So arbeiten wir

EuropeanStack is a public directory of European technology products. We want every listing to give a thorough, factual overview of capabilities so buyers can compare European alternatives with confidence.

Keeping that directory complete and accurate at scale is hard. We use AI heavily to research companies, cross-check public claims, and draft structured product pages — always with editorial judgment and room for human review.

We use AI to research European companies and validate their claims against publicly available sources: websites, documentation, legal pages, press materials, and other open references.

AI helps us draft capability summaries, decision-aid sections, feature lists, and FAQs. It also helps us spot gaps, contradictions, and outdated statements before a page is published or updated.

AI does not replace accountability. Editors and operators remain responsible for what appears on the site. When something is uncertain, we prefer to say so rather than invent certainty.

Factual capability overviews — not marketing copy rewritten from a vendor homepage.

Clear sourcing and evidence status where we evaluate claims (for example verified, vendor-claimed, or not found).

Ongoing correction: products change, and so should our pages.

If you spot a mistake on a product page, you can flag it from that product’s feedback history. Feedback is public: after you submit, the report appears on that product’s timeline with an “In review” status.

When we finish reviewing a report we publish a response on the same product page history. If we accept the claim, we update the listing and can record the revision that fixed it — so anyone can see feedback, responses, and page changes over time for that product.

Optional contact details you leave (such as an email) stay private for follow-up. The public product history shows the report text, status, our response, and listing updates — not your email. There is no site-wide public feedback index.

Directory pages are informational. Always verify critical details with the vendor before buying or migrating.

AI systems can miss nuance or lag behind product changes. Public feedback is one of the ways we catch and fix that.