# How the Feedmind Score is calculated (FM-score methodology v1.0)

Shopping is moving into assistants, and assistants read product content differently than people do. The Feedmind Score measures one thing: how well a catalog can be read, understood and answered from by an AI agent. We publish the method in full, because a score nobody can audit is a score nobody should trust.

## Four weighted dimensions
- **Agent accessibility — 25%** Can an assistant reach and read the page at all? We check crawler permissions, whether content survives without JavaScript, and whether the page states what it is before a human ever scrolls.
- **Structured data — 25%** Machine-readable facts beat prose. Product and Offer markup with real identifiers is what lets an assistant match your item to the one a shopper is asking about, and quote a price it trusts.
- **Content answerability — 30%** The heaviest weight, because it decides recommendations. We measure whether your copy contains the specifics buyers ask about — fit, compatibility, materials, what is in the box — and whether those facts survive summarization.
- **Multi-language parity — 20%** European catalogs answer in several languages. A thin German page is a German shopper you lose silently, so we compare depth, attributes and markup across every locale you publish.

## Two measurement layers
- **Rule engine** — ~15 deterministic checks across a representative sample. Instant, explainable, free.
- **AI simulation** — real buyer questions answered only from what an assistant can read on the page.

## Grade scale
- **A** (85 — 100) — Agent-ready. Assistants can answer almost anything about the catalog.
- **B** (65 — 84) — Solid. Gaps appear on comparison and compatibility questions.
- **C** (50 — 64) — Readable but thin. Frequently skipped for better-documented rivals.
- **D** (35 — 49) — Structural problems. Most buyer questions go unanswered.
- **F** (below 35) — Effectively invisible to assistants.
Each band splits in thirds for +/−. F is terminal.

## Benchmarks
A category median is the anonymized aggregate of every store scanned in that category, published only once a category has at least 20 scanned stores. The named competitor is a public scan of a public storefront, run with exactly the same rules.

## What the score is not
- **It is not a guarantee of placement.** No one outside the model providers can promise that a change makes an assistant recommend you, and anyone who does is selling something.
- **It is not attribution.** We do not measure traffic or revenue from assistants; we measure whether your content can be used at all. Readability is a precondition, not a conversion metric.
- **It is not a ranking.** Two stores with the same score are equally readable; which one gets named depends on price, availability, reviews and the shopper’s question.