AI automation and integration

AI Container-Aging Monitor
Requirement for shipping company
1. Objective
It manages approximately 35,000 containers across 100+ ports worldwide. A significant portion of these containers sit empty, idle, and unmoved by clients longer than they should—tying up valuable assets and creating avoidable costs. Currently, identifying and addressing these idle containers is a manual, time-consuming process that often results in delayed action and lost revenue.

This proposal outlines an automated monitor that:

Detects aging/standby containers past agreed thresholds

Contacts the responsible client directly via personalized, AI-drafted emails

Escalates through a defined follow-up sequence if the client ignores communications

Provides the director with a summarized, decision-ready report instead of raw case data

The solution uses two AI touch-points in a scheduled workflow system—not a live chatbot. This ensures cost and complexity scale with how many containers actually go idle, not with overall email volume or container count.

2. Proposed Solution Overview
The system will operate as a scheduled workflow with the following core components:

2.1 Detection Module
Function: Automatically flag containers that remain idle or standby beyond an agreed threshold

Data Source: Read from container management system (ClimaxSuite)

Trigger: Scheduled checks (e.g., daily) against container status data

Key Metrics: Time since last movement, current status (empty/idle), location, client assignment

2.2 AI Drafting Module
Function: Generate a personalized, professional email to the responsible client for each flagged container

Personalization: Includes container number, idle duration, port location, and a clear call to action

Tone: Professional and firm but not confrontational—preserving client relationships

Output: Ready-to-send email drafts for review or direct sending

2.3 Response Tracking Module
Function: Monitor for client replies versus silence within a defined window

Tracking: Email open rates, reply detection, timestamps

Window: Configurable (e.g., 48–72 hours per follow-up)

2.4 Escalation Logic Module
The system follows a clear decision tree:

Scenario Action
Client complies (moves container, provides confirmation) Close case, log resolution
Client replies with questions/stalls Re-send up to 2–3 times on a cadence, then escalate to director
Client ignores all communications Escalate immediately to director with full case history
2.5 AI Reporting Module
Function: Summarize open and escalated cases into a single decision-ready report

Format: One-page executive summary with case counts, aging metrics, and recommended director actions

Output: Delivered via email or dashboard—no raw case data to sift through

3. Engagement Structure
The project is structured in two phases. Production data access for CIM Shipping's ClimaxSuite deployment is not yet confirmed—Phase 1 resolves this while building a working prototype so Phase 2 can be priced precisely.


Deliverables:

Data Access Confirmation: Technical assessment of ClimaxSuite API/data export capabilities; confirm how container data will be accessed

Business Rules Definition: Agree on idle-time threshold and follow-up schedule with CIM Shipping stakeholders

Working Prototype:

Built and tested on sample data (not live production)

Demonstrates: detection → email drafting → response tracking → escalation logic → reporting

Runs locally or in a test environment


Deliverables:

Live Data Connection: System connected to CIM Shipping's real ClimaxSuite data

Fully Automated Operation: End-to-end workflow runs autonomously:

Detection → AI email → Follow-up reminders → Escalation → Reporting

Production Deployment: Hosted in a secure environment (cloud or on-premise)

Handover & Training:

Short walkthrough for CIM Shipping's operations team

Documentation covering configuration, monitoring, and maintenance

Administrative dashboard access (if applicable)

Support: 30 days of post-deployment support included

4. Deliverables Summary
# Deliverable Phase
1 Automatic detection of containers sitting idle beyond an agreed time limit 1 & 2
2 AI-written email sent to the client responsible for each idle container 1 & 2
3 Tracking of whether the client replies or ignores the email 1 & 2
4 Automatic follow-up (up to 2–3 reminders) if the client doesn't act 1 & 2
5 Automatic escalation to the director if the client ignores all reminders 1 & 2
6 Single summarized report for the director showing cases needing decision or a phone call 1 & 2

5. Technical Approach
5.1 Architecture
Scheduler: Cron-based or orchestrated workflow system (e.g., Apache Airflow, AWS Step Functions)

AI Integration: Large Language Model API for email drafting and report summarization

Email Delivery: SMTP or email API (SendGrid, AWS SES)

Data Storage: Secure database for case tracking, logs, and audit trail

Dashboard: Optional lightweight interface for monitoring (Phase 2)

5.2 Technology Stack (Proposed)
Backend: Python (FastAPI/Flask) or Node.js

AI: OpenAI GPT or equivalent for drafting and summarization

Database: PostgreSQL or MongoDB

Email: Integrated delivery service with tracking

Deployment: Cloud or on-premise (customer preference)

5.3 Security & Compliance
Secure handling of client contact information

Audit logging for all system actions

Data encryption at rest and in transit

Access controls for administrative functions

6. Assumptions & Dependencies
Assumption Dependency
CIM Shipping can provide sample data for testing Phase 1 start
ClimaxSuite offers API or export capability for live data access Phase 2 readiness
Client contact details are maintained in CIM Shipping's system System can look up responsible client
Email delivery infrastructure is available or can be set up Integration work
Agreement on idle-time threshold and follow-up schedule Client input during Phase 1