Empowering Indian lawyers with AI & Blockchain – Trust, Transparency, Compliance.
OPAL × Avalanche: Legal AI meets Blockchain Innovation
OPAL (Open-Source Private AI for Law) is a next-generation GenAI-powered legal co-counsel platform, purpose-built for Indian lawyers. It combines advanced multi-agent AI reasoning, hybrid legal research, and blockchain notarization on a custom Avalanche Subnet for trust, transparency, and compliance with Indian legal standards.
The platform leverages a sophisticated DAO of Agents architecture where multiple specialized AI agents collaborate through a democratic voting mechanism to provide comprehensive legal analysis, ensuring accuracy and reducing hallucination through collective intelligence.
 Statute Agent Legal Frameworks |
 Precedent Agent Case Law Analysis |
 Limitation Agent Time Limitations |
 Risk Agent Legal Risk Assessment |
 Devil's Advocate Argument Challenges |
 Ethics Agent Professional Conduct |
 Drafting Agent Document Preparation |
🗳️ Aggregator Democratic Voting |
- Statute Agent – Analyzes statutory provisions and legal frameworks
- Precedent Agent – Examines case law and judicial precedents
- Limitation Agent – Checks time limitations and procedural deadlines
- Risk Agent – Assesses legal risks and potential outcomes
- Devil's Advocate Agent – Challenges arguments and identifies weaknesses
- Ethics Agent – Ensures professional conduct and ethical compliance
- Drafting Agent – Assists with legal document preparation
- Confidence-Weighted Voting – Agents vote based on confidence levels
- Multiplicative Weight Updates (MWU) – Dynamic weight adjustment based on performance
- Subdomain Specialization – Different agent weights for different legal areas
- Alignment Detection – Identifies consensus among agents for reliability
- Semantic Search – Qdrant vector database for contextual legal research
- Keyword Search – PostgreSQL full-text search for precise term matching
- Citation Verification – Automatic legal citation extraction and validation
- Multi-Court Filtering – Supreme Court, High Courts, and Tribunal decisions
- Immutable Notarization – Merkle root storage for tamper-proof verification
- Smart Contracts – Solidity contracts for transparent operations
- Encrypted Evidence Storage – AES-GCM encrypted audit trails
- Gas-Free Operations – Server covers all blockchain transaction costs
- End-to-End AES-256 Encryption – Envelope encryption for sensitive data
- Automatic PII Detection & Redaction – GDPR/DPDP 2023 compliance
- Row Level Security – Multi-tenant data isolation
- Crypto-Shredding – Secure data deletion for privacy compliance
- Audit Trails – Immutable logging of all operations
- DPDP 2023 Compliance – Full compliance with Indian data protection laws
- Cryptographic Anchoring – Every AI output anchored on-chain
- Data Retention Policies – Automated data lifecycle management
- Export Capabilities – PDF/DOCX report generation with citations
Comprehensive OPAL architecture showing the complete flow from user input through multi-agent processing to blockchain notarization
The OPAL platform follows a microservices architecture with the following key components:
- Next.js Application – Modern React-based user interface
- Clerk Authentication – Secure user authentication and authorization
- Real-time Updates – WebSocket connections for live agent updates
- FastAPI Server – High-performance Python backend
- Rate Limiting – Request throttling and quota management
- Security Middleware – JWT validation, CORS, and input sanitization
- Agent Orchestrator – Coordinates agent execution and voting
- Specialized Agents – 7 domain-specific legal AI agents
- Voting Mechanism – Democratic consensus with confidence weighting
- Weight Management – Dynamic agent weight updates using MWU algorithm
- PostgreSQL – Primary database with full-text search
- Qdrant Vector DB – Semantic search for legal documents
- Redis Cache – Session management and query caching
- Supabase Storage – Encrypted document storage
- Custom Avalanche Subnet – Legal-specific blockchain network
- Smart Contracts – Notary, CommitStore, and ProjectRegistry
- Subnet Validators – Decentralized network validation
- Immutable Audit Trails – Cryptographic proof of research integrity
- AES-256 Encryption – End-to-end data protection
- PII Detection – Automated personal information redaction
- Data Retention – Automated lifecycle management
- Audit Logging – Comprehensive compliance tracking
OPAL operates on a custom Avalanche subnet specifically designed for legal research notarization and compliance:
- Custom Blockchain – Tailored for legal research notarization & evidence storage
- Smart Contracts – Immutable proofs & encrypted evidence with zero gas fees
- Transparency – Every AI output cryptographically anchored on-chain
- ⚖Compliance – Supports comprehensive audit trails for Indian legal standards
- Performance – Sub-second finality with high throughput
- Cost-Effective – Predictable costs with server-covered transaction fees
// Immutable notarization of legal research runs
contract Notary {
mapping(bytes32 => bytes32) public roots;
function publish(bytes32 runId, bytes32 rootHash) external;
function get(bytes32 runId) external view returns (bytes32);
