Logistics Control Tower
for Shipment Exception Management
A Canadian logistics organization uses an AI-powered control tower to monitor shipments, detect exceptions, and coordinate resolution through Agentic AI and RAG.
Business Process Challenges
A logistics organization needed to improve shipment visibility and reduce the operational effort involved in monitoring deliveries, identifying exceptions, investigating disruptions, and coordinating resolution across multiple logistics workflows. Existing operations relied heavily on manual shipment monitoring, making it difficult to identify issues early and respond consistently.
Key challenges included:
- Manual shipment monitoring: Teams had to continuously monitor shipment activity across multiple operational stages and systems.
- Delayed exception detection: Potential delays, missed milestones, route disruptions, and delivery exceptions were difficult to identify early.
- Repetitive investigation: Operations teams spent significant time investigating shipment issues and gathering relevant information.
- Manual escalation: Unresolved exceptions required teams to manually coordinate escalation and follow-up.
- Limited operational visibility: Teams lacked a structured view of the overall exception-resolution process.
Our Strategic Approach
Softree designed an AI Logistics Control Tower using Amazon Bedrock, Amazon Bedrock AgentCore, and Retrieval-Augmented Generation (RAG) to support intelligent shipment monitoring and exception management.
The implementation included:
- Intelligent Shipment Monitoring: The control tower continuously evaluates shipment information to identify delays, missed milestones, delivery exceptions, route disruptions, and other operational status changes requiring attention.
- Exception Detection: The system evaluates operational anomalies, categorizes exceptions based on conditions, and prioritizes cases requiring faster attention, reducing dependency on shipment-by-shipment manual monitoring.
- RAG-Powered Operational Knowledge: RAG retrieves relevant logistics policies, shipment handling guidelines, exception-resolution procedures, service-level requirements, and internal operational documentation to support more grounded resolution decisions.
- Multi-Agent Coordination: Specialized Monitoring, Exception, Resolution, and Coordination Agents were designed to handle different responsibilities across the shipment exception workflow while maintaining a coordinated process.
- Exception Resolution: Once an exception is identified, the solution can coordinate actions such as requesting information, triggering operational tasks, notifying responsible teams, retrieving procedures, escalating issues, and tracking resolution progress. Complex cases remain available for human review.
How we delivered it.
Explore the Solution Through visuals
What changed for the client.
The AI Logistics Control Tower transforms shipment monitoring from a primarily manual process into an AI-assisted logistics operations workflow. Specialized agents can monitor shipment activity, identify exceptions, retrieve operational knowledge, and coordinate resolution workflows while keeping human teams involved when operational judgment is required.
Target outcomes included:
- Up to 40% reduction in repetitive shipment-monitoring effort
- Up to 35% faster identification of shipment exceptions
- Up to 30% reduction in manual exception investigation
- Up to 30% faster operational escalation
- Improved shipment visibility
- More consistent exception handling
- Faster access to operational knowledge
- Better coordination between logistics teams
The numbers behind the rollout.
The full integration layer.
Other engagements worth a look.
Emergency Department Performance Analytics Platform
A Microsoft Fabric and Power BI analytics platform helped a multi-specialty healthcare network reduce emergency department wait times by approximately 34%.
Read case study→
Employee Knowledge Discovery and Search Assistant
A leading IT services organization improved employee knowledge access with a SharePoint-based AI assistant, reducing manual document search and review effort.
Read case study→
AI-Powered Healthcare Operations Knowledge Assistant
Healthcare knowledge assistant reduces document search time by up to 35% while improving access to approved operational procedures.
Read case study→
AI Healthcare Patient Care Coordinator
Manual patient coordination created repetitive administrative work, delayed responses, and limited support availability, making it difficult for care teams to efficiently manage routine appointments, follow-ups and patient requests.
Read case study→
Metadata-Driven Data Ingestion with Microsoft Fabric
A U.S. based SaaS company replaced dataset-specific ETL pipelines with a reusable Microsoft Fabric ingestion framework for hundreds of tables.
Read case study→
Configurable Report Generation & Automation Platform
Streamline professional report creation with configurable templates, automated workflows, AI-assisted content generation, human review, and Word document automation.
Read case study→Frequently asked questions.
Let's Start a Conversation
Extend Your Engineering Capacity.
Not Your Hiring Complexity.
Build, scale and deliver more with an engineering partner that works as an extension of your team.
What we offer
- Agentic AI & Automation
- Web Application Development
- Power Platform & SharePoint
- Data Engineering & Power BI
- Mobile App Development
HOW WE CAN EXTEND YOUR TEAM
AI ENGINEERING
- Agentic AI
- AI Automation
- RAG
MICROSOFT
- Azure
- Fabric
- Power Platform
QUALITY ENGINEERING
- AI Testing
- Security Testing
- Automation Testing
SOFTWARE ENGINEERING
- React / Next.js
- Node.js / Python
- FastAPI
CLOUD & DATA
- Azure / AWS
- Data Engineering
- DevOps
Got a question, challenge, or idea?
Fill out the form or pick a time on our scheduler:
30-min discovery call
Same Calendly as our booking page · instant invite
11th Floor, Prestige Tech Park, Platina 2 · Outer Ring Rd, Kadubeesanahalli, Bengaluru 560087
PLOT 5C/1283, SECTOR-10, CDA, Cuttack, Odisha 753014, India