AI Product Classification: Workflow & Evaluation

Cohentrix product team · Reviewed 29 October 2026 · Independent examples

What information should a classifier retain?

Keep the original label, market, source identifier and unit alongside the proposed category, subcategory, product and variant. A normalized label should not erase information needed to check the decision.

Rules, AI and human review.

Rules work well for explicit constraints and known exceptions. AI can propose mappings for variable wording. Reviewers resolve business meaning, missing context and disagreements. These roles should be evaluated separately rather than treating a plausible response as a verified mapping.

A practical workflow.

1. Define the target hierarchy and required fields.
2. Select a representative, independently labelled evaluation sample.
3. Generate suggestions while preserving source context.
4. Route ambiguous and incomplete records to reviewers.
5. Record corrections and assess errors before expanding.

How should quality be measured?

Report coverage, field completeness, exact agreement at each taxonomy level, reviewer correction rate and time per reviewed record. Break results down by category, market and language. Keep a held-out sample separate from examples used to improve prompts or rules.

What does Cohentrix provide?

Cohentrix focuses on taxonomy mapping, human review and decision history. A pilot establishes the source preparation, access model and quality criteria for a specific deployment. This website shows synthetic examples, not model accuracy results.

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Product taxonomy fundamentals · Explore Cohentrix · All resources