Kodėl Regex, o ne AI?
Reguliavimo atitikties tikslais jums reikia rezultatų, kuriuos galite paaiškinti ir atkartoti. Mūsų deterministinis požiūris suteikia būtent tai—jokios juodosios dėžės, jokių staigmenų.
Išsami palyginimas
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 |
|---|---|---|
| Struktūrizuoti Duomenys | Regex Šablonai | El. paštai, SSN, kreditinės kortelės, IBAN, telefono numeriai |
| Vardai ir Organizacijos | ML Modeliai (spaCy, Stanza) | Asmenų vardai, įmonių pavadinimai, vietovės |
| 48 Kalbos | XLM-RoBERTa | Kryžminis kalbų subjektų atpažinimas |
| Atkartojamumas | 100% Atkartojama | Tas pats įvestis = tas pats išvestis, kiekvieną kartą |
| Vardų Aptikimas | Aukštas Tikslumas ML | Įrodyti NLP modeliai su pasitikėjimo balais |
| Audituojamumas | +Visiškai Audituojama | Pozicija, tipas, pasitikėjimas kiekvienam subjektui |
Kaip veikia modelių atitikimas
Kiekvienas subjektų tipas turi kruopščiai sukurtus regex modelius, kurie atitinka specifinius formatus.
El. pašto adresai
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}Atitinka standartinį el. pašto formatą: local-part@domain.tld
Kredito kortelių numeriai
\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|...)\bAtitinka Visa, Mastercard, Amex ir kitų kortelių formatus su Luhn validacija
Vokietijos 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}Atitinka Vokietijos IBAN formatą su neprivalomais tarpais
Sukurtas atitikties tikslais
Kai auditoriai klausia "kodėl tai buvo aptikta?" jums reikia aiškaus atsakymo. Mūsų regex pagrindu veikiantis požiūris suteikia būtent tai.
- GDPR 25 straipsnis: Privatumas pagal dizainą su paaiškinamu apdorojimu
- ISO 27001: Dokumentuoti, pakartojami procesai
- Audito takas: Kiekviena detekcija gali būti atsekama iki konkretaus modelio
Pavyzdys audito atsakymo
Patirkite deterministinę detekciją
Išbandykite mūsų regex pagrindu veikiančią PII detekciją nemokamai su 200 tokenų per ciklą.
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.