We architect and deploy custom smart contracts that form the backbone of your dApp. Our development process ensures security-first design, gas optimization, and full audit readiness from day one.
Zero-Knowledge Machine Learning Library Development
Smart Contract Development
Secure, production-ready smart contracts built by Web3 experts.
- Custom Logic: Tailored
Solidity 0.8+contracts for DeFi, NFTs, DAOs, and enterprise use cases. - Security Foundation: Built with
OpenZeppelinstandards and formal verification patterns. - Deployment & Management: Full lifecycle support from testnets to mainnet, including upgradeable proxy patterns.
Deliver a secure, auditable, and maintainable codebase that scales with your protocol's growth, reducing time-to-market by weeks.
Core Development Capabilities
We architect and deploy production-ready zero-knowledge machine learning systems that are private, verifiable, and performant. Our full-stack approach ensures your ZKML application is built on a secure, scalable foundation.
Proving Infrastructure
Deploy scalable proving backends with GPU acceleration. We implement batching, recursion, and distributed proving to achieve sub-2 second proof times for complex models.
On-Chain Verification
Integrate verifier smart contracts with gas-optimized Solidity or Cairo. We ensure cost-effective verification on Ethereum L1, L2s (zkSync, Starknet), and other EVM-compatible chains.
Security & Auditing
Rigorous security review of circuits and smart contracts. Our process includes formal verification of constraints and audits following OpenZeppelin standards to eliminate vulnerabilities.
Why Partner for ZK-ML Library Development
Building a production-ready ZK-ML library requires deep expertise in cryptography, machine learning, and blockchain systems. We deliver battle-tested, auditable code that accelerates your time-to-market and ensures security.
Gas-Optimized Circuit Design
Our engineers specialize in writing efficient ZK circuits for ML operations (matrix multiplications, activations) that minimize on-chain verification costs by up to 70% compared to naive implementations.
Production Deployment & Scaling
We provide end-to-end support from library design to mainnet deployment, including prover/verifier optimization, multi-chain compatibility (EVM, Starknet, zkSync), and horizontal scaling strategies.
Structured Development Packages
Compare our structured packages for integrating a production-ready Zero-Knowledge Machine Learning library into your application.
| Feature / Deliverable | Proof of Concept | Production Pilot | Enterprise Scale |
|---|---|---|---|
Custom ZK Circuit Design & Integration | |||
Pre-Trained Model Conversion (e.g., PyTorch → ZK) | 1 Model | Up to 3 Models | Unlimited Models |
Performance Optimization (Prover Time) | Basic | Advanced (< 5s) | Bespoke (< 1s) |
On-Chain Verifier Smart Contracts | Single Chain | Multi-Chain (2) | Multi-Chain (5+) |
Comprehensive Security Audit | Internal Review | Third-Party Audit Report | Third-Party Audit + Bug Bounty |
Integration Support & Documentation | Basic | Priority | Dedicated Engineer |
Ongoing Maintenance & Updates | 6 Months SLA | 12+ Months SLA | |
Estimated Timeline | 4-6 Weeks | 8-12 Weeks | Custom |
Starting Investment | $50K - $100K | $150K - $300K | Custom Quote |
Our ZK-ML Development Process
We deliver production-ready ZK-ML libraries through a rigorous, security-first development lifecycle designed for enterprise-grade applications.
Requirement & Architecture Design
We begin by mapping your specific ML model (TensorFlow, PyTorch) to an optimal ZK circuit architecture, selecting the right proving system (Groth16, Plonk, Halo2) for your performance and privacy needs.
Circuit Implementation & Optimization
Our engineers implement your model in Circom, Noir, or custom R1CS, focusing on constraint minimization and memory optimization to reduce proving costs by up to 70% versus naive implementations.
Proving System Integration
We integrate the optimized circuit with a high-performance proving backend (e.g., SnarkJS, Bellman) and a lightweight verifier contract, ensuring sub-2-second proof generation on consumer hardware.
SDK & Developer Tooling
We deliver a TypeScript/Python SDK with comprehensive documentation, example dApps, and testing suites, enabling your team to integrate private ML inference in under 2 weeks.
Deployment & Ongoing Support
We manage the trusted setup ceremony (if required), deploy the verifier to your target chain (Ethereum, Polygon, zkSync), and provide 6 months of support with 99.9% SLA on critical updates.
Proven ZK Frameworks & Tools
We leverage established, battle-tested frameworks to build your ZKML library, ensuring security, performance, and interoperability from day one.
ZK-ML Library Development FAQs
Answers to the most common questions CTOs and technical founders ask when evaluating a partner for zero-knowledge machine learning library development.
A core library with 3-5 standard ZK-ML primitives (e.g., verifiable inference for a CNN, privacy-preserving model training proofs) typically takes 6-10 weeks from spec to audit-ready code. Complex, custom circuits or novel proof systems can extend to 12-16 weeks. We provide a detailed sprint-by-sprint roadmap during discovery.
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Our experts will offer a free quote and a 30min call to discuss your project.