AI transparency notice

ECGT Ready uses an artificial intelligence component to classify environmental claims. This page is the transparency notice required by Article 50 of the EU AI Act (Regulation (EU) 2024/1689), whose transparency obligations apply from August 2026.

1. AI system in scope

• Component: claim detection, classification, and rewrite suggestion in the scan engine; per-page diagnosis and drafted fix paragraphs across privacy policy, cookies, terms, returns, contact, and product claim pages.
• Provider of the underlying model: Anthropic, PBC. The scan pipeline uses two model tiers from the Claude family: a diagnose tier and a verifier tier (see model naming below).
• Deployer: ECGT Ready, operated by Mathys Seynaeve from Mamer, Luxembourg.
• Risk classification: limited risk under the AI Act. The system is not a high-risk AI system within the meaning of Annex III. It is not used for credit scoring, employment, education, law enforcement, or biometric identification.

1a. Model naming convention

For clarity in pricing, marketing, and dashboard copy we refer to the two model tiers by neutral labels. The mapping below is the authoritative reference and is provided here for transparency.

• Basic: the Claude Sonnet family. Runs the diagnose pass that detects environmental claims, scores severity, and drafts the suggested rewrite. Used on every plan.
• Max: the Claude Opus family. Runs the verifier pass that re reads each finding against the source page and attaches the citation footnote. Used on Advanced, Pro, Agency, PAYG Pro scans, and Deep Scan.

These labels are commercial names. They do not change the underlying model provider, training, data handling, or any other obligation described in this notice. If the underlying model changes we will update this page.

2. Purpose

Detect environmental claims on a webpage, classify them against the rule set described in our methodology, and propose lower-risk rewrites. The output is an indicative risk score and a list of flagged sentences.

3. What this AI is not

• It is not an automated legal decision under GDPR Article 22.
• It does not block your store, contact regulators, or fine you.
• It does not produce a binding compliance certificate.
• It does not generate deepfake media or impersonations.

4. Training and customer data

Anthropic does not train its production models on data submitted through the API. We do not run a separate training pipeline that feeds on customer data. If we ever introduce a feedback loop that adapts model behaviour, we will publish that fact here, ask for explicit consent, and offer an opt-out.

5. Human oversight

The dashboard surfaces every flag with a Disagree button. We review contested flags weekly. For high-stakes claims (future commitments, named certifications) we recommend a human review by qualified counsel before publishing. The disclaimer is shown next to every report.

6. Limits and risks

• The model can hallucinate. Flags should be checked against the source page.
• The model is trained on text up to a fixed cutoff. Recent regulatory guidance may not be reflected immediately.
• Edge cases (mixed-language pages, image-only claims) can be misclassified.

7. Data minimisation

We send the model only the page text and minimal metadata required to classify (URL, locale, category). We do not send account identifiers or billing data.

8. Evaluation

We evaluate the engine quarterly against a held-out set of pages labelled by hand. Precision, recall, and false-positive rate per category are reviewed and used to adjust the rule set.

9. Contact

Questions, feedback, or to challenge an output: contact@ecgtready.eu. For privacy-specific concerns: contact@ecgtready.eu.