Savvycom

An AI-powered Yard Management System designed to provide real-time visibility and intelligent tracking of containers across high-volume logistics yards. By integrating AI cameras, computer vision, and digital yard visualization, the solution significantly improved container traceability, reduced search time, and optimized yard operations at scale.

Client: The client , a global logistic provider, operates multiple large-scale logistics yards handling 400–500 container movements per day, with over 500 containers stored simultaneously at each site.

They required a solution to:

Achieve real-time visibility of container positions and status
Eliminate manual and error-prone yard records
Track container movements automatically across the yard
Optimize yard operations and reduce container dwell time

Challenges:

• Limited Real-time Visibility: Difficulty locating containers, causing delays in yard operations.

• Lack of Specialized YMS: Reliance on manual records led to inconsistencies and operational errors.

• Untracked Container Movements: Internal movements were not recorded, resulting in misplacement and loss of traceability.

• High Operational Load: Large daily container volume created bottlenecks and prolonged handling cycles.

Solutions:

• AI Camera Integration: Automated recognition of container IDs, trailer IDs, and truck license plates at entry/exit points.

• Real-time Container Tracking: Applied object detection and tracking to monitor container movements across the yard.

• 2D Digital Yard Mapping: Visualized the exact position and status of each container in real time.

• Digital Move Orders: AI-validated container placement based on camera detection to ensure correctness.

• Operational Dashboards: Enabled instant container search, dwell time monitoring, and operational insights.

Results:

• 95% accuracy in container placement and movement tracking

• 60% reduction in container search and retrieval time

• 35–40% increase in overall operational productivity

• Significant reduction in manual errors and container misplacement

Techstacks:

AI / Computer Vision: YOLOv8, PaddleOCR, DeepSORT

Backend & Services: Python, .NET

Data & Storage: PostgreSQL, MinIO

Streaming & Monitoring: Kafka, Prometheus

Integration: RESTful APIs

Cloud Platform: Microsoft Azure

An automated contract review platform leveraging Google Cloud AI to "read" logistics contracts, identifying risks and critical clauses to replace a slow, error-prone manual process.

Client: LX Pantos, a leading logistics company in South Korea.

Challenges:

• Manual Processes: Contract review was heavily reliant on legal teams, leading to delays.


• Error-Prone Reviews: Human oversight increased the risk of missing critical clauses.


• Scalability: Growing volume required a scalable solution.


• Data Management: Extracting insights from unstructured data was challenging.

Solutions:

• Design & Consultation: Workflow analysis to understand compliance needs.


• Model Development: Custom ML models (Vertex AI) to analyze contracts and flag risks.


• Data Management: BigQuery to store/query large volumes of contract data.


• Automated Workflows: Seamless integration with internal document systems

Results:

• 50% reduction in contract review time.


• 95% accuracy in identifying critical clauses and risks.


• Scaled to process over 1,000 contracts per month.

Techstacks:

• AI/NLP: Vertex AI, TensorFlow, Python (spaCy, NLTK)


• Data: BigQuery


• Cloud: Google Cloud Platform (GCP)

An AI-driven solution designed to digitize large-scale construction blueprints and automatically extract material tables, quantities, and specifications. The system replaces manual document handling with computer vision and OCR technologies, significantly improving operational efficiency, accuracy, and cost control in large infrastructure projects.

The client is a large Japanese construction corporation specializing in:
- Large-scale infrastructure projects
- Commercial buildings
- Residential complexes

To support operational excellence and digital transformation initiatives, the client sought to:
- Convert blueprint drawings into searchable digital assets
- Automatically extract material quantities and specifications
- Reduce human effort and manual data entry errors
- Standardize data storage for cost control and project planning

The project team faced several critical challenges:

1. Massive Volume of Documentation: Hundreds of architectural drawings across different project phases.
2. Difficulty Locating Material Details: Manual review required significant time and effort.
3. High Risk of Human Error: Manual data entry led to frequent inaccuracies.
4. Lack of Standardized Data Format: Extracted information was fragmented and inconsistent.

Solutions:

• Blueprint Digitization: Standardized and processed scanned construction drawings for AI analysis.

• Object Detection: Applied computer vision models to detect structural elements and material-related objects within drawings.

• Text Extraction (OCR): Used OCR to extract material tables, quantities, and specifications directly from drawings.

• Automated Data Pipeline: Delivered structured material data via APIs for downstream systems (estimation, procurement).

Results:

• 20% reduction in operational costs

• 65% improvement in process automation

• 35% increase in overall productivity

• Significant reduction in manual data entry errors

Techstacks:

AI / ML: YOLO-v8, PaddleOCR, OpenCV, TensorFlow
Programming Languages: Python, JavaScript (React)
Frameworks & Libraries: FastAPI, React
Data & Storage: PostgreSQL, Azure Blob Storage
Platform: Microsoft Azure
Deployment: Docker-based microservices

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