Sincera receives millions of unstructured records monthly describing products and services from diverse internet sources.
The records lack consistency, some represent customer segments like 'Lipton purchaser' while others describe products like 'pork tenderloin', and needed to be standardized into a usable format.
Fractional AI built a five-agent LLM pipeline that categorizes Sincera's unstructured product and audience records against Shopify's 10,000-category taxonomy, which runs up to seven levels deep. Sequential agents classify each record, enrich it with fuller descriptions, select the best candidate category, and judge the decision with a confidence score before returning the final categorization.
Categorization accuracy consistently above 85%.
Processes one to two million unstructured records per month.
Records are categorized in real time against a 10,000-category taxonomy.
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