Zergatik Regex, Ez AI?
Araudi betetzeko, azaldu eta erreproduzitu daitezkeen emaitzak behar dituzu. Gure hurbilketa deterministikoak zehazki hori ematen du—ez kutxa beltzik, ez sorpresa.
Konparaketa Detallatua
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 |
|---|---|---|
| Datu Estrukturatua | Regex Patroiak | Posta elektronikoak, SSN-ak, kreditu txartelak, IBAN-ak, telefono zenbakiak |
| Izena & Erakundeak | ML Modeloak (spaCy, Stanza) | Pertsona izenak, enpresa izenak, kokapenak |
| 48 Hizkuntza | XLM-RoBERTa | Hizkuntza anitzeko entitateen ezagutza |
| Erreproduzibiltasuna | 100% Errepikagarria | Sarrera bera = irteera bera, beti |
| Izena Detektatzea | Zehaztasun Handiko ML | Konfiantza puntuazioak dituzten NLP modelo frogatuak |
| Auditatzea | +Osorik Auditatua | Posizioa, mota, konfiantza entitate bakoitzeko |
Nola Funtzionatzen Duten Patroiak Matchatzea
Entitate mota bakoitzak formatu zehatzak matchatzen dituzten regex patroiak ditu.
Email Helbideak
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}Estandarreko email formatuarekin bat etortzen da: local-part@domain.tld
Kredituko Txartel Zenbakiak
\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|...)\bVisa, Mastercard, Amex, eta beste txartel formatu batzuk Luhn balidazioarekin bat etortzen da
Alemaniako IBAN
DE[0-9]{2}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{4}\s?[0-9]{2}Alemaniako IBAN formatuarekin bat etortzen da espazio optionalekin
Araudi Betetzeko Eraikia
Auditoriek "zergatik detektatu da hau?" galdetzen dutenean, erantzun argi bat behar duzu. Gure regex oinarritutako hurbilketa zehazki hori ematen du.
- GDPR 25. artikulua: Diseinuan pribatutasuna azalduz prozesatzea
- ISO 27001: Dokumentatutako, errepikakorrak diren prozesuak
- Audit Trail: Detekzio bakoitza patroi zehatz batera jarrai daiteke
Adibide Audit Erantzuna
Esperimentatu Detekzio Deterministikoa
Probatu gure regex oinarritutako PII detekzioa doan 200 token ziklo bakoitzeko.
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.