Expertise
Understanding product data
Matching, enrichment and distribution standards: pages that explain how these subjects really work, written by a team that practises them in production — useful even if you never work with us.
Product matching: exact and similar
Why matching two catalogues is difficult, the reliable signals (EAN/GTIN, reference + brand, visual verification), equivalence rules and common pitfalls.
Read →Product data enrichment with AI agents
Category-specific schemas, source hierarchy, closed-set document collection, source-based verification for every value, labelled inferences, and what to demand from an enrichment process.
Read →How to enrich an existing PIM with AI
The reality of legacy PIM systems: enrich upstream, then feed data back through files or an API without replacing the existing system. Native AI is one option, not a prerequisite.
Read →AI product categorisation
Classifying products accurately among thousands of categories: blind second opinions without anchoring bias, checks against existing products, confidence scores and variant-group classification.
Read →Reliable product data: lessons from production
Mechanically enforced source hierarchy, error propagation across variants, why certifications must never be inferred, how a quality checker can introduce errors, and why completeness is not quality.
Read →// industry glossary — all definitions
These topics are Opsylen's daily work — KaraK brings them to industrial scale.