We architect and deploy custom smart contracts on EVM-compatible chains (Ethereum, Polygon, Arbitrum) and Solana. Our process ensures zero critical vulnerabilities through formal verification and third-party audits by firms like Quantstamp and CertiK.
LABS
Services
ZKML Inference Engine Development
We design and deploy high-performance, verifiable inference engines for on-chain AI models, enabling trustless execution of ML predictions with zero-knowledge privacy guarantees.
Chainscore © 2026
overview
CORE SERVICE
Smart Contract Development
Secure, production-ready smart contracts built for scale and compliance.
Deliver a market-ready product in 4-6 weeks with a fixed-scope MVP, including full test coverage and deployment scripts.
- Token Standards:
ERC-20,ERC-721,ERC-1155,SPLwith custom minting, vesting, and governance modules. - DeFi Primitives: Automated Market Makers (AMMs), liquidity pools, staking contracts, and yield aggregators.
- Enterprise Features: Role-based access control, upgradeability patterns, and gas optimization for high-volume applications.
key-features-cards
ENTERPRISE-GRADE INFRASTRUCTURE
Core Engine Capabilities
Our ZKML inference engine is built for production, delivering verifiable machine learning with the performance, security, and reliability required for high-stakes financial and identity applications.
01
High-Performance Proving
Optimized proving circuits for popular ML frameworks (TensorFlow, PyTorch) with sub-2 second proof generation for common inference tasks, enabling real-time on-chain verification.
< 2 sec
Proof Generation
> 100 TPS
Verification Throughput
02
Secure Model Integration
End-to-end security pipeline for converting trained models into verifiable circuits. Includes integrity checks, weight encryption, and protection against model extraction attacks.
SOC 2 Type II
Compliance
Zero-Knowledge
Data Privacy
03
Multi-Chain Deployment
Deploy your verifiable inference engine across Ethereum, Polygon, Arbitrum, and other EVM-compatible chains. We handle chain-specific optimizations and smart contract integration.
5+
Supported Chains
< 3 weeks
Integration Time
04
Scalable Orchestration
Managed infrastructure for proof generation, batching, and verification with auto-scaling to handle demand spikes. Includes monitoring, alerting, and 99.9% uptime SLA.
99.9%
Uptime SLA
< 50 ms
API Latency
05
Custom Circuit Development
Tailored ZK-SNARK/STARK circuit design for proprietary ML models or novel consensus mechanisms, ensuring optimal gas costs and verification speed for your specific use case.
30+
Circuit Templates
~40%
Avg. Gas Savings
benefits
DELIVERABLES & GUARANTEES
Business Outcomes for Your Product
Our ZKML Inference Engine Development service delivers production-ready infrastructure with measurable results. We focus on verifiable performance, security, and time-to-market advantages.
01
Production-Ready ZKML Engine
Deploy a fully integrated, high-performance ZKML inference engine. We deliver custom circuits for your specific ML model, optimized for on-chain verification with minimal proving time and gas costs.
< 2 sec
Avg. Proof Gen
> 99.9%
Model Accuracy
02
End-to-End Security & Audits
Receive a system hardened against adversarial attacks. Our development includes formal verification of circuits, integration of OpenZeppelin libraries, and a final audit report from a leading security firm like Trail of Bits or Quantstamp.
2+ Audits
Security Layers
Formal
Verification
03
Rapid Integration & Deployment
Accelerate your time-to-market. We provide a complete SDK, API gateway, and deployment pipeline for your chosen EVM chain (Ethereum, Polygon, Arbitrum). Integration support reduces your engineering overhead.
4-6 weeks
To MVP
Full SDK
Included
04
Cost-Optimized On-Chain Operations
Achieve predictable, low-cost inference. Our gas optimization techniques and circuit design reduce on-chain verification costs by up to 70% compared to baseline implementations, ensuring economic viability.
~70%
Gas Savings
Fixed Fee
Cost Model
Performance Benchmarks
ZKML Inference Engine Technical Specifications
Compare the performance, security, and scalability of our ZKML inference engine development packages. All tiers include core ZK circuit design and on-chain verifier deployment.
| Specification | Starter | Professional | Enterprise |
|---|---|---|---|
ZK Circuit Complexity | Up to 10k constraints | Up to 100k constraints | Custom, unlimited |
Inference Latency (Off-chain) | < 5 seconds | < 2 seconds | < 500 ms |
On-chain Verification Gas Cost | Optimized for L2s | Optimized for Mainnet | Custom gas optimization |
Supported ML Frameworks | ONNX, PyTorch (basic) | ONNX, PyTorch, TensorFlow | Any framework + custom ops |
Proving System | Groth16 / Plonk | Plonk / Halo2 | Custom (Halo2, Nova, etc.) |
Audit & Security Review | Basic code review | Full formal verification | End-to-end security audit + formal verification |
Uptime SLA & Monitoring | N/A | 99.5% | 99.9% with 24/7 SRE |
Team Expertise | 2 Senior Engineers | Lead Architect + 3 Engineers | Dedicated Pod (Architect, 4 Engineers, SRE) |
Integration Support | Documentation | Direct engineering support | White-glove integration & training |
Estimated Delivery Timeline | 6-8 weeks | 10-12 weeks | Custom (16+ weeks) |
Starting Price | $80,000 | $200,000 | Custom Quote |
how-we-deliver
A PROVEN METHODOLOGY
Our ZKML Inference Engine Development Process
We deliver production-ready, verifiable ML models with a structured, transparent process designed for enterprise security and rapid deployment.
01
1. Model Architecture & Feasibility
We analyze your ML model for ZK-circuit compatibility, identifying optimization opportunities and defining the technical scope for verifiable inference.
02
2. Circuit Design & Implementation
Our engineers convert your model into optimized ZK circuits using Circom or Halo2, focusing on proof size and verification speed for your specific use case.
03
3. On-Chain Integration & Testing
We deploy and integrate the verifier contract (Solidity/Vyper) with your dApp, conducting rigorous on-chain and off-chain testing to ensure flawless execution.
04
4. Security Audit & Optimization
Every component undergoes a multi-layered security review, including circuit logic checks and gas optimization for the on-chain verifier, before final delivery.
ZKML Development
Frequently Asked Questions
Common questions from CTOs and technical founders about building and deploying private machine learning on-chain.
A complete, audited ZKML inference system typically takes 6-10 weeks from kickoff to mainnet deployment. This includes 2-3 weeks for circuit design and proof-of-concept, 3-4 weeks for core development and integration, and 1-2 weeks for security review and deployment automation. We deliver working prototypes within the first 2 weeks.
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall