ISPC Verifiable Computation Paradigm

Unlock Decentralized Intelligence for the AI Era

Break the limits of deterministic consensus, enabling AI and complex computations to run trustlessly on-chain.

  • Verifiable Computation Paradigm · Single execution, multi-point verification
  • AI Native · Full AI inference process auditable on-chain

Workbench is only available on desktop browsers. Please visit workbench.weisyn.com on your PC.

Scroll

Paradigm Breakthrough

From 'Repeated Execution' to Verifiable Computation

Traditional blockchains rely on , making it difficult to support AI and complex business logic. WES / Weisyn reconstructs this with : single execution, multi-point verification, enabling complex computations that are both high-performance and provable.

Traditional Blockchain: All Nodes Repeat Execution

Traditional Blockchain: All Nodes Repeat Execution

  • N computations · N costs
  • Deterministic requirements limit complex computations
  • Difficult to support AI and external system calls
WES / ISPC

WES / ISPC: Single Execution + Multi-Point Verification

  • Single execution, controllable costs
  • Verifiable proofs replace repeated execution
  • Support AI inference and long transactions

Core Capabilities

Three Core Capabilities: From Paradigm to Implementation

WES / Weisyn centers on the ISPC verifiable computation paradigm, providing breakthrough capabilities in three dimensions: AI Native, enterprise long transactions, and cost optimization.

ISPC · Layer 1

AI Native: Blockchain Built for AI

Break deterministic consensus, enabling AI inference processes to run trustlessly on-chain.

  • Single execution + multi-point verification · Verifiable proofs replace repeated execution, avoiding redundant AI computations across the network
  • Multiple AI model inference recordable · Support recording key inference steps and parameters on-chain in a verifiable manner
ISPC · Layer 2

Enterprise Long Transactions & External System Coordination

Complete business processes can be safely executed within a single atomic boundary.

  • Cross-system long transaction orchestration · Chain ERP / MES / WMS and other multi-system operations in a single atomic transaction
  • External calls controlled and witnessed on-chain · Database and API calls have clear order and state records on-chain
ISPC · Layer 3

Verifiable Computation Paradigm & Cost Optimization

Center on verifiable computation, reconstructing performance and cost structure.

  • ISPC Verifiable Computation Paradigm · Replace repeated execution with verifiable consensus, seen as the foundation of third-generation blockchains
  • Single execution, controllable costs · Complex computations and external calls execute only once, significantly reducing fees and latency

Get Started with WES / Weisyn Now

Two core entry points: to browse on-chain data, to start building and operating applications

Product Matrix

Product Matrix: Complete Toolchain from Browsing to Building

WES / Weisyn provides a complete toolchain from on-chain data browsing, contract development to AI model deployment, offering a one-stop experience for developers and operators.

On-Chain Browsing & Debugging

weisyn-explorer

On-chain data browsing and debugging entry point.

One-Stop Workbench

weisyn-workbench

One-stop development and operations workbench for contracts and AI models.

Developer Toolkit

Client SDK (Go / JS)

Go / JS Client SDKs for securely integrating WES into applications with verifiable on-chain read/write; other languages can integrate via any HTTP / JSON-RPC client.

Go SDK ↗JS SDK ↗

Contract SDK (Go / TS / AS)

Contract SDK (Go / TS / AS)

Business-semantic contract SDKs with implementations in Go, TypeScript and AssemblyScript (WASM-friendly), helping you build on-chain business logic in familiar languages.

Go Contract SDK ↗JS Contract SDK ↗

Network Status

Network Status at a Glance: The Chain is Continuously Producing Blocks and Running Inference

Real-time data showing the active status of WES / Weisyn Mainnet, proving that verifiable computation is continuously happening on this chain.

Current Block Height
Chain continuously producing blocks, maintaining secure operation
Total On-Chain Transactions
Total number of all confirmed transactions
Average Transactions per Block
Reflects 'ongoing activity' through recent N blocks' transaction statistics
Deployed Smart Contracts
Business logic callable on-chain
Deployed AI Models
Model instances supporting verifiable inference

Use Cases

AI + Enterprise Applications: Not Speculation, But Real Business

With the ISPC verifiable computation paradigm, WES / Weisyn supports diverse scenarios from healthcare to finance, manufacturing to e-commerce, bringing AI decisions and complex business processes on-chain while maintaining verifiability and high performance.

High Sensitivity / High Responsibility Scenarios

Medical AI: Traceable Diagnosis Process

Doctor/system initiates diagnosis
AI model inference + proof generation
WES chain records evidence
Retrospective traceability

Record key AI inference and parameters on-chain, enabling retrospective review of every diagnostic basis in case of medical disputes.

Want to learn more about industry cases and PoC partnership paths?

Developers & Ecosystem

Everything Starts with Developers: 3 Paths into WES / Weisyn

Whether you're a , , or , you can enter the WES ecosystem from a starting point prepared for you.

Use 30-second experience + documentation and community resources to quickly understand the technical and business value of 'verifiable computation'.

Developers

  • 30-second experience of on-chain AI and smart contracts
  • Quick Start · SDK Docs · Example Repositories

Architects

  • System architecture and ISPC technical details
  • Performance and security model evaluation materials

Enterprises & Partners

  • Application scenarios and market positioning
  • Typical cases and PoC partnership paths
30-Second Experience: On-Chain AI Inference + Smart ContractsBased on WES CLI · Verifiable Computation
# 启动本地开发节点
./bin/development --api-only

# 部署示例 AI 模型
wes ai deploy models/examples/basic/sklearn_randomforest/sklearn_randomforest.onnx \
  --name "Iris Classifier"

# 调用模型进行链上推理
wes ai infer \
  --model-hash 0xabc123... \
  --input '{"input": [[5.1, 3.5, 1.4, 0.2]]}'