LLM Results Analyzer: Unified Analysis Framework
Combining Custom Clarification and Custom Optimization Results
Executive Summary
The LLM Results Analyzer integrates outputs from the Custom Clarification Project (theoretical HS code data) and Custom Optimization Project (real-world product descriptions) into unified classification recommendations. This solution tackles the challenge of leveraging both theoretical compliance knowledge and practical trade data to provide accurate and well-justified HS code classifications.
The system features a unified endpoint that simultaneously sends requests to both LLM systems—one processing theoretical HS code definitions and the other analyzing real-world product data. After both systems complete their analysis, a third specialized LLM analyzes and synthesizes the results from both sources, providing users with recommendations that consider both legal compliance requirements and practical trade precedent.
This integrated approach eliminates the need for users to manually compare and reconcile outputs from separate systems. Instead of choosing between theoretical accuracy and practical relevance, users receive unified recommendations that combine the strengths of both approaches, improving classification confidence and decision-making speed.
The analyzer framework processes requests efficiently by running both analyses in parallel, reducing total response time while providing more comprehensive results than either system could deliver independently. This solution represents a significant advancement in HS code classification technology, providing unprecedented integration of regulatory knowledge and practical trade intelligence.
Figure 1.1. Graphical interface of the customs HS code classification system showing the outputs of the Custom Clarification API (left) and the Custom Optimization API (right). The left panel displays the system's automated analysis of a product description, based on the theoretical data with recommended HS codes. The right panel lists potential optimized product descriptions from the 65.5 million dataset. This figure illustrates how the system combines both results and analyzes final HS code suggestions. For more, you can watch the video at the link.
Introduction
The Custom Clarification Project and Custom Optimization Project created two powerful but separate systems for HS code classification. The Custom Clarification Project provided access to theoretical HS code definitions, official regulations, and legal frameworks essential for compliance accuracy. The Custom Optimization Project offered insights from 65.5 million real-world product descriptions, reflecting actual trade practices and classification precedents.
While both systems delivered value independently, users faced the challenge of determining how to best utilize both sources of information for optimal classification decisions. Customs officers and businesses needed to understand not only what the official regulations specified, but also how similar products were classified in practice. However, accessing and comparing results from two separate systems created workflow inefficiencies and required users to manually synthesize potentially conflicting recommendations.
The fundamental question became: how could organizations leverage both theoretical accuracy and practical relevance without creating additional complexity for end users? The answer required developing an integration layer that could automatically combine insights from both systems and provide unified, well-reasoned recommendations.
The LLM Results Analyzer addresses this integration challenge through an orchestration framework. When users submit classification requests, the system automatically dispatches the query to both the Custom Clarification and Custom Optimization endpoints simultaneously. This parallel processing approach ensures efficient use of system resources while gathering information from both theoretical and practical perspectives.
After both systems complete their analysis, a specialized analyzer LLM reviews the results from both sources, identifies areas of agreement and divergence, and synthesizes the information into coherent, justified recommendations. This approach ensures users receive the benefits of both regulatory compliance and practical trade intelligence without the complexity of managing multiple separate systems.
The Problem
The existence of two separate classification systems created an integration challenge that limited the potential value both systems could deliver when used together. Users found themselves with access to theoretical knowledge through the Custom Clarification Project and extensive practical data through the Custom Optimization Project, but no efficient way to combine these resources.
The primary challenge was workflow complexity. Users had to manually submit the same query to both systems, wait for separate responses, and then attempt to reconcile potentially different recommendations. This process was time-consuming and required expertise to properly interpret and synthesize the different types of information each system provided.
Inconsistent results between the two systems created confusion and decision paralysis. The Custom Clarification Project might suggest one HS code based on strict regulatory interpretation, while the Custom Optimization Project might recommend a different code based on how similar products were actually classified in practice. Users lacked guidance on how to resolve these discrepancies or determine which recommendation to prioritize.
The manual comparison process was prone to human error and inconsistency. Different users might interpret the same pair of results differently, leading to varied final decisions. This inconsistency undermined the reliability and standardization that both underlying systems were designed to provide.
Additionally, the separate system approach failed to leverage the complementary strengths of both data sources optimally. Users couldn't easily identify cases where theoretical and practical approaches aligned strongly, or understand the reasons why they might diverge. This limited the ability to make truly informed decisions that considered all available information.
The time required to use both systems effectively also became a barrier to adoption. In fast-paced trade environments, users often defaulted to using only one system rather than investing the time required to properly utilize both sources of information, thereby losing the potential benefits of using both.
