// Case study
RAG workflow for legal due-diligence document review
LLM extraction with citation anchors, 200-page datarooms triaged in hours, not days, with human sign-off gates.
A corporate law boutique supporting M&A with 15 associates faced high-volume PDF datarooms. Partners needed faster first-pass review without compromising privilege or accepting unverified AI citations.
11 → 3.5 hours
First-pass review time
200-page dataroom sets
Zero accepted
Unverified citations (pilot)
Human confirm gate per clause
15
Associate team
Per-matter workspace isolation
200 pages
Document volume
Typical M&A dataroom set
Delivered by Shivraj · Published December 18, 2025 · 10 min read
Client context
A corporate law boutique supporting M&A, 15 associates, high volume of PDF datarooms, needed faster first-pass review without compromising privilege or accuracy hallucinations could destroy.
Our approach
Simplileap built a private RAG pipeline: documents ingested to Azure Blob; chunking with layout-aware parser; embeddings in pgvector; GPT-4o retrieval with mandatory citation spans; LangSmith trace logging.
Technical implementation
Governance: no training on client data; per-matter workspace isolation; associate must click confirm per extracted clause; export audit PDF for file.
The challenge
Problems: scanned PDFs OCR quality poor, human flag queue; Tamil and Hindi exhibits required multilingual embedding model swap; cost caps per matter with token budgeting.
Results & impact
Outcome: median first-pass review time 11 hours → 3.5 hours on 200-page sets; partners reported zero unverified citations accepted in pilot. Firm anonymized, corporate law practice.
// Related services
CIN
AAU-8582
Startup India
DIPP83124
Founded
November 2020
Office
Residency Rd, Bengaluru, India
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