function isNotarized(bytes32 runId) external view returns (bool);
}// Opaque encrypted commit storage
contract CommitStore {
mapping(bytes32 => bytes) private blob;
mapping(bytes32 => bytes32) public labelHashes;
function commit(bytes32 id, bytes32 labelHash, bytes calldata ciphertext, bytes32 dataHash) external;
function get(bytes32 id) external view returns (bytes memory);
}// Immutable registry of project versions
contract ProjectRegistry {
struct ReleaseInfo {
bytes32 sourceHash;
bytes32 artifactHash;
uint256 timestamp;
string version;
}
function register(bytes32 versionId, bytes32 sourceHash, bytes32 artifactHash, string calldata version) external;
}- Chain ID: 43210 (Custom)
- Block Time: 1 second
- Gas Limit: 15M per block
- Validators: 5 initial validators with PoS consensus
- Native Token: OPAL (for internal operations)
The system implements a sophisticated Multiplicative Weight Updates (MWU) algorithm:
- Initial Weights: Each agent starts with domain-specific weights
- Confidence Scoring: Agents provide confidence scores (0.0-1.0) with their responses
- Alignment Detection: System identifies which agents agree with the synthesized answer
- Weight Updates: Aligned agents get weight increases, misaligned agents get decreases
- Subdomain Adaptation: Different legal areas have different optimal agent weights
# Example weight configuration for Criminal Law
CRIMINAL_WEIGHTS = {
"statute": 1.7, # High - IPC/CrPC provisions critical
"precedent": 1.5, # High - Criminal precedents important
"limitation": 1.6, # High - Time limits critical
"risk": 1.8, # Very High - Liberty at stake
"devil": 1.3, # Medium-High - Important for defense
"ethics": 1.4, # Medium-High - Professional conduct critical
"drafting": 0.7, # Low - Less about drafting
}Each agent follows a specialized workflow:
- Query Analysis – Understanding the legal question context
- Knowledge Retrieval – Accessing relevant legal databases
- Domain Processing – Applying specialized legal reasoning
- Confidence Assessment – Self-evaluating response quality
- Collaborative Voting – Participating in democratic decision-making
# Multi-layer encryption for sensitive data
def encrypt_user_input(plaintext: str) -> Dict[str, str]:
# Generate random data key
data_key = AESGCM.generate_key(bit_length=256)
# Encrypt data with data key
ciphertext = aesgcm_data.encrypt(nonce, plaintext.encode(), aad)
# Encrypt data key with master key (envelope encryption)
encrypted_data_key = aesgcm_master.encrypt(key_nonce, data_key, None)
return {
"ciphertext": base64.b64encode(ciphertext).decode(),
"encrypted_key": base64.b64encode(encrypted_data_key).decode(),
# ... metadata
}- Automatic Detection – Identifies personal information in legal documents
- Context-Aware Redaction – Preserves legal context while protecting privacy
- Reversible Masking – Authorized users can unmask when necessary
- Audit Logging – All PII operations logged for compliance
class DataRetentionManager:
def __init__(self):
self.default_retention_days = 180 # DPDP 2023 compliance
self.pii_retention_days = 90 # Shorter for sensitive data
self.crypto_shred_delay_hours = 24 # Recovery window- ✅ Data Minimization – Collect only necessary legal data
- ✅ Purpose Limitation – Data used only for legal research
- ✅ Storage Limitation – Automatic deletion after retention period
- ✅ Consent Management – Clear consent mechanisms for data processing
- ✅ Right to Erasure – Crypto-shredding for secure deletion
- ✅ Data Portability – Export capabilities for user data
- ✅ Breach Notification – Automated incident response
- Bar Council Rules – Compliance with professional conduct rules
- Client Confidentiality – Attorney-client privilege protection
- Conflict Checking – Automated conflict of interest detection
- Audit Trails – Complete research history for professional accountability
// Modern React/Next.js with TypeScript
- Framework: Next.js 15.2.4 (App Router)
- Language: TypeScript 5.0+
- Styling: TailwindCSS + Radix UI
- Authentication: Clerk Auth
- State Management: Zustand + React Query
- Charts: Recharts
- Icons: Lucide React# High-performance Python backend
- Framework: FastAPI 0.109.0
- Language: Python 3.11+
- ORM: SQLAlchemy 2.0 (async)
- Task Queue: Celery + Redis
- API Documentation: OpenAPI/Swagger
- Monitoring: Structlog + Prometheus
- Testing: Pytest + AsyncIO# Multi-model AI architecture
- Primary LLM: OpenAI GPT-4o
- Embeddings: OpenAI text-embedding-3-large
- Vector Database: Qdrant (semantic search)
- Agent Framework: Custom multi-agent system
- Confidence Scoring: Ensemble methods
- Hallucination Detection: Cross-validation-- Multi-database architecture
- Primary DB: PostgreSQL 15+ (with full-text search)
- Vector DB: Qdrant (legal document embeddings)
- Cache: Redis 7+ (session & query caching)
- File Storage: Supabase Storage (encrypted documents)
- Search: Hybrid semantic + keyword search// Avalanche Subnet Integration
- Blockchain: Custom Avalanche Subnet
- Smart Contracts: Solidity 0.8.20
- Development: Hardhat + TypeScript
- Testing: Hardhat + Chai
- Deployment: Automated CI/CD pipeline# Production-ready deployment
- Containerization: Docker + Docker Compose
- Orchestration: Kubernetes (production)
- CI/CD: GitHub Actions
- Monitoring: Grafana + Prometheus + Loki
- Logging: Structured logging with correlation IDs
