The Global PII Anonymization API

A self-hosted API for detecting and redacting sensitive data in text and LLM chats. Supports 14 languages across 13 countries.

Raw Input

Safe Output

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Per-hour pricing on AWS and Azure Marketplaces. No sales calls and no lock-in.

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Designed for Regulated Sectors

Built for GDPR compliance across regulated industries such as banking, legal, healthcare and finance.

100% Private & Air-gapped

Runs entirely within your infrastructure (VPC/On-prem) in a single container. No external connections required.

Powerful Models

Built with the latest transformer technology, ensuring accurate detection on real world data that might contain RAG ingestion artifacts and internet slang.

150+ Localized Entity Types

Languages don’t follow borders. PII Eraser supports dozens of country-specific entity types such as Australian TFNs and Austrian Firmenbuchnummers (FN).

OpenAI Chat Support

Easily create guardrails with native support for OpenAI-format chats. Benefit from improved accuracy and speed via intelligent context handling.

1M+ Token Context

Process entire documents without manual chunking, with linear compute scaling and no accuracy degradation.

Realtime Power

>5000 tokens/sec on a single low cost instance, with optimizations for the latest ARM and x86 CPUs like AWS Graviton 4.

Interactive Demo

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Minimal Code. Maximum Protection.

We handle the complexity, from mixed-language inputs to large document chunking and cleaning.

Python Input

import requests

# Detect sensitive data instances
response = requests.post(
    "<base url>/text/detect",
    json={
        "text": ["Hallo Matthias"],
    }
)

import requests

# Replace sensitive data with entity types
response = requests.post(
    "<base url>/text/transform",
    json={
        "text": ["Contact marcus@pii-eraser.com"],
        "operator": "redact", 
    }
)

import requests

# Mask characters with asterisks
response = requests.post(
    "<base url>/text/transform",
    json={
        "text": ["My ID is 452-992-112"],
        "operator": "mask", 
    }
)

import requests

# Cryptographic hashing for analytics
response = requests.post(
    "<base url>/text/transform",
    json={
        "text": ["User: John Smith"],
        "operator": "hash", 
    }
)

Output JSON

response.json()

{
  "entities": [
    [
      {
        "entity_type": "NAME",
        "start": 6,
        "end": 14,
        "score": 0.995
      }
    ]
  ],
  "stats": { "total_tokens": 4, "tps": 5420 }
}

{
  "text": [
    "Contact [EMAIL_ADDRESS]"
  ],
  "entities": [
    [
      {
        "entity_type": "EMAIL_ADDRESS",
        "output_start": 8,
        "output_end": 23
      }
    ]
  ],
  "stats": { "total_tokens": 8, "tps": 5420 }
}

{
  "text": [
    "My ID is ***-***-112"
  ],
  "entities": [
    [
      {
        "entity_type": "US_SSN",
        "output_start": 9,
        "output_end": 20
      }
    ]
  ],
  "stats": { "total_tokens": 10, "tps": 5100 }
}

{
  "text": [
    "User: <PERSON_8f4a2c>"
  ],
  "entities": [
    [
      {
        "entity_type": "PERSON",
        "output_start": 6,
        "output_end": 21
      }
    ]
  ],
  "stats": { "total_tokens": 5, "tps": 4950 }
}

For the full range of options, including how to configure entity types via YAML, please visit the documentation

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