hs code classification from product description
Analyzes product descriptions and attributes to automatically suggest appropriate Harmonized System (HS) codes for customs classification. Uses machine learning to match products against the HS tariff database and identify the most relevant classification codes.
expert human validation of hs classifications
Routes AI-generated HS code suggestions to qualified customs experts for review and approval before use. Ensures classifications meet regulatory standards and reduces risk of customs penalties or delays due to misclassification.
batch hs code classification processing
Processes multiple product SKUs simultaneously to generate HS code classifications in bulk, rather than one-at-a-time lookup. Enables rapid classification of large product catalogs or shipment manifests.
hs code confidence scoring and flagging
Assigns confidence scores to each AI-generated HS code classification and flags uncertain or ambiguous classifications for additional review. Helps prioritize which items need expert attention versus which can be processed automatically.
hs code history and audit trail tracking
Maintains a complete record of all HS code classifications, including original AI suggestion, expert modifications, approval status, and timestamp. Creates an auditable history for compliance documentation and dispute resolution.
tariff and duty rate lookup
Provides current tariff rates, duty percentages, and trade agreement benefits associated with each HS code classification. Helps businesses understand the financial impact of their product classifications.
product attribute extraction and standardization
Extracts relevant product attributes (material, weight, dimensions, function, etc.) from unstructured product descriptions and standardizes them for HS code classification. Ensures consistent data quality for accurate classification.
hs code recommendation based on similar products
Suggests HS codes for new products by finding similar previously-classified products in the system and recommending their codes. Leverages historical classification data to improve accuracy and consistency.