Executive Summary
A global law firm struggled to extract insights from extensive legal document repositories. Traditional and out-of-the-box (OOTB) RAG solutions proved insufficient for complex queries. After partnering with Elastiq, they implemented a tailored RAG solution combining chunking strategies, domain-specific knowledge layers, and analytical capabilities that extended beyond standard RAG functionality.
Client Profile
The client is an American multinational law firm with over 1,000 lawyers representing Fortune 500 companies, asset managers, financial institutions, and pro bono clients. Their document repositories span decades of legal work across multiple practice areas.
Business Challenges
Complex Document Structures
Legal documents span hundreds of pages with intricate layouts. Questions like “What’s the discount provided to client X on their SOW regarding matter Y?” require maintaining relationships across scattered information chunks.
Traditional semantic search and fixed-width chunking proved inadequate, necessitating domain-specific data structures that understand legal document hierarchies.
Information Analysis Requirements
Standard RAG systems retrieve information but cannot analyze it. A query like “What clients have renewals coming within 3 months?” requires calculating expiry dates-something traditional RAG cannot accomplish independently.
The firm needed not just retrieval, but computation over retrieved data.
Large Dataset Processing
RAG struggles when analyzing numerous documents simultaneously. “How many legal cases involving contract disputes were filed in California between 2018 and 2020?” might require processing thousands of documents, exceeding typical retrieval limitations.
Aggregation queries across large corpora required a fundamentally different approach.
Why Traditional RAG Falls Short
Fixed Chunking Limitations
Standard chunking strategies (500-1000 tokens) break documents at arbitrary boundaries, splitting clauses across chunks and losing context.
No Relationship Awareness
Traditional RAG treats each chunk independently. It cannot understand that an MSA references a specific SOW, which modifies terms from an earlier engagement letter.
Retrieval Without Reasoning
Finding relevant chunks is only half the problem. Legal queries often require computing over the retrieved data-calculating dates, comparing terms, or aggregating across documents.
Elastiq Solution
Elastiq Discover addressed these gaps through a three-pronged approach:
Smart Search
Identifies relevant document types (engagement letters, MSAs, SOWs), parties, dates, and clauses. The system filters chunks intelligently alongside semantic search, understanding that a question about “Client X’s discount” should prioritize pricing clauses in that client’s SOW.
Integrated Knowledge Layer
Builds relationships between documents, metadata, clauses, and chunks. The knowledge graph maintains:
- Document hierarchies (MSA → SOW → Amendments)
- Entity relationships (Client → Matters → Documents)
- Temporal connections (Original terms → Modifications)
- Cross-references between related clauses
Analytical Engine
Adds aggregation and comparison capabilities beyond retrieval:
- Date calculations (renewals within N months)
- Value comparisons across contracts
- Trend analysis over document versions
- Statistical summaries across document sets
Results
The solution achieved up to 60% reduction in document retrieval time while supporting query types that were previously impossible with traditional RAG.
Technical Approach
Legal-Aware Chunking
Instead of fixed-width chunks, the system uses document structure:
- Clauses as natural boundaries
- Section hierarchies preserved
- Cross-references maintained as metadata
- Amendment chains linked to originals
Hybrid Retrieval
Combines multiple retrieval strategies:
- Semantic search for conceptual queries
- Keyword search for specific terms
- Structured queries over metadata
- Graph traversal for relationship queries
Query Classification
The system first classifies incoming queries to route them appropriately:
- Retrieval queries → Standard RAG with smart filtering
- Analytical queries → Knowledge graph + computation
- Aggregation queries → Batch processing with summarization
Conclusion
This implementation demonstrates how purpose-built systems outperform generic OOTB solutions in enterprise contexts. Legal document management requires understanding not just what documents say, but how they relate to each other and what can be computed from their contents.
The key insight: RAG is not a monolithic solution. It’s a pattern that must be adapted to domain-specific requirements, combining retrieval with knowledge representation and analytical capabilities.