- Security: HTTPS, JWT, Rate limitingUpload Document → File Type Detection → PDF/DOCX Processing → Text Extraction →
Paragraph Segmentation → Metadata Extraction → Document Chunking →
Embedding Generation → Vector Indexing → FTS Indexing → Document Ready
- User Query Submission – Legal question submitted through web interface
- PII Detection & Redaction – Automatic identification and masking of personal data
- Agent Orchestration – Query routed to specialized legal AI agents
- Parallel Processing – Agents work simultaneously on different aspects:
- Statute Agent → Analyzes relevant laws and regulations
- Precedent Agent → Searches case law and judicial precedents
- Risk Agent → Assesses potential legal risks and outcomes
- Ethics Agent → Ensures professional conduct compliance
- Confidence Scoring – Each agent provides confidence level (0.0-1.0)
- Democratic Voting – Agents participate in weighted consensus mechanism
- Response Synthesis – Aggregator combines agent outputs using MWU algorithm
- Blockchain Notarization – Research run notarized on Avalanche subnet
- Citation Verification – Legal citations validated and linked
- Report Generation – Comprehensive legal analysis delivered to user
Legal Research Complete → Generate Merkle Tree → Compute Root Hash →
Create Run ID → Encrypt Audit Data → Submit to Notary Contract →
Store in CommitStore → Transaction Confirmed → Update Database →
Return Verification Hash
Data Input → PII Detection → Encryption → Processing → Audit Logging →
Retention Check → [Expired?] → Crypto-Shred → Compliance Report
- Node.js 18+ and npm
- Python 3.11+ and pip
- PostgreSQL 15+
- Redis 7+
- Docker and Docker Compose
- Git for version control
git clone https://github.com/your-org/opal-avalanche.git
cd opal-avalanche# Copy environment files
cp backend/env_example backend/.env
cp frontend/env.example frontend/.env.local
# Edit configuration files with your settings
# - Database connections
# - API keys (OpenAI, Clerk)
# - Subnet configuration
# - Encryption keyscd infra
docker-compose up -d
cd ..This starts:
- PostgreSQL database
- Redis cache
- Qdrant vector database
- Monitoring stack (optional)
cd backend
# Create virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run database migrations
alembic upgrade head
# Start the backend server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000Backend will be available at: http://localhost:8000
API documentation: http://localhost:8000/docs
cd frontend
# Install dependencies
npm install
# Start development server
npm run devFrontend will be available at: http://localhost:3000
cd backend
# Start Celery worker for background tasks
celery -A app.core.tasks worker --loglevel=info# For blockchain integration (optional for development)
cd subnet-contracts
# Install dependencies
npm install
# Deploy contracts to local subnet
npx hardhat deploy --network localhost
# Update backend configuration with contract addresses# Backend tests
cd backend
pytest tests/ -v --cov=app
# Frontend tests
cd frontend
npm run test
# Smart contract tests
cd subnet-contracts
npx hardhat test# Python linting and formatting
cd backend
black app/
isort app/
mypy app/
# TypeScript checking
cd frontend
npm run type-check
npm run lintWe welcome contributions from the legal tech community! Here's how to get started:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Follow the development workflow above
- Ensure tests pass and code quality checks pass
- Submit a pull request
- Code Style: Follow existing patterns and linting rules
- Testing: Add tests for new features
- Documentation: Update README and API docs
- Security: Follow security best practices
- Legal Compliance: Ensure changes maintain compliance
- Agent Development – New specialized legal agents
- Search Enhancement – Improved legal document retrieval
- Security Features – Enhanced privacy and compliance
- Internationalization – Support for multiple Indian languages
- Analytics – Better insights and reporting
- Testing – Expanded test coverage
- Multi-agent legal AI system
- Avalanche subnet integration
- Basic security and compliance
- Document ingestion pipeline
- Web interface
- Advanced NLP – Support for Hindi and regional languages
- Mobile App – React Native mobile application
- API Marketplace – Third-party integrations
- Advanced Analytics – Legal trend analysis
- Collaborative Features – Team workspaces
- Enterprise SSO – SAML/LDAP integration
- Advanced Compliance – Additional regulatory frameworks
- AI Model Training – Custom legal models
- Global Expansion – Support for other legal systems
- Blockchain Interop – Cross-chain compatibility
- Legal AI Research – Indian legal domain expertise
- Blockchain Engineering – Avalanche subnet specialists
- Security & Compliance – Privacy and data protection experts
- Product Design – User experience and interface design
- Data Science – Machine learning and NLP specialists
- Indian Legal System – Supreme Court & High Court judgments
- Avalanche – Blockchain infrastructure and support
- ⚖Legal Community – Feedback and domain expertise
Powered by Avalanche • Built for Indian Legal Professionals • Open Source & Transparent
Making legal research more accessible, accurate, and trustworthy through AI and blockchain technology.