Enterprise multi-agent system managing 24 autonomous AI agents for document processing, data analysis, and customer communication. Processes 1,800+ tasks per hour with 99.2% accuracy — cutting operational costs by 80% for a US-based logistics firm.
Vertex Logistics, a mid-market freight company based out of Houston, TX, was drowning in paperwork. Their operations team manually processed bills of lading, customs declarations, and shipping manifests — roughly 200 documents a day with a 12.4% error rate. Each mistake meant delayed shipments, compliance fines, and unhappy clients. They'd tried two RPA tools before and both failed on unstructured document formats.
Their VP of Operations reached out to us after seeing our work on LinkedIn. His exact words: "We need something that actually understands messy real-world documents, not just templates."
We built a multi-agent orchestration platform where 24 specialized AI agents collaborate to process, validate, route, and archive documents end-to-end. Each agent has a narrow responsibility — one extracts tables, another validates against shipping databases, another flags anomalies for human review. A central orchestrator agent manages task delegation, retry logic, and SLA monitoring.
Central coordinator that decomposes incoming work, delegates to specialist agents, and monitors SLA compliance in real-time.
Vision + LLM agents that parse handwritten notes, scanned PDFs, and irregular table layouts with 99.2% accuracy.
Cross-references extracted data against shipping databases, tax codes, and historical records to catch errors before they propagate.
Automatically routes processed documents to the right department, flags exceptions, and escalates edge cases to humans.
Real-time dashboard showing agent status, throughput, accuracy metrics, and SLA tracking across all document pipelines.
Automatic retry with alternative strategies when an agent fails. Learns from failures to improve future processing.
Production-grade infrastructure running on the client's AWS account with full data sovereignty.
We were drowning in manual document processing. We'd tried two RPA solutions before Rivan.ai and both choked on our messy, inconsistent paperwork. Rivan.ai's team flew out to Houston and spent three weeks just watching our team work before they wrote a single line of code. That told me they were different. Six months later, we've cut our ops team from 14 people to 4 — and those 4 now handle the complex stuff that actually needs a human brain. The system caught 23 errors during shadow testing that our people missed. I don't say this lightly: this project paid for itself in the first 7 weeks.
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