Autonomous AI Agent Workflows & Enterprise RAG Knowledge Retrieval
Client Profile: National Logistics & Supply Chain Enterprise
Engineered an autonomous multi-agent system and vector RAG pipeline indexing 400,000+ shipping manifests, customs paperwork, and ERP logs to automate ticket resolution and route triaging in real time.
68%
Manual Triage Reduction
1.2s
Average Query Latency
99.4%
Retrieval Accuracy
3.5x
Faster Operations SLA
The Business Challenge
Operations teams were overwhelmed with over 15,000 weekly vendor inquiries, customs discrepancy notices, and invoice validation requests. Manual lookup across disconnected ERPs took 12–18 minutes per inquiry.
Engineering Architecture & Delivery
Built a hybrid RAG pipeline using LangChain, Pgvector, and OpenAI embeddings, paired with autonomous Python agents capable of verifying document signatures, validating warehouse inventory levels, and drafting approved resolutions.
Quantified Impact
Automated 68% of tier-1 support and invoice validations with zero human intervention. Reduced inquiry response time from 15 minutes to under 2 seconds while preserving strict enterprise audit logs.
Technical & Architecture Highlights
- Chunked vector search with Pgvector & hybrid BM25 lexical ranking
- Autonomous tool-calling agents for SAP ERP and database validation
- Human-in-the-loop review threshold for high-value transactional decisions
- Encrypted tenant data isolation with SOC-2 compliant audit trails
“Vedrisha Technology designed an AI automation engine that transformed our supply chain back-office. What previously took 15 people all day is now handled autonomously in seconds.”
Vice President of Technology · Global Logistics Group