{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"private-ai","slug":"private-ai","name":"Private AI","type":"api","url":"https://www.private-ai.com","page_url":"https://unfragile.ai/private-ai","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"private-ai__cap_0","uri":"capability://data.processing.analysis.context.aware.pii.detection.across.50.entity.types","name":"context-aware pii detection across 50+ entity types","description":"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.","intents":["Identify all sensitive data in unstructured text before using it for LLM fine-tuning or RAG context","Detect medical information, financial data, and personal identifiers in healthcare or financial documents for compliance","Find PII in conversational transcripts, customer support logs, or audio recordings without manual review","Discover confidential company information in internal documents before sharing with external AI systems"],"best_for":["Healthcare organizations processing patient records and clinical notes for AI applications","Financial services firms handling credit card data, SSNs, and account information","Enterprises building LLM applications that require HIPAA, PCI-DSS, or GDPR compliance","Teams processing multilingual datasets with real-world noise (OCR artifacts, speech recognition errors)"],"limitations":["Accuracy degrades on heavily corrupted or severely malformed input (e.g., severely garbled OCR output)","No documented maximum input size or token limits — throughput constraints unknown","Language support is 52 languages but specific list not published; coverage for low-resource languages unknown","Contextual detection may miss PII in highly ambiguous or domain-specific contexts without fine-tuning"],"requires":["API key (authentication method not documented)","Network access to Private AI / Limina endpoints or on-premises deployment infrastructure","For on-prem: Docker/container runtime and customer VPC or on-premises infrastructure"],"input_types":["unstructured text","conversational transcripts","medical notes","financial documents"],"output_types":["structured JSON with detected entities and confidence scores","entity type classification (PII, PHI, PCI, CCI)","entity location/span information for redaction"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_1","uri":"capability://data.processing.analysis.multi.modality.pii.redaction.with.transformation.strategies","name":"multi-modality pii redaction with transformation strategies","description":"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).","intents":["Redact sensitive data from documents before sharing with external vendors or AI systems","Create pseudonymized datasets for AI training while maintaining data utility and consistency","Generate synthetic replacements for PII to preserve statistical properties while removing identifiability","Redact PII from images and scanned documents for compliance and safe sharing"],"best_for":["Data teams preparing datasets for LLM fine-tuning or model training","Compliance officers anonymizing documents for external sharing or regulatory audits","Healthcare and financial institutions creating de-identified datasets for research","Organizations building synthetic data pipelines for privacy-preserving AI applications"],"limitations":["Redaction strategies are not documented — unclear which transformation methods are available (masking, pseudonymization, synthetic generation)","No documented control over redaction consistency across documents or time periods","Image redaction relies on OCR accuracy — redaction quality degrades with poor image quality or handwriting","No streaming or real-time redaction documented — batch processing latency unknown"],"requires":["API key and network access to Private AI / Limina endpoints","For document processing: supported file format (PDF, DOCX, XLS, XLSX, PPTX, XML, JSON, CSV)","For image processing: TIFF, PNG, or JPEG format with readable text"],"input_types":["text","PDF documents","Word documents (DOCX)","spreadsheets (XLS, XLSX)","presentations (PPTX)","structured data (XML, JSON, CSV)","images (TIFF, PNG, JPEG)"],"output_types":["redacted text","redacted documents (same format as input)","redacted images","mapping of original to redacted entities (for consistency tracking)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_10","uri":"capability://data.processing.analysis.multi.language.pii.detection.with.code.switching.support","name":"multi-language pii detection with code-switching support","description":"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.","intents":["Detect PII in multilingual documents and conversations without language preprocessing","Process code-switched text (e.g., Spanglish, Franglais) with accurate PII detection","Handle international PII formats (e.g., EU phone numbers, UK postcodes, Japanese addresses)","De-identify multilingual customer support transcripts and global healthcare records"],"best_for":["Global organizations processing multilingual customer data","Healthcare systems serving multilingual patient populations","Financial institutions with international operations and multilingual documents","Teams processing code-switched conversational data (customer support, interviews)"],"limitations":["Specific list of 52 supported languages is not published — unclear which languages are included","Code-switching support is mentioned but not detailed — unclear which language combinations are tested","No documented accuracy metrics per language — unclear if accuracy varies significantly across languages","Language detection is not documented — unclear if automatic or requires explicit specification"],"requires":["API key and network access to Private AI / Limina endpoints","Input text in one of 52 supported languages (specific list not provided)","For code-switched text: no explicit language specification required (auto-detection assumed)"],"input_types":["text in any of 52 