B2B manufacturers and distributors carry some of the hardest catalog data in commerce. Product information originates as engineering specs, part numbers, PDFs, and legacy ERP or PLM fields — accurate for internal use, but rarely shaped for how buyers search or how downstream channels ingest data. The core work is turning that technical source material into complete, consistent, buyer-ready product content.
Why B2B catalogs are hard
Manufacturers tend to own deep but narrow data: precise attributes with little of the descriptive, benefit-oriented language buyers and search engines rely on. Distributors face the opposite problem — assembling a coherent catalog from many suppliers, each with its own formats, naming, and level of completeness. In both cases the raw data is technically correct but commercially unready, and the gap is usually closed manually, slowly, one product at a time.
Typical enrichment priorities
- Attribute discovery from manufacturer and supplier sources
- Normalization of units, naming, and compatibility across brands
- Category schema definition and required-field enforcement
- Deduplication and entity resolution across overlapping supplier feeds
- Descriptive, benefit-oriented content layered onto technical specs
Where AI discovery raises the stakes
As search engines, answer engines, and AI agents read product data directly, thin or inconsistent B2B catalogs simply don't surface. Structured, normalized attributes and clear descriptions are increasingly the difference between being found and being invisible — in Google, in marketplaces, and in AI-driven procurement.
Common pitfalls
- Over-reliance on PDFs and unstructured spec sheets
- Inconsistent attribute naming across brands and suppliers
- No canonical source-of-truth or precedence model
- Treating enrichment as a one-time cleanup rather than an ongoing process