Setting New Standards in Supply Chain Data Quality

Key Takeaways

More than half of international carriers prefer to use paper-based processes, creating significant data challenges
Trax's AI Extractor goes beyond traditional OCR with true concept comprehension
The human-in-the-loop approach creates a continuous learning cycle, improving accuracy over time
Implementation is already underway, with complete paper match capability coming soon
Quality document data forms the foundation for more advanced supply chain intelligence

Did you know that over half of international carriers prefer to operate through paper-based processes despite the widespread adoption of electronic data interchange (EDI)? This creates a significant challenge in supply chain management, where accurate data is essential for effective decision-making. Trax is addressing this challenge head-on with the AI Extractor technology that goes far beyond traditional methods.

Trax's AI Extractor technology differs from conventional approaches by truly comprehending document concepts and relationships, not just identifying text locations. This matters for supply chain professionals seeking to improve data quality, enhance efficiency, and gain better control over their transportation spend.

The Data Challenge in Modern Supply Chains

For global enterprises with complex supply chains, managing freight documentation remains a persistent challenge. Despite significant technological advances in other areas, paper documents continue to dominate many aspects of freight management. Trax's research shows that over half of carriers still rely on paper-based processes, primarily PDFs, creating a significant processing burden for companies.

Traditional Optical Character Recognition (OCR) technology has attempted to address this issue, but with limited success. Conventional OCR merely identifies where information is located on a document without truly understanding its meaning or context. This leads to several critical problems:

Inconsistent data extraction across different document formats
High error rates requiring extensive manual correction
Inability to handle complex or non-standard documents
Limited capacity to adapt to new document types

These limitations have real business consequences. Poor data quality costs companies more than $600 billion annually. In transportation specifically, inaccurate freight documentation directly impacts billing accuracy, compliance, and the ability to make informed decisions.

How Trax's AI Document Understanding Works

Trax's approach represents a fundamental shift from location-based OCR to genuine document understanding. While traditional OCR simply identifies text position, Trax's AI solution comprehends document concepts, relationships, and structures.

The AI Extractor technology focuses on two primary document types:

Invoices - which constitute the majority of paper-based carrier documents
Complex rate contracts - such as multi-page carrier agreements that previously required extensive manual extraction

What sets Trax's solution apart is its multi-model approach. The system employs multiple large language models optimized for specific document types, with a sophisticated confidence-scoring mechanism that identifies uncertainty in extraction results. This creates a far more accurate and adaptable system than traditional OCR methods.

The AI Extractor technology enables the extraction, translation, and normalization of data into a comprehensive data model, creating a single source of truth for all transportation data.

The Human-in-the-Loop Advantage

One of the most innovative aspects of Trax's approach is the human-in-the-loop interface, which has already been implemented in production. Rather than requiring staff to review entire documents, the system directs human attention only to fields with low confidence scores.

This targeted approach provides several key advantages:

Staff focus only on the most challenging data points
Human corrections feed back into the model
The system creates a continuous learning cycle
Accuracy improves over time with each document processed

Implementation is progressing steadily, with the AI Extractor already functional in Trax's test environment. The current timeline includes complete paper match capability within weeks and integration with the top paper-based carrier invoices by quarter's end.

This combination of advanced AI with strategic human oversight creates Trax's Freight Data Management approach that optimizes both technological capabilities and human expertise.

Real Business Impact for Supply Chain Leaders

For supply chain leaders, the business impact of this technology is substantial and measurable:

Significantly reduced manual data entry time
Improved data accuracy and consistency
Accelerated carrier onboarding process
Enhanced compliance with regional documentation standards

By enhancing document processing, Trax's solution directly addresses a critical barrier to supply chain optimization. For companies with complex global supply chains, the financial impact is clear - Trax customers save 5-7% of their annual transportation spend through improved data quality and processing.

The improved data quality also enables better downstream processes, including more accurate auditing, improved analytics, and more reliable business intelligence. Most importantly, it allows companies to shift their focus from manual data processing to strategic decision-making.

The Future of Document Intelligence in Supply Chain

Trax's document understanding technology is just the first component of a comprehensive AI strategy. While delivering immediate value through improved data extraction, it also establishes the foundation for more advanced capabilities.

With high-quality, normalized data as the starting point, companies can progress to:

Pattern recognition in freight audit exceptions
Contextual understanding of transportation contracts
Strategic modeling of transportation spend
Proactive identification of optimization opportunities

As Trax's Chief Product Officer, Jason Westigard emphasizes, companies gain the most value from AI when they focus on data quality first before moving to more advanced applications. Trax's approach follows this proven path to success.

As data extraction becomes more reliable and comprehensive, supply chain leaders can shift their focus from tactical concerns about data accuracy to strategic questions about cost optimization and service improvement.

Unlock the Power of Quality Supply Chain Data

Our AI Extractor technology addresses a fundamental challenge in transportation management: obtaining high-quality data from diverse document sources. By combining advanced AI models with targeted human oversight, the system creates a continuous improvement cycle that enhances accuracy and efficiency over time.

For supply chain professionals, this means spending less time wrestling with data extraction and more time making strategic decisions based on reliable information. The foundation of quality data enables everything from better freight auditing to advanced spend optimization.

Ready to enhance your freight audit data quality? Contact us today to discover how our AI Extractor technology can create a solid foundation for your transportation spend management strategy.