---
title: promptshield
sdk: static
pinned: false
license: mit
short_description: Document anonymization that runs on your device
---

# promptShield

Document anonymization that never uploads your documents.

We build an offline PII detection and redaction tool for contracts, financial
statements and medical records. The desktop app runs a local engine; the web app
runs the **entire pipeline in the browser** — PDF parsing, OCR, entity detection
and redaction — so a document never leaves the device it was opened on.

That constraint is why we publish here. Running detection client-side makes model
size a product decision: every megabyte is a first-visit download, paid by the
user before they have seen anything work.

## Models

- **[bert-base-multilingual-cased-ner-hrl-latin](https://huggingface.co/promptshield/bert-base-multilingual-cased-ner-hrl-latin)**
  — Davlan's multilingual NER model with the vocabulary a Latin-script
  deployment can never tokenize removed. **178.5 MB → 139.6 MB with provably
  identical output**: 0 entity differences across 2,219 entities, max logit
  delta 0.0000000000. Not a distillation — no encoder weight is touched.

## The idea, if you only read one thing

In `bert-base-multilingual-cased`, the 119,547-token embedding table is **~92 MB
of the 178 MB file** — more than half the model. Most deployments ship a handful
of the 104 languages it covers and download the rest anyway. Dropping the
unreachable rows is exact, not approximate: the embedding is per-tensor
quantized and read by a `Gather`, so surviving rows keep their values *and* their
dequantization.

Verified by comparing raw logits, not entity lists — those can agree by luck.

🔗 [promptshield.ca](https://promptshield.ca)