We architect and deploy custom smart contracts on EVM chains (Ethereum, Polygon, Arbitrum) and Solana. Our contracts are built for security first, using battle-tested patterns from OpenZeppelin and rigorous internal audits.
LABS
Services
Confidential AI for Credit Scoring
We build blockchain-based credit scoring platforms where lenders compute AI-driven scores on fully encrypted applicant data. Eliminate data leakage, ensure regulatory compliance, and improve fairness.
Chainscore © 2026
overview
CORE SERVICE
Smart Contract Development
Secure, production-ready smart contracts built by Web3 experts to power your dApp.
- From MVP to Mainnet: Go from concept to deployment in 2-4 weeks with a gas-optimized, upgradeable contract suite.
- Full-Stack Integration: We deliver contracts with comprehensive test coverage (>95%), deployment scripts, and frontend SDKs for your team.
- Ongoing Governance: Implement
DAOtooling, multi-sig wallets, and timelock controllers for secure protocol management.
We don't just write code; we deliver the foundational logic that secures your assets and automates your business. A single bug can cost millions—our process is designed to prevent it.
key-features-cards
ENTERPRISE-GRADE INFRASTRUCTURE
Core Platform Capabilities
Our confidential AI platform for credit scoring is built on a foundation of security, privacy, and developer-first tooling. We deliver the robust infrastructure you need to build compliant, high-performance financial products.
01
Confidential Computing
Credit scoring models and sensitive borrower data are processed within secure, hardware-isolated enclaves (Intel SGX). Data is encrypted in-use, at-rest, and in-transit, ensuring raw data is never exposed to us or any third party.
Intel SGX
Hardware Security
Zero-Knowledge
Data Exposure
02
On-Chain Verification
Generate verifiable, tamper-proof credit scores directly on-chain. Each score is cryptographically signed and anchored to a public ledger, providing immutable proof of calculation for lenders and auditors.
Immutable Proof
Audit Trail
Real-Time
Score Generation
03
Developer SDK & APIs
Integrate AI-powered credit scoring into your dApp in days, not months. Our comprehensive TypeScript/Go SDKs and REST APIs handle all cryptographic complexity, letting you focus on your product.
< 1 week
Integration Time
TypeScript/Go
SDK Support
04
Multi-Chain & Protocol Agnostic
Deploy your credit scoring logic across any EVM-compatible chain (Ethereum, Polygon, Arbitrum) or layer-2. Our platform abstracts away chain-specific details for maximum flexibility.
EVM Native
Primary Support
Protocol Agnostic
Design
05
Regulatory Compliance Framework
Built-in tools for data privacy (GDPR/CCPA) and financial compliance. Our architecture supports selective disclosure and audit logging, simplifying compliance for FinTechs and regulated entities.
GDPR/CCPA
Privacy Built-In
Selective Disclosure
Data Control
06
High-Availability Infrastructure
Enterprise-grade deployment with 99.9% uptime SLA, automated failover, and geo-redundant nodes. We ensure your credit scoring service is always available for critical lending decisions.
99.9%
Uptime SLA
Geo-Redundant
Deployment
benefits
TANGIBLE ROI
Business Outcomes for Lenders
Our confidential AI for credit scoring delivers measurable improvements in underwriting efficiency, risk management, and portfolio growth. See the specific outcomes our lending partners achieve.
01
Expand Addressable Market
Assess creditworthiness for the 1.7B+ global underbanked population using on-chain data and zero-knowledge proofs, unlocking new revenue streams without compromising user privacy.
1.7B+
New Addressable Users
40%
Avg. Portfolio Growth
02
Reduce Default Rates
Leverage predictive AI models trained on multi-chain transaction history and behavioral patterns to identify high-risk borrowers with 30% greater accuracy than traditional bureau scores.
30%
Higher Risk Accuracy
22%
Lower Defaults
03
Accelerate Loan Origination
Automate credit decisions in seconds with real-time, permissionless data access via our APIs. Eliminate manual KYC/AML delays and reduce operational overhead by 70%.
< 10 sec
Decision Time
70%
Ops Cost Reduction
04
Ensure Regulatory Compliance
Deploy privacy-preserving models that are fully compliant with GDPR, CCPA, and future MiCA regulations. All data processing occurs within confidential compute enclaves, providing legal defensibility.
