ReposiTrak has introduced its patent-pending system and methods for automated error detection and context-aware correction of food traceability data, addressing average data error rates of 40 percent in all traceability records.

The technology addresses a critical industry problem: traceability and transactional files that technically conform to standards but contain content errors such as missing or incorrect lot codes, inaccurate product identifiers, and inconsistent shipping details. These are issues that typically require costly and time-consuming manual correction.

ReposiTrak’s patent-pending system ingests heterogeneous data formats, including EDI, CSV, XLSX, XML, JSON, and API feeds, and normalizes them into a canonical data model. A hybrid engine combining deterministic expert rules and an artificial intelligence (AI)-driven inference identifies structural, semantic, and contextual anomalies. The system then generates, ranks, and applies candidate corrections using historical records and cross-document correlation, with confidence scoring to determine whether corrections are applied automatically or routed for human review. All actions are recorded with a complete audit trail to support regulatory compliance.

The invention supports supplier-specific behavioral models, configurable confidence thresholds, and continuous learning from human adjudication and explainable correction logic to provide reliable, touchless traceability—with "dirty data in" and "clean data out."

ReposiTrak: www.repositrak.com