We built a production-grade semantic search engine for a legal-tech firm, using modern NLP and vector search to surface contextually relevant cases—not just keyword matches. The engine understands legal language, intents, and relationships between documents, helping lawyers and researchers get to the right precedents in a fraction of the time.
Powered by BERT-family sentence embeddings, vector databases, and a robust Python API layer, the platform analyzes 10K+ legal documents and continuously improves as new data and feedback come in.
Top-5 semantic matches vs. manually curated case references.
Average time saved per query in legal research workflows.
Judgements, case laws, and internal knowledge documents.
Custom REST APIs integrated with the client’s existing CMS.
Traditional keyword search falls short in legal research where synonyms, nuanced phrasing, and cross-case references matter. We replaced simple text search with true semantic retrieval, tuned specifically for legal language and workflows.
The platform combines modern NLP models, vector search, and a robust backend layer to deliver low-latency, high-quality recommendations integrated with the client’s existing tools.
Legal documents (PDF, Word, HTML, CMS entries) are ingested via ETL jobs. Text is cleaned, segmented into passages, and enriched with metadata (court, jurisdiction, topics, citations).
A BERT-based or SentenceTransformer model encodes each passage into dense vectors. Batch jobs run on GPU-enabled workers, and embeddings are versioned to support model upgrades without downtime.
Embeddings are stored in a vector database / ANN index (e.g., Elasticsearch/OpenSearch with vector fields, or dedicated vector DB). Metadata stays in PostgreSQL / document storage, linked by stable IDs.
User queries are encoded into vectors in real time. The engine performs top-K vector search, then optionally re-ranks with a cross-encoder or scoring function tuned for legal relevance and citation strength.
A FastAPI-based microservice exposes search endpoints, suggestion APIs, and analytic hooks. The client’s CMS calls these APIs to show recommended cases, related judgements, and “similar documents” widgets.
