The safety & ppe attributes buyers filter on — and most catalogs miss

Safety & PPE buyers rarely browse. A safety manager specifying gloves for a glass-handling line already knows the spec before opening a catalog. They filter for it, or ask an AI assistant for it. If the attribute isn't in a structured field, the product doesn't exist to them - even if it's the right glove sitting three rows down in the raw feed description.

Why PPE is an attribute-first category

PPE selection is regulated and litigated. OSHA compliance and ANSI/ISEA ratings mean a safety buyer isn't shopping on vibes - they're matching a hazard profile to a certified spec. A cut level A2 glove rated for light cardboard is a liability on a line cutting sheet metal. That makes the attribute the product, in a way that's less true for apparel or office supplies.

The PPE distribution market is also growing fast, with e-commerce increasingly the primary procurement channel rather than counter conversations with a rep who can translate a vague ask into the right SKU, per market research on PPE distribution. More of this buying is happening through filters and search than ever.

The attributes that actually drive filtered search

Here's the core schema for hand protection, the largest PPE category by revenue share and the one with the most standardized, and most often missing, rating data:

Every category downstream of gloves follows the same pattern. Hi-vis apparel needs ANSI/ISEA 107 class (1, 2, or 3) and reflective material type. Respirators need NIOSH approval number and assigned protection factor. Fall protection needs ANSI Z359 category and maximum arrest force. Eye protection needs impact rating (Z87+) and lens tint. A certification code plus a handful of physical attributes determines whether a SKU is even legal to recommend for a given hazard.

Where the data actually breaks

Most PPE catalogs carry this information - just not in a field a filter can read. It's buried in a spec sheet PDF, folded into a product title as shorthand, or dropped when a supplier flat file gets mapped into a PIM with a generic template built for a different category.

The failure mode is consistent. Faceted search on a distributor site can only offer a "Cut Level" filter if every SKU in that category has a value in that field. One gap and the buyer either sees an incomplete result set or sees a competitor's product instead, because theirs was mapped correctly. Industrial buyers already say they'd switch suppliers over search that can't get them to the right part fast, a frustration Hum Commerce documents in its industrial supply research.

AI answer engines compound the problem rather than solve it. Ask one "what glove should I use for handling sheet metal with moderate cut risk" and it has to reason over cut level, coating, and dexterity at once - fields it can only surface if they're clean, extractable attributes rather than prose buried in a PDF. A page that says "durable industrial-grade protection" gives the model nothing to match against. A page with ANSI Cut Level: A4, Coating: Sandy nitrile palm, Touchscreen compatible: No gives it exactly what it needs to recommend your SKU by name.

Before and after: a cut-resistant glove

Here's a raw supplier feed description, the kind that shows up untouched in a lot of PIMs:

"13g nylon/HPPE blend shell blk sandy nitrile palm coat cut lvl 4 touch compat sz S-2XL good for glass/metal handling"

Everything a buyer needs is technically in there. None of it is filterable. Here's the same SKU enriched into structured attributes:

Now the SKU shows up when a buyer filters by "A4" and "touchscreen compatible," and when someone asks an answer engine for a touchscreen-friendly A4 glove for glass handling. Same product, same feed - the only difference is whether the data was pulled out and scored into fields.

Structuring it so it holds up

Three things make a PPE attribute schema durable rather than a one-time cleanup:

• Pull values from the source, not the copywriter. Cut level, EN 388 code, and NIOSH numbers should come from the supplier's actual spec sheet or safety data sheet, not marketing text. Anything unverifiable gets flagged, not guessed.
• Score completeness by category, not by SKU count. A catalog is only as good as its worst-covered segment. Track what percentage of gloves have a populated cut level field, not just how many gloves exist.
• Keep certification fields as controlled values, not free text. "Cut Level A4," "cut level 4," and "A-4" are the same fact written three ways - a filter or an AI model treats them as three different things unless they're normalized.

This is the layer Anglera works on. Your PIM - Akeneo, Salsify, inriver, or a flat file with no PIM at all - stores the catalog. Anglera reads supplier documentation, extracts and quality-scores attributes like cut level, coating, and certification codes, and gap-fills what's missing, so a SKU that's invisible in a filter today shows up correctly tomorrow. It's additive to whatever you already run, and most catalogs are live with real enrichment inside 30 days.

Sources:

• Ergodyne: ANSI/ISEA 105 & EN 388 cut-resistant glove standards
• The ANSI Blog: ANSI/ISEA 105-2024 hand protection classification
• Hum Commerce: B2B ecommerce for industrial supply