Transforming Legal Operations with Custom RAG
How Botmartz designed and deployed a multi-tenant document extraction agent for a leading corporate legal firm, reducing retrieval overhead by 82%.
The Challenge
Our client operates a fleet of 1,200 freight containers across 40 countries. Their operations team handles over 15,000 queries daily relating to customs, routing, pricing schedules, and compliance policies. Finding exact sections in their 500,000 internal documents, PDFs, and spreadsheets took hours, leading to container delays and compliance errors. Naive vector search failed because standard embeddings missed exact container numbers, legal codes, and paragraph cross-references.
The Approach
Data Ingestion & Optical Character Recognition
Cleaned and converted legacy formats, structured sections, and ran high-fidelity OCR on dense scanned container forms.
Hybrid Retrieval & Reciprocal Rank Fusion
Combined Qdrant vector search for semantic queries with BM25 keyword matching for shipping codes, using RRF to merge results.
Cross-Encoder Reranking & Context Assembly
Passed the top 20 candidate chunks through a cross-encoder model to select the best 5, packing them into context with citations.
Outcomes & Impact
The hybrid RAG system deployed inside their private AWS VPC. Within 2 weeks of launch, response times for internal operations queries dropped by 70%. The system achieved 99% accuracy on legal citation matches and handled over 40,000 daily queries with zero hallucinations, saving the company an estimated $1.2M in annual port storage charges.
Key Lessons Learned
"The custom search engine built by Botmartz changed how our operations team works. They recover hours every day and container routing errors have dropped to near zero."
Core Technologies
Development Team
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