Legal Retrieval and Semantic Search Engine
Hybrid semantic search and document retrieval engine for litigation records
Engineered a specialized document retrieval and semantic search engine for law firms. The platform processes high-volume court filings and scanned briefs via asynchronous OCR, uses hybrid retrieval combining Pinecone dense vector indexing with OpenSearch lexical search, and enforces cross-encoder reranking with strict page-level citation validation to eliminate hallucinations.
Engineering team structure and leadership.
How the engineering organization was structured, staffed across disciplines, and directed through delivery.
Dense vector embeddings, Pinecone indexing, and Cohere cross-encoder reranker tuning
Python FastAPI query layer, PostgreSQL metadata schema, and low-latency API contracts
Celery and Redis worker pipelines, Tesseract OCR parsing, and legal document chunking
Legal researcher interface, document previewer, and verified citation coordinate navigation
Litigation discovery workflows, citation inspection UI, and legal search user experience
Docker containerization, AWS ECS cluster management, and secure document store access
Automated regression suites, citation accuracy verification tests, and query latency benchmarks
Legal domain requirements, citation compliance, case law taxonomy, and statutory mapping
Led the 10-person multidisciplinary engineering team across ML, backend, QA, and infrastructure from architectural concept to delivery
Architected the hybrid search pipeline pairing Pinecone dense vector indexing with OpenSearch lexical BM25 matching
Designed the asynchronous background ingestion architecture using Celery, Redis, and Tesseract OCR for high-volume scanned court briefs
Engineered structural chunking strategies that respect legal boundaries (statutes, clauses, citations) rather than fixed token splits
Implemented cross-encoder reranking and citation validation guaranteeing direct source page coordinates for every retrieved result
Measured operational outcomes.
Concrete reliability benchmarks and performance metrics delivered to production.
Search Latency
Citation Accuracy
Ingestion Throughput
Index Coverage
Architectural decisions & implementation.
Target Architecture & Implementation
How the system was designed, structured, and deployed
Engineered an asynchronous ingestion pipeline with Celery and Redis that handles document OCR, text extraction (PyPDF / Unstructured), and custom structural chunking that preserves statutes, legal clauses, and citation boundaries. Implemented a hybrid search engine combining PostgreSQL for structured relational metadata, Pinecone for dense semantic vector search, and OpenSearch for lexical BM25 matching. Integrated a cross-encoder reranker to refine top candidate passages, backed by an attribution engine that mandates exact source page coordinates for every retrieved result.
System Component & Infrastructure Ledger
Detailed breakdown of runtime dependencies, protocols, and architectural roles
High-performance query execution and REST endpoints
Relational case records, metadata, and citation mappings
Managed high-dimensional dense vector indexing and similarity search
Inverted index BM25 lexical keyword matching
Background OCR and PDF chunking
Text extraction from degraded legal scans
Precision candidate scoring
Reproducible deployments across staging and cloud
Encrypted document and court record storage
"The search engine turned days of discovery review into minutes. Being able to jump directly from a search result to the highlighted paragraph on page 84 of an original court filing gave our legal team absolute confidence."
Legal Technology & Litigation Practice