How we measure
Technical AI-readability measurement — not a quality judgement
This page describes exactly what our AI-Readiness Score measures, how it is weighted, and what it explicitly is NOT. All numbers map one-to-one to the code (computeOverallScore8 in agenticCommerce.ts).
Important: This is a technical measurement of AI readability, NOT a judgement about the quality, trustworthiness or reputation of the shop.
Score version: v2-8pillar-2026-06
1. What we measure
We measure only the technical AI- and agent-readability of a publicly accessible webshop: can AI shopping agents (e.g. GPT, Claude, Perplexity, Gemini) find the shop, interpret it, and prepare transactions with it? The score says nothing about product quality, customer service, pricing or business operations.
2. How we measure — 8 pillars with fixed weights
The final score is a weighted average of 8 pillars. Weights are hard-coded and stored with every scan alongside the score version. In total we run ~30 deterministic checks (no LLM judgement for the deterministic pillars).
| Pillar | Weight | What |
|---|---|---|
| AI Visibility | 15% | Visibility inside AI answers.
|
| Agentic Commerce | 15% | Can an AI agent initiate transactions?
|
| Schema.org | 13% | Structured data per schema.org.
|
| Product Content | 13% | Quality of machine-readable product data.
|
| Technical | 12% | Technical baseline readability.
|
| Trust Signals | 12% | Publicly visible trust signals.
|
| Transaction Readiness | 10% | Can a transaction technically complete?
|
| Operational Maturity | 10% | Signals of operational maturity.
|
Weight defaults: if a deterministic pillar cannot be measured we apply a conservative default of 40 (see PILLAR_DEFAULTS). For aiVisibility we NEVER apply a default: if all three AI models fail, that pillar is removed from both numerator and denominator — no 'silent 50'.
3. What the score is NOT
This score is explicitly not:
- not a trust or safety certification
- not a consumer review or experience score
- not a legal or GDPR compliance judgement
- not a recommendation to buy or avoid buying from a shop
- not a guarantee of availability, quality or after-sales
4. Reproducible
Any shop can recompute its own score: the 30+ deterministic checks probe public endpoints and HTML signals (including /.well-known/mcp.json, /llms.txt, robots.txt, JSON-LD Offer). Every scan stores the weights, defaults, aiMeasured status and score version. On request we provide the full `pillarsUsed` / `defaultsUsed` object for a specific scan.
5. Limitations and known uncertainties
Deterministic checks vs. vendor-specific AI Visibility
Seven of the eight pillars consist of deterministic checks against public endpoints and HTML — vendor-independent and reproducible. Only the AI Visibility pillar (15%) is vendor-specific: it measures visibility with the current generation of models (GPT, Claude, Perplexity, Gemini). A high AI Visibility score therefore does not automatically guarantee visibility with future or other agents.
Rendering: agent-suitability ≠ readability without JavaScript
We fetch every shop both with a headless browser (JavaScript executed) and as raw HTML. The difference between the two is reported as an explicit, named finding — a JS-dependent shop is not silently penalised, it gets a visible observation that non-rendering crawlers see less.
Where the weights come from
The weights (15/15/13/13/12/12/10/10) are reasoned estimates based on failure impact: how badly does an agent interaction break when this signal is missing? They are NOT empirically validated against actual agent conversion rates. This is the same approach Google Lighthouse takes; we prefer to state it rather than let the score look more precise than it is.
Bonus categories do not add to the 100
Google Agent-Friendliness and Product Data Integrity are informational findings/badges. They are shown separately, do not add on top of the 100 points, and are not a multiplier. The final score is purely the weighted average of the eight pillars.
Measurement uncertainty and model drift
LLM judgements are sensitive to prompt variation and model updates. We therefore run a weekly test-retest check: the same shop is scanned twice on the same day and the score difference is logged and published as a measurement-uncertainty figure (± points). Score changes within that band should not be read as real site changes.
Selection bias in the discovery engine
Our discovery engine finds more discoverable shops first, so the Registry is not representative of the full EU e-commerce market. This is acknowledged and not yet mitigated. A stratified sample per country and sector is on the roadmap for Q4 2026.
Objection or correction
Are you the shop owner and does something look wrong? Request removal or re-assessment via /data-aanvraag or privacy@langhoormarchal.com.