Use Cases:
- Smart contract-embedded AI content recommendations
- AI-generated scripts and tokenized IP with on-chain royalties
- High-frequency NFT drops powered by fast finality

Opportunity: Studios and artists can leverage one-click AI agent deployment to create custom content generators or interactive NFT storylines—no blockchain coding required.

4. Financial Services & Payments
Web2 Targets: PayPal, Visa, Mastercard
Web3 Collaborators: Jarvis Network, Augur

Use Cases:
- AI fraud detection with federated learning
- Microtransaction settlements (e.g., B2B, remittances)
- AI-powered DeFi strategy execution

Opportunity: ZippyChain enables algorithmic DeFi strategies and prediction markets to run on an EVM with ultra-low fees and near-instant settlement.

5. Healthcare & Pharma
Web2 Targets: Pfizer, Mayo Clinic, CVS Health
Web3 Collaborators: Numerai, Deeper Network

Use Cases:
- Zero-knowledge machine learning for diagnostics
- AI model training on anonymized health data via Ocean Protocol
- Drug traceability on-chain with secure IoT data integration

Opportunity: Healthcare platforms can now run privacy-preserving AI agents that interact with ZippyChain to provide diagnostics or alerts—all deployable with a single click.

Billions are being poured into warehouse robots and AI, with shipments growing 50% annually. The promise: faster fulfillment, fewer errors, and operations that outperform humans.

But up to 73% of projects fail to hit ROI—not because of the robots, but because the data behind them is flawed. Missing labels, inconsistent mapping, and bad annotation leave AI blind to the real world, turning ambitious automation into costly downtime and idle machines.

Zippy fixes poor data for industrial AI and robotics by combining AI-assisted annotation with a distributed, verifiable network of contributors. Our system labels and validates datasets quickly, accurately, and at scale, turning messy, inconsistent, or incomplete data into reliable inputs for robots and automation systems. By continuously learning from expert corrections, Zippy improves over time, reducing costs and time while ensuring high-quality data. This enables companies’ robots and AI systems to finally perform as intended, unlocking real ROI and making automation scalable, dependable,

Ethereum

Monad

Ethereum - is the defacto for finanicial applications. Its single-thread confirmations are not suitable for AI agent payment, onchain AI (fine tuning, decentralized AI), its layer 2s are segregated.

Monad - lacks parallel EVM design which will impact cost as you'll need bigger machines to support workloads rather than distributing it across multiple machines

Revenue will come from Zippychain utilization for:
- transactions
- dApps and DeFi
- AI Agents
- Providing Liquidity

We project 2M transactions by Q3 2026 which can lead to millions in revenue.

Tokenomics model where revenue will be derived from on-chain utilization from transactions, dApps/DeFi, AI Agents and as a Liquidity provider

R&D, S&M and G&A

SAFT agreement which includes discount on tokens at TGE (which is planned for end of Q3 25)

Technology and Development: The platform is still evolving, and there’s a risk that it might not deliver on its promises or face delays in key milestones, like the mainnet launch.

Market Adoption and Competition: ZippyChain could struggle to attract users and developers compared to more established blockchains.

Security and Governance: While the platform has security measures in place, blockchain projects are always at risk of hacks. The decentralized governance model might also slow decision-making.

Token Volatility: ZPT token prices could be highly volatile. The token’s value is closely tied to the platform’s success, so if adoption is slower than expected, the token’s value could fall.

Execution Risk: The development team’s ability to execute its vision is crucial. Internal challenges could slow progress.

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