System Architecture
A deep dive into the technical architecture, retrieval methodology, and explainability framework powering JusticeAI.
Data Ingestion Layer
Constitution of India, IPC/BNS, CrPC, case law corpus, and real-time court transcripts are ingested, parsed, and pre-processed for downstream consumption.
Embedding & Indexing Layer
Legal documents are encoded into dense vector representations using domain-adapted transformers, then indexed for sub-millisecond retrieval.
Retrieval & Reranking Layer
A hybrid retrieval architecture combining sparse (BM25) and dense (FAISS) search, followed by cross-encoder reranking for precision optimization.
Generation & Reasoning Layer
Retrieved context is fed into a large language model with constrained generation, ensuring all outputs are grounded in source documents.
Explainability & Audit Layer
Every response includes source attribution, confidence scores, and attention visualization to ensure transparency and legal traceability.
RAG Pipeline
Retrieval-Augmented Generation ensures every response is grounded in verified legal source documents.
Query Understanding
User's legal query is parsed, intent-classified, and expanded with legal synonyms and relevant statutory terms.
Hybrid Retrieval
Parallel BM25 (keyword) and FAISS (semantic) retrieval fetches candidate passages from the legal knowledge base.
Cross-Encoder Reranking
A fine-tuned cross-encoder model reranks retrieved passages by legal relevance, filtering noise and improving precision.
Context Assembly
Top-ranked passages are assembled into a structured context window with metadata (article numbers, section references, case citations).
Grounded Generation
The LLM generates a response strictly grounded in retrieved context, with inline citations and confidence scoring.
Post-Processing
Response undergoes hallucination detection, citation verification, and formatting before being presented to the user.
Explainability & Trust
Every AI-generated legal response is transparent, auditable, and traceable to source documents.
Source Attribution
Every claim is linked to specific articles, sections, or case paragraphs.
Confidence Scoring
Calibrated confidence scores indicate reliability of each response.
Hallucination Guard
Constrained generation prevents fabrication of non-existent legal provisions.
Audit Trail
Complete log of retrieval steps, sources consulted, and reasoning chain.
Extended Research Areas
Ongoing explorations expanding the capabilities and applicability of the system.
Multilingual Support
The system is designed to handle legal queries in multiple Indian languages. Leveraging multilingual embeddings and translation pipelines, citizens can interact in Hindi, English, and other scheduled languages while maintaining legal accuracy.
- Hindi-English code-mixed queries
- Multilingual embeddings (mBERT)
- Language-agnostic retrieval
- Transliteration support
Blockchain Integrity
A conceptual framework for ensuring the integrity and immutability of AI-generated legal responses. Each response, along with its source citations and confidence scores, can be hashed and recorded on a permissioned blockchain for audit compliance.
- Response hash verification
- Immutable audit chain
- Timestamped evidence trail
- Permissioned ledger design
Knowledge Graph
A structured graph representation of Indian legal knowledge, mapping relationships between constitutional articles, statutory sections, landmark judgments, and legal principles for enhanced reasoning capabilities.
- Entity-relationship mapping
- Legal ontology design
- Graph-based reasoning
- Precedent chain traversal