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Digital Readiness Scorecard · 2026

EIS Inc.

60 / 100

2 points above the median of 58 across 32 measured distributors

Revenue
not disclosed
Archetype
Technical Specialist
Median attributes
18
Consistency spread
24 attrs
Pages sampled
5/5
What this is, precisely. A score computed from five product pages sampled from EIS Inc.’s public catalog on 2026-08-06, plus a probe of the site’s robots.txt, sitemap, and server-rendered markup. It is not an audit of the full catalog, and it says nothing about how the business is run — several of the strongest operators in distribution score modestly here. Data passed privately to customers over EDI, punchout, or a syndication feed is not visible to this measurement and does not count toward it.

The four pillars

Scored against the published framework — fourteen signals, no model judgement anywhere in the scoring.

Product Data Depth18.8 / 35

Can a machine tell what this product is and match it to the same product elsewhere?

Buyer Answerability10 / 25

Does the page answer what a buyer actually asks before they commit?

Commerce Transparency18.4 / 20

Can a buyer find out what it costs and whether it ships, without asking a human?

Machine & Agent Readiness13 / 20

Can a crawler, a marketplace, or an AI shopping agent actually consume any of it?

What the site probe found

Product structured data

No JSON-LD found in the HTML a crawler receives

Sitemap

Valid sitemap resolved, declared in robots.txt

Crawler access to products

Product paths are crawlable under the general user-agent rules

AI crawler stance

No AI-crawler rules in robots.txt, so they are permitted by default

The pages we sampled

Every one, openable. Each was taken from the middle of a different category listing — never a featured or promoted placement — so the sample reflects the catalog rather than its best shelf.

Page score is out of 69 — the portion of the framework scored per page. The remaining 31 points are site-level: consistency, gating, and the four agent-readiness signals.

What would move this number

Derived from the score itself, largest gap first — Product Data Depth lost the most points.

  1. 1A GTIN or UPC appeared on 20% of the sampled pages. Identifiers are what let a marketplace, a search engine, or a procurement system recognise your listing as the same item everyone else is selling.
  2. 2No schema.org Product markup was found in the server-rendered HTML. This is usually the cheapest fix on the list and it is the single clearest signal to a crawler or an AI agent that the page describes a product.
  3. 3The richest and thinnest pages sampled differed by 24 attributes. A spread that wide usually points at categories onboarded under different rules rather than at any one bad page.
  4. 4A price was published on 80% of sampled pages. Withholding price is a legitimate commercial strategy, and it is also a hard stop for any automated buyer comparing options.

Think this is wrong?

Good — that is the useful conversation. The pages we sampled are listed above, so the first thing to do is open them. If the sample missed something, if a category we hit is genuinely unrepresentative, or if your catalog has changed since 2026-08-06, tell us and we re-run the measurement against your live site. Scores are never adjusted by hand — only recomputed.