Private AI

Detects personally identifiable information (names, SSNs, passport numbers, email addresses, phone numbers) and protected health information (medical conditions, medications, diagnoses) across 52 languages including code-switching and non-Latin scripts. Uses a unified neural model trained on real-world conversational data, ASR errors, OCR mistakes, and handwritten forms to identify entities in context rather than via pattern matching, enabling detection of implicit PII references and domain-specific variants.

Removes or replaces detected PII with redaction masks, pseudonymized tokens, synthetic PII, or custom replacement values while preserving document structure and downstream NLP task performance. Supports multiple transformation modes (masking, tokenization, synthetic generation) applied selectively to entity types, enabling safe use of sensitive data in LLM context windows, training datasets, and analytics pipelines without exposing original values.

Integrates with Snowflake via user-defined functions (UDFs) or stored procedures, enabling PII detection directly on data warehouse tables without exporting data to external systems. Allows organizations to scan billions of records in Snowflake using SQL queries, apply transformations in-place, and maintain data governance within the data warehouse, reducing data movement and enabling real-time compliance scanning of production data.

Integrates with NVIDIA NeMo framework for embedding PII detection and redaction into large language model pipelines, enabling organizations to preprocess training data and inference inputs to remove sensitive information before model processing. Supports NeMo's data processing workflows and enables fine-tuning of LLMs on de-identified data while maintaining semantic quality for downstream tasks.

Available as managed service on AWS Marketplace and Azure Marketplace, enabling one-click deployment and integration with cloud provider billing, identity management, and compliance frameworks. Simplifies procurement and deployment for organizations already using AWS or Azure, with automatic updates, scaling, and integration with cloud-native tools (AWS IAM, Azure AD, CloudWatch, Azure Monitor).

Processes multi-format documents (DOCX, PDF, CSV, XLS, PPTX, XML, JSON) and images (TIFF, PNG, JPEG) to extract and detect PII while preserving original document structure, formatting, and layout. Integrates OCR for image-based documents and handles corrupted OCR output, handwritten forms, and mixed-format documents (e.g., PDFs with embedded images), returning entity locations mapped to original document coordinates for precise redaction or highlighting.

Processes audio files by transcribing speech-to-text (ASR) and detecting PII entities in the resulting transcription, handling ASR errors, disfluencies, and conversational speech patterns. Integrates ASR error handling into the detection model, enabling accurate PII identification in noisy or imperfect transcriptions without requiring manual correction, and returns entity locations mapped to audio timestamps for precise audio redaction or masking.

Processes large volumes of documents, text, and media files asynchronously via batch API endpoints, enabling organizations to scan billions of records without blocking on individual request latency. Supports bulk uploads of multiple files, configurable transformation strategies per batch, and returns results via callback webhooks or polling, with claimed processing of billions of API calls per month and deployment across multiple geographic regions (US, Canada, UK, Germany, Japan, Hong Kong, Australia, Switzerland).

Identifies relationships between detected PII entities across documents and conversations, linking related information (e.g., connecting a patient name to their medical record number, SSN, and insurance ID across multiple documents). Uses entity resolution and graph-based linking to construct a unified view of sensitive data across unstructured sources, enabling detection of PII exposure patterns and data leakage that single-document entity extraction would miss.

Converts unstructured text, documents, and conversations into structured intelligence by extracting PII entities, relationships, and context into JSON or database-ready formats. Enables downstream analytics, compliance reporting, and data governance workflows by providing machine-readable PII metadata (entity type, confidence, location, relationships) that can be ingested into data warehouses, SIEM systems, or custom analytics pipelines without manual parsing.

Detects PII across 52 languages including code-switching (mixing multiple languages in single documents), non-Latin scripts (Arabic, Chinese, Cyrillic, Devanagari), and language-specific PII formats (e.g., Indian Aadhaar numbers, EU VAT IDs, Japanese My Number). Uses language-aware entity detection that adapts to regional PII formats and naming conventions, enabling organizations with multilingual data to apply consistent PII policies across all languages without separate detection pipelines.

Provides Python SDK for integrating Private AI PII detection into custom applications, data pipelines, and ML workflows without building REST API clients. Enables local function calls with automatic request/response handling, error management, and optional caching, allowing developers to embed PII detection directly in Python code for data preprocessing, model training, and compliance automation.

Deploys Private AI detection engine on customer infrastructure (on-premises servers, AWS VPC, Azure VNet, or private cloud) with guarantee that data never leaves the specified environment. Enables organizations with strict data residency requirements (GDPR, HIPAA, data sovereignty laws) to use PII detection without sending sensitive data to external APIs, while maintaining feature parity with cloud deployment (same detection models, transformation strategies, and accuracy).