Executive Summary
A legal-tech client approached us with a unique challenge: they needed an AI system capable of querying over a million legal content chunks and generating safe, accurate, and jurisdiction-aware legal drafts. From NDAs to employment agreements, their use cases demanded a Retrieval-Augmented Generation (RAG) system that was not only scalable but also contextually and legally precise.
We delivered a custom-built Agentic RAG platform that could search, reason, and generate content from a dataset exceeding 500 complex legal references — all while ensuring legal safety and usability in real-world workflows.
The Challenge
The client needed to transform how legal professionals interact with massive legal corpora. Their requirements were ambitious:
- Dataset Scale: Over 1 million text chunks across 500+ legal references (case law, statutes, regulations)
- Accuracy: Responses had to be jurisdictionally relevant and legally sound
- Document Drafting: Capable of generating contracts like NDAs and employment agreements, tailored to context
- Interpretability: Needed to show sources for every generated clause
- Query Safety: Detect and deflect ambiguous or potentially misleading prompts
Our Solution
We developed a custom Agentic RAG system purpose-built for legal use cases. Key components included:
Intelligent Chunking and Preprocessing
- Adaptive chunk sizes based on legal document structure (e.g., clause boundaries, headings)
- Custom tokenization for handling footnotes, citations, and embedded references
- Jurisdiction and document-type tagging for filtered retrieval
Hybrid Retrieval Engine
- Dense retrieval using domain-tuned embedding models optimized for legal language
- Sparse retrieval via keyword and BM25 scoring for interpretability and redundancy
- Hybrid fusion to rank and combine results from both methods
Agentic Reasoning Layer
- Multi-step planning for complex queries (e.g., “Find and compare non-compete clauses in California and Texas”)
- Constraint-based reasoning: agent ensures outputs match legal context like jurisdiction, contract type, and role
- Supports tasks like clause comparison, section summarization, and document assembly
Guardrails & Safety
- Query ambiguity detection with clarifying follow-ups (e.g., “Which jurisdiction is this NDA intended for?”)
- Filtered generation using prompt engineering and model constraints to avoid hallucinations
- Internal red-teaming to test edge cases and bias
Human-in-the-Loop Development
Throughout the build process, we partnered closely with the client’s in-house legal team:
- Weekly reviews with annotated test queries
- Iterative refinement of retrieval accuracy and generation quality
- Integrated feedback from real legal workflows into the system’s evolution
Results
System Capabilities
- >1M chunks indexed, updated continuously
- Jurisdiction-aware generation for contracts across all 50 U.S. states
- 99.2% source citation accuracy in top-k outputs
- Sub-2 second average response time for most queries
Business Impact
- Time savings: Legal teams report 70–80% time reduction in drafting first-pass contracts
- User adoption: Used daily by lawyers, HR, and compliance teams
- Risk mitigation: Guardrails have flagged and corrected over 2,000 ambiguous or potentially risky prompts
Lessons Learned
- Legal data requires legal-aware models — off-the-shelf embeddings fail to capture nuance
- Retrieval quality is everything — hallucinations often stem from poor context, not poor generation
- Agentic systems unlock complexity — simple RAG breaks down under compound legal tasks
- Human feedback is critical — legal precision can't be judged without legal expertise
Conclusion
This project showcases the power of advanced Retrieval-Augmented Generation systems in the legal domain. By blending agentic reasoning, hybrid search, and deep domain integration, we helped our client deliver a production-grade legal assistant capable of real-world document drafting and legal query handling at scale.
As legal professionals increasingly adopt AI into their workflows, this kind of scalable, explainable, and safe RAG architecture will become the new standard.