veil-phantom 1.0.0

Privacy-preserving PII redaction for AI pipelines. Real data never leaves your machine.

VeilPhantom

Privacy-preserving PII redaction for AI pipelines. Real data never leaves your machine.

from veil_phantom import VeilClient

veil = VeilClient()
result = veil.redact("John Smith sent $5M to [e-mail]")

result.sanitized   # "[PERSON_1] sent [AMOUNT_1] to [EMAIL_1]"
result.rehydrate(ai_response)  # restore originals in AI output

Install

pip install veil-phantom

What it detects

12 entity types with a 25-label BIO scheme:

Type	Examples
PERSON	Names (Western, African, Asian, South African)
ORG	Companies, institutions, financial firms
EMAIL	Standard and spoken format ("john at example dot com")
PHONE	International, SA, spoken digit sequences
MONEY	USD, ZAR, verbal ("twelve point five million dollars")
DATE	Formats, relative, spoken ordinals
ADDRESS	Street addresses, locations, URLs/domains
GOVID	SSN, SA ID, passport, driver's license
BANKACCT	Account numbers, IBAN
CARD	Credit/debit card numbers
IPADDR	IPv4 addresses
CASE	Legal case numbers

How it works

7-layer detection pipeline, each layer catching what others miss:

Input text
  → Layer 0: Shade V7 NER (PhoneticDeBERTa, 22M params, dual-pass inference)
  → Layer 1: Compound org gazetteers + financial institutions
  → Layer 1.5: Pre-regex critical patterns (IBAN, spoken email)
  → Layer 2: NLP entity detection with POS validation
  → Layer 3: Regex patterns (18 types) + URL/domain filtering
  → Layer 5: Contextual sensitivity (roles, situations, temporal)
  → Tokens: [PERSON_1], [ORG_1], [AMOUNT_1]
  → Send to LLM (PII never leaves your machine)
  → Rehydrate AI response with original values

Powered by Shade V7

Shade V7 (default): PhoneticDeBERTa with Double Metaphone embeddings, 22M params, 97.12% F1, <50ms inference. Dual-pass inference (phonetic + zero-phonetic) picks the best result. Segment rescue for long transcripts. Auto-downloaded from HuggingFace Hub on first use.

Shade V5 (fallback): DeBERTa-v3-xsmall, 22M params, 97.6% F1 in-distribution, 97.3% OOD. Used automatically if V7 model is not present.

Full pipeline (all 7 layers): 100% detection on 24 real meetings (399 entities, 0 leaked).

Model	Parameters	F1 Score	Notes
Shade V7	22M	97.12%	Default, on-device, <50ms, phonetic embeddings
Shade V5	22M	97.6% / 97.3% OOD	Fallback
GLiNER	209M	98.0%	Cloud API required
Kaggle 1st	1.5B	97.0%	Ensemble, slow

Configuration

from veil_phantom import VeilClient, VeilConfig

# All layers (default)
veil = VeilClient()

# Regex + contextual only (no model download needed)
veil = VeilClient(config=VeilConfig.regex_only())

# Maximum privacy (lower thresholds)
veil = VeilClient(config=VeilConfig.max_privacy())

# Custom whitelist
veil = VeilClient(config=VeilConfig(
    additional_whitelist={"MYCOMPANY", "MYBRAND"},
    additional_compound_orgs={"My Corp Ltd"},
))

LLM Integration

# Token-direct mode (recommended)
result = veil.redact(transcript)
ai_response = your_llm(result.sanitized)  # LLM sees [PERSON_1], [ORG_1]
final = result.rehydrate(ai_response)     # restore original values

# OpenAI wrapper
from veil_phantom.integrations.openai import veil_chat
response = veil_chat(client, messages, veil=veil)

# LangChain
from veil_phantom.integrations.langchain import VeilRunnable
chain = VeilRunnable(veil) | your_chain

Training Data

Shade V7 was trained on 862,000 examples generated from 72 million words:

• Entity-swap augmentation: 8,606 base → 855,046 examples (100x expansion)
• Parakeet ASR corruption for speech-realistic noise
• Contrastive hard negatives for false positive reduction
• Double Metaphone phonetic embeddings for cross-cultural name robustness
• Trained on GTX 1050 Ti (accessible compute)

License

Apache 2.0