I. BA-SHI YUEXIN LOGISTICS
1.1 About BA-SHI YUEXIN Logistics
Shanghai BA-SHI YUEXIN Logistics Development Co., Ltd. is a Shanghai-based logistics enterprise that partners with major ports nationwide and serves global markets. Committed to delivering high-quality, efficient logistics solutions, BA-SHI YUEXIN meets client demands while driving societal progress. With a seasoned logistics team and advanced technologies, the company prioritizes both service quality and user experience. Beyond domestic operations, BA-SHI YUEXIN actively expands its international presence, collaborating with logistics partners across multiple countries and regions to offer secure, rapid, and convenient global logistics services. Concurrently, the company continuously enhances its international logistics management capabilities and broadens service networks to deliver comprehensive, professional, and efficient cross-border logistics solutions.
1.2 Challenges
The core business—sea freight booking services—faces critical operational challenges:
Lack of standardized interfaces: Heterogeneous booking APIs across shipping carriers complicate system integration.
Diverse document formats: Booking order formats vary significantly between carriers, increasing data processing complexity.
Inconsistent content layouts: Non-uniform document structures hinder rapid information extraction and verification.
Non-standardized content: Irregular or incomplete key field entries in booking orders escalate manual audit workloads.
These issues intensify during annual peak booking seasons, demanding excessive human and material resources to maintain operational continuity, thereby straining efficiency and cost control.
II. Solution Architecture
2.1 Standardized Data Specifications
Established unified data standards for critical fields:
Booking instruction types
Carrier specifications
Port of loading/destination
Shipping marks
Field definitions, data types, and descriptive norms
2.2 Content Extraction via AWS Bedrock
Leveraged Claude 3.5 Sonnet on AWS Bedrock to extract structured data from images/texts per predefined specifications.
2.3 Prompt Engineering Management
Implemented AWS Bedrock's prompt management for:
Systematic prompt optimization
Version control
Iterative debugging
2.4 Automated Workflow Orchestration
Designed AWS Bedrock Flow to manage multi-stage Claude 3.5 interactions:
File type classification
Watermark detection
Format conversion
Image/text extraction
Structured output generation
2.5 Compliance Safeguards
Deployed AWS Bedrock guardrails to validate input content authenticity and prevent unauthorized data submissions.
III. AWS Infrastructure Implementation
3.1 Architecture Design
Built a purpose-driven AWS architecture integrating multiple services, with Bedrock components significantly accelerating implementation and validation cycles. (Architecture diagram referenced)
3.2 Key Features
Prompt Lifecycle Management: Enabled gray release and rollback capabilities through historical version control, reducing maintenance costs by 40%.
Modular Flow Configuration: Achieved 60% faster development cycles via visual workflow design, enhancing adaptability to evolving business requirements.
IV. Results & Benefits
Project Outcomes
100% recall rate for standardized content extraction across diverse file formats and layouts.
>95% document recognition accuracy with >90% field-level precision.
30%+ operational efficiency gain by eliminating manual template adjustments for API integrations.
