Pourquoi Regex, pas IA ?
Pour la conformité réglementaire, vous avez besoin de résultats que vous pouvez expliquer et reproduire. Notre approche déterministe fournit exactement cela—pas de boîtes noires, pas de surprises.
Comparaison détaillée
We use the best tool for each job: deterministic regex patterns for structured data, and proven ML models for names and entities. Built on Microsoft Presidio.
| Entity Type | Detection Method | Examples |
|---|---|---|
| Données Structurées | Modèles Regex | Emails, numéros de sécurité sociale, cartes de crédit, IBAN, numéros de téléphone |
| Noms & Organisations | Modèles ML (spaCy, Stanza) | Noms de personnes, noms d'entreprises, lieux |
| 48 Langues | XLM-RoBERTa | Reconnaissance d'entités multilingues |
| Reproductibilité | 100% Reproductible | Même entrée = même sortie, à chaque fois |
| Détection de Noms | Haute Précision ML | Modèles NLP éprouvés avec des scores de confiance |
| Auditabilité | +Entièrement Auditable | Position, type, confiance pour chaque entité |
Comment fonctionne l'appariement de motifs
Chaque type d'entité a des motifs regex soigneusement élaborés qui correspondent à des formats spécifiques.
Adresses e-mail
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}Correspond au format standard des e-mails : local-part@domain.tld
Numéros de carte de crédit
\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|...)\bCorrespond aux formats Visa, Mastercard, Amex et autres cartes avec validation de Luhn
IBAN Allemand
DE[0-9]{2}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{2}Correspond au format IBAN allemand avec des espaces optionnels
Conçu pour la conformité
Lorsque les auditeurs demandent "pourquoi cela a-t-il été détecté ?" vous avez besoin d'une réponse claire. Notre approche basée sur regex fournit exactement cela.
- Article 25 du GDPR : Confidentialité par conception avec un traitement explicable
- ISO 27001 : Processus documentés et répétables
- Trace d'audit : Chaque détection peut être retracée à un motif spécifique
Exemple de réponse d'audit
Découvrez la détection déterministe
Essayez notre détection PII basée sur regex gratuitement avec 200 jetons par cycle.
About this page
We update this page when our platform or the law changes.
Read our founder note for how we work.
Each change shows up in the timestamp at the top.
Related reading
- Common questions
- Glossary
- How tokens work
- Security posture
- Where we comply
- What we detect
- Case studies
- Release notes
We follow these rules
- GDPR (EU 2016/679).
- ISO/IEC 27001:2022.
- NIS2 (EU 2022/2555).
- HIPAA safe harbor under 45 CFR § 164.514(b)(2).
Our promise
We do not sell your data.
We do not train models on your text.
We store your files in Germany.
You can delete your account at any time.
You own your work.
Where we run
Our servers live in Falkenstein, Germany.
We use Hetzner. They hold ISO 27001 certification.
All data stays in the EU.
Backups run every day.
Need help?
Email support@anonym.legal.
We reply within one business day.
How we test
We run a full check suite on every release.
Each surface gets its own sweep script and report.
Human reviewers spot-check the output each week.
We track recall and precision on a labelled set.
Bad runs block the deploy.
What we never do
- We never sell your information to third parties.
- We never train models on what you upload.
- We never keep your work after you delete it.
- We never share keys with any outside firm.
- We never run ads inside the product.
Plans in plain words
We sell credits, not seats.
One credit covers one short job.
Long jobs use a few credits each.
You can top up at any time.
Unused credits roll over each month.
Read the plans page for current rates.
Who built this
A small team of engineers and lawyers built this.
We ship from Europe and work in the open.
Our founder note spells out why we started.
Where to start
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
How the parts fit
A browser add-on cleans text inside Chrome.
A Word plug-in handles drafts in Office.
A small desktop tool works on whole folders.
An agent protocol link feeds large models safely.
All four share one core engine and one rule set.
Words from our team
We started this work after a lunch about cookies.
One friend kept getting odd ads on her phone.
We asked why a court file leaked through a draft.
We sketched the first build on a napkin that week.
By month three we had a tiny demo for a friend.
She used it on her first case the next day.
Common questions we hear
Can the tool read scanned PDFs? Yes, with OCR.
Does it work on long files? Yes, in small chunks.
Can I roll my own rule set? Yes, save it as a preset.
Does it run offline? The desktop build runs offline.
Do you keep my files? No, the cloud build wipes after each run.
Will it learn from my work? No, we never train on inputs.
A short tour of the workflow
Upload a file or paste a snippet of prose.
Pick the entities you want gone from the draft.
Choose a method: replace, mask, hash, encrypt, or redact.
Press run and watch the side panel show each hit.
Skim the result and tweak any rule that misfired.
Save the cleaned file or send it to a teammate.