ENTERPRISE RAG
Enterprise Knowledge Search for Global Logistics Company
Built a secure, enterprise RAG system to unify and search across 100M+ freight documents, SOPs, FAQs, and regulations.
Analytics Dashboard
Real-time Metrics+70% Recall
70%
Faster information retrieval
60%
Reduction in support tickets
ClientGlobal Logistics Leader
Timeline12 Weeks
Team Size3 Engineers
CapabilityENTERPRISE RAG
The Challenge
Retrieving freight documents and custom regulations was slow, causing warehouse delays and incorrect customs filings.
The Approach
1
Data Collection
Aggregated SOPs, PDFs, and freight manifests.
2
Semantic Parsing
Configured hybrid search chunking for domain terminology.
Outcomes & Impact
Reduced search latency by 70%, automating lookup for packer operators.
Key Lessons Learned
✓
Vector search alone is insufficient for structured freight and customs queries; hybrid search with RRF is mandatory.
✓
Document chunking must align with layout structures (headers and tables) rather than fixed character offsets.
✓
Reranking is the highest leverage, lowest cost way to improve recall in large document sets.
"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."
MV
Markus Vance
VP of Operations, Global Logistics Leader
Core Technologies
LangChainPineconeFastAPIReact
Development Team
KG
Karan Goel
AI Engineer
PB
Promit Basu
AI/ML Engineer
SS
Sanjay Singh
Lead Architect
Related Cases
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