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Production-Grade Legal Intelligence for Polish Tax Law
Battle-tested legal AI system serving 50+ active researchers and processing queries across 3+ million legal documents. Combines advanced RAG, hybrid search, and intelligent schema generation to transform how legal professionals interact with unstructured legal documents. Proven reliability with 5+ academic publications and measurable impact on legal research workflows.
Legal professionals face a critical bottleneck: extracting actionable insights from massive volumes of unstructured legal text. Court judgments, tax interpretations, and legal acts contain vital information, but manual review is time-consuming and error-prone. A single research query often requires reviewing dozens of documents, cross-referencing multiple sources, and manually extracting key facts into structured formats for compliance systems. This process takes hours and risks missing critical precedents or misinterpreting legal nuances.
Built production-grade legal intelligence platform combining RAG (Retrieval-Augmented Generation), hybrid search (semantic + keyword), and automated schema generation. System processes 500K+ tax interpretations, enabling semantic search across 3M+ documents, conversational Q&A with source attribution, and automatic extraction of structured legal data. All components validated through real-world usage by 50+ researchers, resulting in 5+ publications and enterprise-grade reliability.
Elasticsearch (BM25), pgvector (semantic), sentence-transformers
Hybrid search combining keyword and semantic, 3M+ documents indexed, sub-500ms query latency, metadata filtering by type/date/authority, query expansion with legal synonyms
LangChain, OpenAI GPT-4, Anthropic Claude, Python
Multi-turn conversations with context, source attribution with document citations, prompt engineering for legal reasoning, token optimization with sliding windows, switchable LLM providers
Pydantic, spaCy NER, OpenAI structured outputs
Automated schema generation from document samples, structured extraction with type validation, batch processing for 500K+ documents, audit trail linking extracted data to source text
Tesseract OCR, Python, regex, spaCy
Multi-format ingestion (PDF, Word, HTML), OCR for scanned documents (>98% accuracy), metadata extraction (case numbers, dates), semantic chunking preserving legal reasoning, incremental indexing
FastAPI, PostgreSQL, Redis
RESTful API with async/await, PostgreSQL for structured data and audit logs, Redis for query caching and session management, rate limiting, user authentication
React, TypeScript, Tailwind CSS
Search interface with filters and facets, conversational chat UI with source citations, document viewer with highlighting, extraction result visualization, admin dashboard for corpus management
I specialize in building reliable AI systems for high-stakes domains: domain-specific embeddings, RAG with source attribution, hybrid search at scale, and information extraction. Let's discuss your legal AI or enterprise search challenges.
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