We architect and deploy audit-ready smart contracts for tokens, DeFi protocols, and NFT ecosystems. Our engineers write in Solidity 0.8+ using OpenZeppelin standards and implement formal verification for critical logic.
LABS
Services
Private Model Training on Blockchain
We implement confidential training pipelines on EVM or other L1/L2s, allowing model training on sensitive data without exposing raw inputs, using ZK proofs for verifiable training integrity.
Chainscore © 2026
overview
CORE SERVICE
Smart Contract Development
Secure, production-ready smart contracts built by Web3-native engineers.
- Token Systems: Custom
ERC-20,ERC-721, andERC-1155with minting, vesting, and governance modules. - DeFi Protocols: Automated Market Makers (AMMs), lending/borrowing pools, and yield aggregators.
- Enterprise Logic: Multi-signature wallets, upgradeable proxies, and cross-chain bridges.
- Security First: Every contract undergoes peer review and is prepared for third-party audits from firms like CertiK or Quantstamp.
We deliver contracts with 99.9% uptime SLAs, gas-optimized for cost, and fully documented for your team.
key-features-cards
ENTERPRISE-GRADE INFRASTRUCTURE
Core Capabilities of Our ZKML Training Service
We deliver end-to-end private machine learning training on blockchain, enabling you to build verifiable AI models without exposing sensitive data or proprietary algorithms.
benefits
TANGIBLE ROI
Business Outcomes: Why Private, Verifiable Training Matters
Move beyond theoretical AI promises. Our blockchain-based private training delivers measurable business advantages, from protecting your competitive edge to unlocking new revenue streams with trusted AI.
01
Protect Proprietary Data & Models
Train on sensitive datasets without exposing raw data. Our zero-knowledge and TEE-based frameworks ensure your IP and training inputs remain confidential, even during collaborative training.
Why it matters: Safeguard your most valuable assets—your data and the resulting AI models—from competitors and data leaks.
SOC 2 Type II
Compliance
ZKP/TEE
Privacy Tech
02
Enable Trusted AI Marketplaces
Deploy models with on-chain, immutable proof of training lineage and data provenance. This creates verifiable trust for buyers, enabling you to license or sell AI models as high-value digital assets.
Why it matters: Monetize your AI work in new ways by providing cryptographic assurance of model quality and ethical sourcing.
Immutable
Audit Trail
New Revenue
Stream
03
Ensure Regulatory & Audit Compliance
Generate a tamper-proof record of your model's entire lifecycle—from data sourcing to final parameters. This simplifies compliance with frameworks like GDPR (right to explanation) and upcoming AI regulations.
Why it matters: Reduce legal risk and audit costs with automated, cryptographically-secure documentation for regulators and internal governance.
Automated
Compliance
GDPR-Ready
Framework
04
Facilitate Secure Data Collaborations
Pool data with partners, hospitals, or research institutions for better models without centralized data aggregation. Our privacy-preserving federated learning protocols ensure each party retains full control.
Why it matters: Build more robust, generalized AI models by leveraging broader datasets while maintaining strict data sovereignty agreements.
Federated
Learning
Data Sovereignty
Guaranteed
05
Reduce Model Inference & Operational Risk
Verifiable training proves a model hasn't been tampered with post-deployment. This builds user trust in high-stakes applications like financial forecasting, medical diagnosis, and autonomous systems.
Why it matters: Mitigate brand damage and liability by providing undeniable proof of your model's integrity and ethical training process.
Tamper-Proof
Verification
Reduced
Liability
06
Future-Proof Your AI Strategy
Build on open, interoperable standards instead of proprietary walled gardens. Our blockchain-agnostic approach ensures your verifiable training assets remain portable and valuable as the ecosystem evolves.
Why it matters: Avoid vendor lock-in and ensure long-term asset value by adopting decentralized, standard-based infrastructure from the start.
Blockchain-Agnostic
Architecture
No Vendor
Lock-in
Structured Roadmap for Private AI on Blockchain
Phased Implementation Tiers
A modular approach to deploying private, on-chain model training, from initial PoC to full-scale production.
| Capability | Proof-of-Concept | Production-Ready | Enterprise Scale |
|---|---|---|---|
Private Training Environment | Single Model, Sandbox | Multi-Model, Isolated | Federated Learning Support |
On-Chain Verification | Basic Proof Logging | ZK-SNARK Attestations | Full Proof-of-Training Consensus |
Supported Frameworks | PyTorch | PyTorch, TensorFlow | PyTorch, TensorFlow, Custom |
Data Privacy Method | Local Differential Privacy | Secure Multi-Party Computation (SMPC) | Fully Homomorphic Encryption (FHE) |
Model Size Limit | Up to 1GB | Up to 10GB | Custom / Unlimited |
Blockchain Integration | Single Testnet (e.g., Sepolia) | Multi-Chain (Ethereum, Polygon) | Custom L1/L2 Deployment |
Team Support | Email & Docs | Dedicated Engineer | 24/7 SRE & Architect |
Deployment Timeline | 2-4 Weeks | 6-8 Weeks | Custom Roadmap |
Starting Investment | $25K | $100K | Custom Quote |
how-we-deliver
TRANSPARENT & PREDICTABLE
Our Delivery Process: From Architecture to Audit
A structured, milestone-driven approach to deliver secure, production-ready private AI models on-chain. We provide clear deliverables and timelines at every phase.
01
Architecture & Design
We design a custom system architecture for your private model training, selecting optimal on-chain components (zk-SNARKs, TEEs, MPC) and off-chain compute infrastructure.
1-2 weeks
Design Phase
3+ Options
Architecture Reviewed
02
Smart Contract Development
Development of core on-chain logic for model governance, data access control, and incentive distribution using Solidity 0.8+ with OpenZeppelin security patterns.
2-4 weeks
Development Time
Gas-Optimized
Code Standard
03
Off-Chain Compute Integration
Secure integration of off-chain compute nodes (e.g., using TensorFlow Privacy, PySyft) with on-chain verification, ensuring data privacy and model integrity.
Sub-Second
Verification
Zero-Knowledge
Privacy Guarantee
04
Security Audit & Testing
Comprehensive security review including unit/integration tests, economic simulations, and a formal audit by a third-party firm like Trail of Bits or Quantstamp.
Certified
Audit Report
>95%
Test Coverage
05
Deployment & Monitoring
Production deployment on mainnet (Ethereum, Polygon) or your chosen L2 with 24/7 monitoring, alerting, and performance dashboards for model training cycles.
99.9%
Uptime SLA
< 1 week
Deployment
06
Documentation & Handover
Complete technical documentation, operational runbooks, and developer training to ensure your team can maintain and scale the system independently.
Full Source
Code Ownership
Ongoing
Support Available
Private Model Training
Frequently Asked Questions
Get clear, technical answers to common questions about our secure, on-chain AI development process.
We follow a structured, four-phase methodology: 1) Data Preparation & Encryption: Your training data is encrypted client-side using MPC or FHE before any blockchain interaction. 2) Smart Contract Orchestration: We deploy custom, audited contracts on your chosen L2 (zkSync, Arbitrum) or appchain to manage the training job, access control, and incentive mechanisms for validators. 3) Distributed Compute Execution: The encrypted model trains across our verified node network using frameworks like TensorFlow-encrypted. 4) Model Delivery & Verification: The final model weights are delivered securely, with cryptographic proofs of correct execution stored on-chain for auditability.
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