The Solution
The LLM Results Analyzer was developed as an orchestration framework that automatically combines the strengths of both the Custom Clarification and Custom Optimization projects into a single, unified user experience. The solution centers around an endpoint that coordinates parallel processing across both systems and provides synthesized recommendations through AI analysis.
When users submit a classification request to the unified endpoint, the system immediately dispatches identical queries to both the Custom Clarification and Custom Optimization endpoints simultaneously. This parallel processing approach ensures optimal efficiency by eliminating sequential wait times while gathering information from both theoretical and practical perspectives.
The system waits for both endpoints to complete their analysis before proceeding to the synthesis phase. This ensures that the final recommendations are based on complete information from both sources rather than partial or preliminary results. The parallel architecture is designed to handle varying response times between the two systems without creating bottlenecks or delays.
Once both systems return their results, a specialized analyzer LLM processes the combined outputs using prompt engineering designed specifically for HS code classification synthesis. This analyzer LLM understands the nuances of both regulatory compliance and practical trade classification, enabling it to identify areas of agreement, explain discrepancies, and provide reasoned recommendations that consider both perspectives.
The analyzer LLM provides structured output that includes the most suitable HS codes along with justification that explains how both theoretical definitions and practical precedent support the recommendations. Users receive clear explanations of when both systems agree, why they might disagree, and which factors should be prioritized in different classification scenarios.
The solution maintains full transparency by preserving the original outputs from both underlying systems while adding the synthesized analysis layer. Users can review the individual system recommendations if needed while benefiting from the integration that the analyzer LLM provides.
Results
The LLM Results Analyzer delivered improvements in both user experience and classification accuracy by successfully integrating the strengths of both theoretical and practical HS code classification approaches.
The unified endpoint eliminated the complexity and time overhead associated with manually using two separate systems. Users now receive classification recommendations that combine regulatory compliance with practical trade intelligence in a single response.
The parallel processing architecture ensures that total response time is only marginally longer than the slower of the two underlying systems, rather than the sum of both response times that manual sequential processing would require.
The analyzer LLM successfully identifies and explains discrepancies between theoretical and practical approaches, providing users with clear guidance on how to resolve classification conflicts. This synthesis eliminates the confusion and decision paralysis that previously occurred when the two systems provided different recommendations.
Classification confidence improved as users now have access to justifications that consider both legal compliance requirements and real-world trade precedent. The explanations help users understand not just what code to use, but why it's appropriate from multiple perspectives, increasing trust in the classification decisions.
The unified approach reduced the expertise barrier for effective system utilization. Users no longer need to manually interpret and reconcile outputs from different systems, making the combined power of both projects accessible to a broader range of stakeholders with varying levels of HS code classification expertise.
Adoption rates increased substantially as the simplified workflow eliminated the time and complexity barriers that previously prevented users from fully leveraging both systems. The single endpoint approach made it practical for users to access comprehensive analysis for every classification request rather than selectively using both systems only for the most critical decisions.
Conclusion
The LLM Results Analyzer successfully solves the integration challenge that limited the combined potential of the Custom Clarification and Custom Optimization projects. By creating an orchestration framework that automatically synthesizes theoretical and practical HS code classification approaches, the solution delivers comprehensive analysis while maintaining user-friendly simplicity.
The parallel processing architecture and specialized analyzer LLM demonstrate how AI can be used not just to automate individual tasks, but to combine multiple knowledge sources for better decision-making support. This approach creates better results by leveraging the complementary strengths of both regulatory knowledge and practical trade intelligence.
The success of this integration framework establishes a model for combining different types of classification systems in complex regulatory environments. The methodology developed here—parallel processing, synthesis, and transparent justification—provides a template for similar integration challenges across other domains where multiple knowledge sources must be reconciled for optimal decision-making.
For users, the LLM Results Analyzer transforms HS code classification from a choice between theoretical accuracy or practical relevance into a service that provides both simultaneously. This unified approach improves classification confidence, reduces decision-making time, and ensures that recommendations consider all available information systematically.
The analyzer framework also creates a foundation for continuous improvement as both underlying systems evolve. The integration layer can adapt to enhancements in either the Custom Clarification or Custom Optimization projects while maintaining the unified user experience, ensuring that improvements in individual components automatically benefit the integrated solution.
This project demonstrates the potential of system integration in complex classification environments. By solving the challenge of combining theoretical knowledge with practical experience, the LLM Results Analyzer enables organizations to make more informed, confident, and accurate HS code classification decisions that satisfy both compliance requirements and operational efficiency needs.