supported languages","code-switched text mixing multiple languages","multilingual documents"],"output_types":["detected entities with language tags","de-identified text preserving language structure","language-specific entity classifications"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_11","uri":"capability://image.visual.ocr.based.pii.detection.in.images.and.scanned.documents","name":"ocr-based pii detection in images and scanned documents","description":"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.","intents":["Detect PII in scanned documents, forms, and images before sharing or archiving","Redact sensitive information from images while preserving document structure and readability","Process handwritten forms and documents with OCR-based PII detection","De-identify medical records, insurance documents, and financial statements in image format"],"best_for":["Healthcare organizations processing scanned patient records and medical forms","Financial institutions handling scanned documents and loan applications","Legal teams reviewing scanned documents with PII redaction","Organizations digitizing paper records with privacy requirements"],"limitations":["OCR accuracy is not documented — unclear how OCR errors affect PII detection accuracy","Handwriting recognition capability is mentioned but not detailed — accuracy on handwritten text unknown","Image quality requirements are not specified — minimum resolution or clarity not documented","Redaction approach for images is not documented — unclear if redaction is pixel-based, text-based, or bounding-box-based","No documented support for multi-page documents or batch image processing"],"requires":["API key and network access to Private AI / Limina endpoints","Image file in TIFF, PNG, or JPEG format","Minimum image quality (not specified) for accurate OCR"],"input_types":["TIFF images","PNG images","JPEG images","scanned documents","handwritten forms"],"output_types":["extracted text with detected PII","redacted image (same format as input)","OCR confidence scores","entity location information (bounding boxes)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_12","uri":"capability://data.processing.analysis.asr.based.pii.detection.in.audio.and.transcripts","name":"asr-based pii detection in audio and transcripts","description":"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.","intents":["Detect PII in audio recordings of customer support calls, interviews, or medical consultations","De-identify speech transcripts with ASR errors and conversational disfluencies","Process physician-patient conversations with high accuracy despite speech recognition errors","Redact PII from audio files before sharing or archiving recordings"],"best_for":["Healthcare organizations processing physician-patient conversations","Contact centers de-identifying customer support call recordings","Research organizations processing interview recordings","Teams building privacy-preserving voice analytics systems"],"limitations":["Audio format support is not documented — unclear which audio formats are supported (WAV, MP3, M4A, etc.)","ASR engine is not documented — unclear if Private AI uses its own ASR or integrates with third-party (Google, AWS, etc.)","ASR error handling approach is not detailed — unclear how it handles severe transcription errors","No documented accuracy metrics for audio PII detection vs. text PII detection","Audio file size limits are not documented"],"requires":["API key and network access to Private AI / Limina endpoints","Audio file in supported format (format list not documented)","For transcripts: conversational text with potential ASR errors"],"input_types":["audio files (format not specified)","speech transcripts with ASR errors","conversational text with disfluencies"],"output_types":["detected PII with confidence scores","de-identified transcript","redacted audio file (if supported)","ASR error handling notes"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_13","uri":"capability://data.processing.analysis.structured.data.de.identification.for.json.xml.and.csv","name":"structured data de-identification for json, xml, and csv","description":"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.","intents":["De-identify JSON API responses and structured data exports before sharing with external systems","Redact PII from XML documents and configuration files while preserving structure","Clean CSV datasets for sharing with external teams or for ML training","Maintain data relationships and referential integrity during de-identification of structured data"],"best_for":["Data teams preparing structured datasets for external sharing or ML training","API developers de-identifying JSON responses before logging or sharing","Organizations exporting data from databases in structured formats","Teams building privacy-preserving data pipelines with structured data"],"limitations":["Schema-aware redaction approach is not documented — unclear how schemas are specified or inferred","Support for nested structures and complex hierarchies is not detailed","No documented handling of schema evolution or schema mismatches","Field-level redaction configuration is not documented — unclear how to specify which fields contain PII"],"requires":["API key and network access to Private AI / Limina endpoints","Structured data in JSON, XML, or CSV format","Optional: schema specification for schema-aware redaction (format not documented)"],"input_types":["JSON objects and arrays","XML documents","CSV files with headers"],"output_types":["de-identified JSON with same structure","de-identified XML with same structure","de-identified CSV with same schema","field-level redaction report"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_2","uri":"capability://data.processing.analysis.entity.linking.and.relationship.extraction.across.documents","name":"entity linking and relationship extraction across