为什么选择正则表达式，而不是AI？
为了满足监管合规性，您需要可以解释和重现的结果。我们的确定性方法正是提供这一点——没有黑箱，没有惊喜。
详细比较
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 |
|---|---|---|
| 结构化数据 | 正则表达式模式 | 电子邮件、社会安全号码、信用卡、国际银行账户号码、电话号码 |
| 姓名与组织 | 机器学习模型（spaCy, Stanza） | 个人姓名、公司名称、地点 |
| 48种语言 | XLM-RoBERTa | 跨语言实体识别 |
| 可重复性 | 100% 可重复 | 相同输入 = 每次相同输出 |
| 姓名检测 | 高准确率机器学习 | 经过验证的自然语言处理模型，具有置信分数 |
| 可审计性 | +完全可审计 | 每个实体的位置、类型、置信度 |
模式匹配的工作原理
每种实体类型都有精心设计的正则表达式模式，匹配特定格式。
电子邮件地址
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}匹配标准电子邮件格式：local-part@domain.tld
信用卡号码
\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|...)\b匹配Visa、万事达、美国运通及其他卡格式，并进行Luhn验证
德国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}匹配带可选空格的德国IBAN格式
为合规性而构建
当审计员问“为什么会检测到这个？”时，您需要一个明确的答案。我们的基于正则表达式的方法正好提供这一点。
- GDPR第25条：隐私设计，具有可解释的处理
- ISO 27001：文档化、可重复的流程
- 审计轨迹：每个检测都可以追溯到特定模式
示例审计响应
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.