Getting jan/san & packaging products cited by ChatGPT, Perplexity, and AI Overviews

A facilities manager restocking a multi-site cleaning program doesn't start with a distributor's website search bar anymore. She opens ChatGPT or Perplexity, describes the spec — dilution ratio, certification, case pack — and asks for a match. If your Jan/San or packaging catalog can't answer that question in structured form, the engine routes around you to a supplier whose data made the decision easy. That shift is showing up across B2B purchasing broadly, and it exposes a specific weakness in how most Jan/San distributors have exported product data for the last twenty years.

The research step moved off your website and into a chat window

This is not a niche trend. Forrester's 2026 Buyers' Journey Survey of 18,000 global business buyers found that 94% of B2B buyers used AI somewhere in their most recent purchase process, up from 89% a year earlier, and that generative AI now edges out vendor websites, product experts, and sales reps as the single most influential research source (Machine Relations, B2B AI Vendor Research 2026). The same research puts 55% of buyers comparing vendors inside AI tools and 54% researching product information directly through them, often before a distributor's sales team knows the deal exists (73% of B2B Buyers Use AI Tools in Purchase Research).

For Jan/San and packaging distributors, the facilities manager, BSC owner, or procurement generalist restocking chemicals, dispensers, liners, and case-pack supplies is increasingly running that restock as a prompt, not a site search. Jan/San buying is often done by someone juggling ten other responsibilities — exactly the profile most likely to lean on an answer engine to translate a facility need ("green-certified, low-foam floor cleaner safe for LVT flooring") into a specific SKU. If your catalog can't supply that translation in a form the model can parse, you get skipped.

Why a full warehouse looks empty to a language model

Most Jan/San ERP exports and legacy catalog feeds were built to print a price sheet, not to answer a question. A typical row looks like this:

Raw ERP feed description (as-is):

CHEM MULTI-SURF CLNR RTU 4/1GAL GRN SEAL

That string tells a counter clerk who already knows the shorthand what's in the case. It tells a model — or a buyer typing into Perplexity — almost nothing verifiable. Is it ready-to-use or a concentrate that needs dilution? What does "GRN SEAL" actually certify, and is that certification current? What surfaces is it rated safe for? None of that is answerable from the string itself.

Enriched, the same SKU looks like this:

That's the gap between a string an ERP can print on an invoice and a labeled attribute set an answer engine can reason over. A model matching "Green Seal certified, low-VOC, safe-for-LVT daily cleaner, ready-to-use, sold in case packs" against your catalog needs those values sitting in fields it can parse — not compressed into a ten-character abbreviation string.

Ask an answer engine: what this looks like today

Here's a plausible query from a facilities buyer managing several commercial sites:

"I need a Green Seal certified, ready-to-use all-purpose cleaner safe for LVT and laminate flooring, sold by the case, for a school district contract. Which distributors carry it and can fulfill a standing order?"

An answer engine parsing that is matching on certification, formulation type, surface compatibility, pack configuration, and fulfillment capability. If your product content encodes those as explicit, labeled attributes — on the page, in schema.org Product and additionalProperty markup, in a feed a retrieval layer can actually ingest — you're eligible to be the cited answer. Independent benchmarking backs this up directionally: pages with valid structured product markup have been observed appearing roughly 20-30% more often in AI-generated summaries than unstructured equivalents, though the mechanism is indirect — structured data helps a model confirm meaning that already exists in the content, it doesn't invent it (Schema Markup for AI Visibility, ailabsaudit.com). Packaging categories carry the same problem in a different shape — a corrugated case or stretch film SKU is only matchable if burst strength, gauge, and dimensions are explicit fields, not folded into a size code only your ERP understands.

What "machine-readable" actually requires

This isn't a rebuild. It's the enrichment work most Jan/San and packaging distributors already know is behind, with a sharper reason it now matters:

• Break compound spec strings into discrete labeled fields: formulation type, certification, dilution ratio, surface compatibility, case pack.
• Standardize the abbreviations that vary supplier to supplier — RTU vs. concentrate, Green Seal vs. EcoLogo, fragrance-free vs. unscented — so a query and a catalog value can actually match.
• Fill the fields supplier feeds routinely leave blank. Certifications and surface compatibility are two of the most commonly missing attributes in raw Jan/San distributor data, and they're exactly what an answer engine needs to confirm a fit.
• Keep it current as manufacturers reformulate or recertify products, so an answer engine isn't citing a SKU on a lapsed certification.

Done by hand — checking supplier documentation, normalizing terminology, filling gaps, re-verifying against updated spec sheets — that enrichment work runs in the range of 30-45 minutes per SKU. Across a Jan/San or packaging catalog with tens of thousands of active SKUs spanning dozens of manufacturers, that's not work a data team clears before the next supplier update makes the catalog stale again.

Where this fits for distributors

Your PIM or ERP stays the system of record — Anglera doesn't replace it and has nothing to do with your CRM. What Anglera does is plug into whatever you already run (Akeneo, Salsify, inriver, Stibo, Syndigo, Pimcore, Informatica, or nothing — a flat file is enough to start) and continuously extract, quality-score, and gap-fill the attributes that turn a compressed spec string into something a buyer's answer engine can actually match. It's live in about 30 days, not a multi-year integration project. Distributors getting their Jan/San and packaging data to that standard aren't optimizing for a traffic metric — they're making sure their SKU is the one an answer engine can confidently recommend when the question that used to start with a phone call to the rep now starts with a prompt.