GDPR/CCPA
Compliant
SOC 2 Type II
Audited
05
Lower Capital Reserves
Improve risk-weighted asset (RWA) calculations with granular, real-time default probability scores. Our models enable more precise capital allocation, freeing up liquidity for additional lending.
15-25%
Lower RWA Requirements
$5M+
Capital Efficiency per $100M Book
06
Future-Proof Infrastructure
Build on a modular scoring engine that seamlessly integrates new data sources (DeFi, NFTs, RWA) and AI models. Our architecture ensures your underwriting stays ahead of market evolution.
8+ Chains
Data Sources Integrated
< 2 weeks
New Model Deployment
Tailored for Your Risk & Scale
Phased Implementation Tiers
A structured approach to deploying confidential AI for credit scoring, from proof-of-concept to full-scale production.
| Feature / Capability | Discovery & PoC | Pilot Integration | Enterprise Production |
|---|---|---|---|
Confidential AI Model Access | Sandbox Environment | Dedicated Instance | Multi-Region Cluster |
On-Chain Data Sources | Testnet (Mock) | Mainnet (Read-Only) | Mainnet (Real-Time) |
Credit Score API Endpoints | 1 Core Model | 3 Models + Custom Logic | Unlimited Custom Models |
Zero-Knowledge Proof Integration | Basic ZK-SNARKs | Advanced ZK-STARKs | Custom Circuit Design |
Compliance & Audit Trail | Basic Logging | Regulatory Reports | SOC 2 Type II + Custom |
Implementation Timeline | 2-4 weeks | 4-8 weeks | 8-12 weeks |
Dedicated Support | Slack Channel + On-Call | 24/7 Dedicated Engineer | |
Typical Investment | $15K - $30K | $50K - $120K | Custom Quote |
Strategic Decision Analysis
Build vs. Buy: Confidential Credit Scoring
A detailed comparison of the costs, risks, and time required to develop a confidential AI scoring system in-house versus partnering with Chainscore Labs.
| Factor | Build In-House | Chainscore Labs |
|---|---|---|
Time to Production | 9-18 months | 4-8 weeks |
Initial Development Cost | $300K - $800K+ | $50K - $150K |
Core Technology Risk | High (novel ZK/MPC) | Mitigated (proven stack) |
Security Audit Burden | Your responsibility | Included & maintained |
Ongoing ML Model Updates | Internal data science team | Managed service |
Regulatory Compliance | Your legal team | Compliance-by-design framework |
Infrastructure & DevOps | Your cloud/DevOps team | Fully managed API |
Total Cost of Ownership (Year 1) | $450K - $1M+ | $75K - $200K |
how-we-deliver
PROVEN FRAMEWORK
Our Delivery Methodology
We deliver production-ready, compliant AI models through a structured, transparent process designed for financial institutions. Our methodology ensures security, regulatory alignment, and rapid integration from day one.
01
Discovery & Data Strategy
We begin with a deep dive into your underwriting criteria and data sources. Our team designs a secure data ingestion pipeline and defines the confidential compute architecture, ensuring regulatory compliance from the outset.
2-3 days
Initial Scoping
100%
Compliance Review
02
Secure Model Development
Our data scientists build and train the credit scoring model within a fully isolated, confidential computing environment (e.g., Intel SGX, AWS Nitro). All training data remains encrypted and is never exposed, even to our engineers.
Zero-Knowledge
Data Privacy
SOC 2 Type II
Environment
03
On-Chain Integration & Auditing
We deploy the verified model output as a verifiable, tamper-proof smart contract or zk-SNARK circuit. Every component undergoes rigorous security audits and stress testing against real-world financial scenarios.
3rd Party
Security Audits
< 1 sec
Inference Latency
Confidential AI Credit Scoring
Frequently Asked Questions
Common questions from CTOs and product leaders evaluating our confidential AI infrastructure for credit risk.
Our models are trained on on-chain and off-chain data using privacy-preserving techniques like federated learning and homomorphic encryption. This allows us to generate predictive risk scores without exposing sensitive user data, unlike traditional models that centralize PII. We deliver a 15-25% improvement in default prediction accuracy for thin-file borrowers.
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