Case Study
MULTI-AGENT OPS AUTOMATION
Executive Summary
A global maritime shipping and crew-management operator running large vessel fleets, where vessel ETAs, seafarer contracts and crew rotations must be tracked continuously.
- Industry:
- Geography:
- Capability: Agentic AI Platform
- Technologies:
- Key Outcome: • Automated ETA prediction
The Challenge
A global maritime shipping and crew-management operator running large vessel fleets,
where vessel ETAs, seafarer contracts and crew rotations must be tracked continuously.
Delivery
A LangGraph-orchestrated multi-agent system deploys task-specific agents for ETA prediction, contract-expiry retrieval and crew-replacement planning. Each agent can run a different LLM tuned to its task complexity, cost and accuracy needs, while live loggers and trackers give full visibility into agent activity and execution.
TECH STACK & INTEGRATIONS
LangGraph — multi-agent orchestration framework
Azure OpenAI (o1) — reasoning for complex, multi-step tasks
Task-Specific Agents — ETA, contracts & crew planning
Swappable LLM per Agent — accuracy vs. cost tuning
Live Logger & Trackers — real-time agent monitoring
Integrations — fleet ops, HR/crew & contract systems
Results
• Automated ETA prediction
• Proactive contract-expiry monitoring
• Intelligent, timely crew replacement
Deep Dive into the Outcomes
Get the detailed PDF report covering the complete problem-solution-impact lifecycle and measurable ROI metrics for this project.
Related Case studies
GCC Bank Achieves Full Cloud Sovereignty
Government Agency Deploys AI Concierge at Scale
