Miksi Regex, ei AI?
Sääntelyvaatimusten vuoksi tarvitset tuloksia, joita voit selittää ja toistaa. Meidän deterministinen lähestymistapamme tarjoaa juuri sitä—ei mustia laatikoita, ei yllätyksiä.
Yksityiskohtainen vertailu
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 |
|---|---|---|
| Rakenteellinen Data | Regex-mallit | Sähköpostit, henkilötunnukset, luottokortit, IBANit, puhelinnumerot |
| Nimet & Organisaatiot | ML-mallit (spaCy, Stanza) | Henkilöiden nimet, yritysten nimet, sijainnit |
| 48 Kieltä | XLM-RoBERTa | Ristiin kielten entiteettien tunnistus |
| Toistettavuus | 100% Toistettavissa | Sama syöte = sama tulos, joka kerta |
| Nimen Tunnistus | Korkea Tarkkuus ML | Todistetut NLP-mallit luottamusarvioilla |
| Auditointi | +Täysin Auditoitavissa | Sijainti, tyyppi, luottamus jokaiselle entiteetille |
Kuinka kaavojen vertailu toimii
Jokaisella entiteettityypillä on huolellisesti laaditut regex-kaavat, jotka vastaavat tiettyjä muotoja.
Sähköpostiosoitteet
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}Vastaavat standardisähköpostimuotoa: paikallinen-osa@domain.tld
Luottokorttinumerot
\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|...)\bVastaavat Visaa, Mastercardia, Amexia ja muita korttimuotoja Luhn-validoinnilla
Saksalainen 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}Vastaavat saksalaista IBAN-muotoa valinnaisilla välilyönneillä
Rakennettu vaatimustenmukaisuutta varten
Kun tarkastajat kysyvät "miksi tämä tunnistettiin?" tarvitset selkeän vastauksen. Meidän regex-pohjainen lähestymistapamme tarjoaa juuri sen.
- GDPR 25. artikla: Tietosuoja suunnittelussa selitettävällä käsittelyllä
- ISO 27001: Dokumentoidut, toistettavat prosessit
- Audit Trail: Jokainen tunnistus voidaan jäljittää tiettyyn kaavaan
Esimerkki tarkastusvastauksesta
Koe deterministinen tunnistus
Kokeile regex-pohjaista PII-tunnistustamme ilmaiseksi 200 tokenilla per sykli.
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.