documents","description":"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.","intents":["Maintain entity consistency when pseudonymizing multi-document datasets (same person gets same pseudonym everywhere)","Extract relationship graphs from documents for knowledge base construction while preserving privacy","Identify duplicate or related entities across documents to enable accurate de-identification","Build entity resolution mappings for downstream analytics on de-identified data"],"best_for":["Healthcare systems processing patient records across multiple encounters and providers","Financial institutions tracking accounts and transactions across documents","Legal teams reviewing documents with consistent entity replacement for privilege review","Research organizations building de-identified knowledge graphs from sensitive documents"],"limitations":["Entity linking approach is not documented — unclear whether it uses string similarity, semantic embeddings, or rule-based matching","No documented accuracy metrics for entity resolution or relationship extraction","Relationship extraction scope is unclear — which relationship types are supported (clinical, financial, organizational)?","No documented handling of ambiguous entities (e.g., common names that could refer to multiple people)"],"requires":["API key and network access to Private AI / Limina endpoints","Multiple documents or a document collection to enable entity linking","Supported input format (text, PDF, DOCX, or other document formats)"],"input_types":["multiple text documents","document collections (PDF, DOCX, etc.)","structured data with entity references (JSON, CSV)"],"output_types":["entity resolution mappings (original → pseudonym)","relationship graph (entity → entity relationships)","consistency report (entities and their occurrences across documents)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_3","uri":"capability://automation.workflow.on.premises.and.vpc.isolated.data.processing","name":"on-premises and vpc-isolated data processing","description":"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.","intents":["Process sensitive healthcare data without transmitting it to external cloud services","Comply with data residency requirements (e.g., EU data must stay in EU)","Reduce data exfiltration risk by processing PII in isolated network environments","Maintain audit trails and data governance within customer infrastructure"],"best_for":["Healthcare organizations subject to HIPAA with strict data residency requirements","Financial institutions processing regulated data (PCI-DSS, SOX) that cannot leave premises","European organizations requiring GDPR compliance with data localization","Government agencies and defense contractors with classified data handling requirements"],"limitations":["Requires customer infrastructure management — no managed service; customer responsible for deployment, scaling, and updates","Container resource requirements not documented — CPU, memory, and storage needs unknown","No documented update mechanism for model improvements or security patches","Scaling and high-availability setup is customer responsibility — no built-in clustering or failover"],"requires":["Docker runtime or Kubernetes cluster","Customer VPC or on-premises network infrastructure","Sufficient compute resources (exact requirements not documented)","Network connectivity for license validation (if required)","API key or license file for container authentication"],"input_types":["text","documents (PDF, DOCX, etc.)","images","audio files"],"output_types":["de-identified text","de-identified documents","de-identified images","processing logs and audit trails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_4","uri":"capability://automation.workflow.saas.cloud.hosted.de.identification.with.multi.region.deployment","name":"saas cloud-hosted de-identification with multi-region deployment","description":"Provides cloud-hosted de-identification API where data is processed in Limina's managed infrastructure across multiple geographic regions (US, Canada, UK, Germany, Japan, Hong Kong, Australia, Switzerland). The SaaS model offers managed scaling, automatic updates, and no infrastructure management burden, with data processed in region-specific endpoints to support data residency compliance. Customers can choose between on-premises and SaaS deployment based on compliance and operational requirements.","intents":["Process PII detection without managing on-premises infrastructure","Scale de-identification workloads elastically without capacity planning","Leverage managed service with automatic security patches and model updates","Process data in specific geographic regions for data residency compliance"],"best_for":["Organizations without on-premises infrastructure or DevOps capability","Teams requiring elastic scaling for variable de-identification workloads","Companies prioritizing operational simplicity over data residency control","Enterprises with multi-region requirements needing region-specific endpoints"],"limitations":["Data is transmitted to Limina's cloud infrastructure — not suitable for organizations with strict data residency requirements","No documented data retention policy — unclear how long data is retained after processing","No documented encryption in transit or at rest specifications","Pricing is enterprise-only ('REQUEST A QUOTE') — no transparent per-request or per-token pricing","No documented SLA for latency, uptime, or accuracy"],"requires":["API key (authentication method not documented)","Network access to Limina's cloud endpoints","Acceptance of data processing in Limina's infrastructure"],"input_types":["text","documents","images","audio files"],"output_types":["de-identified text","de-identified documents","de-identified images","API response with entity metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_5","uri":"capability://tool.use.integration.marketplace.integrated.de.identification.for.snowflake.aws.and.azure","name":"marketplace-integrated