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 john@acme.com")

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:

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).

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