context-aware pii detection across 50+ entity types
Detects personally identifiable information, protected health information, payment card data, and confidential company information across 50+ entity types by analyzing semantic context rather than pattern matching alone. Unlike regex-based approaches, the system reads contextual relationships between tokens to distinguish legitimate uses of PII-like strings (e.g., 'John' as a common noun vs. a person's name) and handles real-world data quality issues including ASR errors, OCR mistakes, handwritten forms, and conversational disfluencies. Supports 52 languages including code-switching scenarios.
Unique: Uses contextual semantic analysis ('reads context' per product claims) rather than pattern matching to detect PII, enabling accurate identification even with ASR errors, OCR mistakes, and conversational disfluencies where regex-based tools fail. Handles code-switching and 52 languages natively.
vs alternatives: Achieves 99.5% accuracy on physician conversations (Providence Health case study) vs. AWS Comprehend, Microsoft Presidio, and Google DLP which reportedly drop to 60-70% accuracy on real-world noisy data.
multi-modality pii redaction with transformation strategies
Redacts, pseudonymizes, or synthetically replaces detected PII entities across text, documents, images, and audio using configurable transformation strategies. The system applies entity-specific redaction rules (e.g., masking credit card numbers with asterisks, replacing names with consistent pseudonyms, generating synthetic replacements) while preserving document structure and downstream usability. Supports batch processing across multiple file formats (PDF, DOCX, XLS, XLSX, PPTX, XML, JSON, CSV) and image formats (TIFF, PNG, JPEG with OCR-based redaction).
Unique: Applies context-aware redaction across multiple modalities (text, documents, images, audio) with entity linking to maintain consistency across related documents — e.g., the same person's name is replaced with the same pseudonym throughout a dataset. Handles structured formats (JSON, CSV, XML) with schema-aware redaction.
vs alternatives: Supports multi-format document redaction (PDF, DOCX, spreadsheets, presentations) in a single API call, whereas most PII tools require separate pipelines for text vs. documents vs. images.
multi-language pii detection with code-switching support
Detects PII across 52 languages including support for code-switching (mixing multiple languages within the same document or conversation). The system handles language-specific entity formats (e.g., different date formats, phone number patterns, address structures across countries) and recognizes PII in multilingual contexts without requiring explicit language specification. Supports real-world multilingual data including conversational transcripts with language mixing.
Unique: Supports PII detection across 52 languages including code-switching (language mixing) without requiring explicit language specification, handling language-specific entity formats and multilingual contexts natively.
vs alternatives: Enables code-switched and multilingual PII detection vs. language-specific tools (AWS Comprehend supports ~10 languages, Google DLP is English-focused) which require separate processing per language or fail on code-switched text.
ocr-based pii detection in images and scanned documents
Detects and redacts PII in images and scanned documents by performing optical character recognition (OCR) to extract text and then applying context-aware PII detection to the extracted content. The system handles real-world image quality issues including poor resolution, skewed text, handwritten annotations, and partial visibility. Supports TIFF, PNG, and JPEG formats and can redact detected PII directly in the image output.
Unique: Combines OCR with context-aware PII detection to handle scanned documents and images, including handwritten forms and poor-quality scans, with direct image redaction output preserving document structure.
vs alternatives: Enables end-to-end image PII detection and redaction vs. separate OCR + text PII tools which require manual integration and intermediate text extraction steps.
asr-based pii detection in audio and transcripts
Detects PII in audio files and speech transcripts by handling automatic speech recognition (ASR) errors, conversational disfluencies, and real-world speech patterns. The system recognizes that ASR output contains errors and uses contextual analysis to identify PII despite transcription mistakes (e.g., 'John' transcribed as 'Jon', 'Smith' as 'Smyth'). Supports audio file input and transcript text with conversational patterns including filler words, interruptions, and informal speech.
Unique: Detects PII in audio and transcripts while handling ASR errors and conversational disfluencies, achieving 99.5% accuracy on physician conversations (Providence Health case study) despite speech recognition imperfections.
vs alternatives: Handles ASR-corrupted transcripts with context-aware detection vs. text-only PII tools which fail when applied to noisy ASR output with transcription errors.
structured data de-identification for json, xml, and csv
De-identifies structured data formats (JSON, XML, CSV) by applying schema-aware redaction that preserves data structure and enables downstream processing. The system understands structured data schemas and applies entity-specific redaction rules to relevant fields while maintaining referential integrity and data relationships. Supports nested structures, arrays, and complex data hierarchies.
Unique: Applies schema-aware de-identification to structured data formats (JSON, XML, CSV) preserving data structure and relationships while redacting PII, enabling downstream processing and analytics on de-identified structured data.
vs alternatives: Maintains structured data integrity during de-identification vs. text-based PII tools which treat structured data as plain text and may corrupt structure or break relationships.
entity linking and relationship extraction across documents
Connects related PII entities across multiple documents and extracts relationships between detected entities to maintain data consistency and enable entity resolution. The system identifies when the same person, organization, or account appears across different documents (e.g., matching 'John Smith' in one document with 'J. Smith' in another) and tracks relationships (e.g., 'patient John Smith was treated by Dr. Jane Doe'). This enables consistent pseudonymization where the same entity receives the same replacement across a dataset.
Unique: Performs cross-document entity linking to maintain pseudonymization consistency — the same entity receives the same replacement across a dataset. Extracts relationships between entities to enable knowledge graph construction while preserving privacy through consistent entity replacement.
vs alternatives: Enables consistent de-identification across multi-document datasets where standard PII tools would independently redact each document, potentially creating inconsistent pseudonyms for the same entity.
on-premises and vpc-isolated data processing
Deploys the de-identification engine as a containerized service within customer infrastructure (on-premises or customer VPC) ensuring sensitive data never leaves the customer's network. The system runs as a Docker container in the customer's environment, processes data locally, and returns only de-identified results. This architecture enables compliance with strict data residency requirements (HIPAA, GDPR, CCPA) and eliminates data transmission risk to third-party servers.
Unique: Provides containerized on-premises deployment where sensitive data never leaves customer infrastructure — data is processed locally and only de-identified results are returned. Enables compliance with strict data residency and data sovereignty requirements without relying on cloud infrastructure.
vs alternatives: Eliminates data transmission risk vs. cloud-based PII detection services (AWS Comprehend, Google DLP) which require sending sensitive data to external servers, making it suitable for highly regulated industries with strict data residency mandates.
+6 more capabilities