de-identification for snowflake, aws, and azure","description":"Integrates de-identification capabilities directly into data warehouse and cloud marketplace environments (Snowflake, AWS Marketplace, Azure Marketplace) enabling PII detection and redaction within existing data pipelines without external API calls. The integration allows customers to apply de-identification transformations as SQL functions or native data processing steps within their warehouse, reducing data movement and enabling privacy-preserving analytics on sensitive data in place.","intents":["Apply de-identification transformations within Snowflake SQL queries without exporting data","Deploy de-identification as AWS or Azure Marketplace application for integrated billing and governance","Build privacy-preserving data pipelines within data warehouses without external API latency","Enable data teams to de-identify data using familiar warehouse tools (SQL, stored procedures)"],"best_for":["Snowflake customers processing sensitive data in their warehouse","AWS and Azure customers seeking integrated marketplace solutions","Data teams building privacy-preserving analytics pipelines","Organizations wanting to minimize data movement and external API calls"],"limitations":["Marketplace integration details are not documented — unclear how Snowflake integration works (UDF, stored procedure, native function?)","AWS and Azure marketplace deployment model is not specified — unclear if it's a managed service or customer-deployed","No documented performance characteristics for in-warehouse processing vs. external API","Pricing through marketplaces is not transparent — likely enterprise-only with custom quotes"],"requires":["Snowflake account (for Snowflake integration) or AWS/Azure account (for marketplace deployment)","Marketplace subscription or integration setup (process not documented)","API key or authentication credentials for marketplace integration"],"input_types":["Snowflake tables and views","AWS S3 data (via marketplace)","Azure Blob Storage data (via marketplace)"],"output_types":["de-identified Snowflake tables","de-identified data in AWS/Azure storage","transformation logs and audit trails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_6","uri":"capability://data.processing.analysis.fine.tuning.for.domain.specific.and.custom.entity.types","name":"fine-tuning for domain-specific and custom entity types","description":"Enables customers to fine-tune the de-identification model for domain-specific PII patterns and custom entity types not covered by the standard 50+ entity types. The system allows training on customer data to recognize industry-specific sensitive information (e.g., internal employee IDs, proprietary account numbers, domain-specific medical codes) and improve accuracy on customer-specific data distributions. Fine-tuning is performed in collaboration with Limina's technical team as part of onboarding.","intents":["Detect custom PII types specific to your industry or organization (e.g., internal employee IDs, proprietary identifiers)","Improve detection accuracy on your specific data distribution and language patterns","Reduce false positives and false negatives for your use case through targeted training","Adapt the model to domain-specific terminology and entity formats"],"best_for":["Healthcare organizations with proprietary medical codes or internal patient identifiers","Financial institutions with custom account number formats or internal transaction IDs","Enterprises with industry-specific confidential information (e.g., research data, trade secrets)","Organizations with non-standard data formats or domain-specific terminology"],"limitations":["Fine-tuning process is not documented — unclear whether it requires labeled training data, how much data is needed, or how long it takes","No documented fine-tuning API — appears to be a manual process requiring Limina's technical team involvement","No documented versioning or rollback mechanism for fine-tuned models","Fine-tuning cost is not transparent — likely included in enterprise pricing but not separately quoted"],"requires":["Enterprise plan (fine-tuning not available on lower tiers)","Labeled training data with examples of custom entity types (quantity not specified)","Collaboration with Limina's technical team during onboarding","Domain expertise to define custom entity types and validation criteria"],"input_types":["labeled training data (text with annotated custom entities)","example documents with custom PII patterns"],"output_types":["fine-tuned model version","accuracy metrics on custom entity types","validation report on fine-tuned model performance"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_7","uri":"capability://safety.moderation.expert.determination.and.compliance.reporting","name":"expert determination and compliance reporting","description":"Provides expert determination reports and compliance documentation from independent partners validating de-identification effectiveness and regulatory compliance. The system generates reports demonstrating that de-identification meets standards for HIPAA Safe Harbor, GDPR anonymization, CCPA compliance, and other regulatory frameworks. Reports are prepared by independent experts and can be used for regulatory audits, compliance demonstrations, and legal defensibility.","intents":["Obtain expert validation that de-identified data meets HIPAA Safe Harbor standards","Generate compliance documentation for regulatory audits and inspections","Demonstrate GDPR anonymization compliance to data protection authorities","Build legal defensibility for de-identification decisions in litigation or regulatory proceedings"],"best_for":["Healthcare organizations subject to HIPAA requiring Safe Harbor compliance documentation","European organizations needing GDPR anonymization validation","Financial institutions demonstrating PCI-DSS compliance","Organizations undergoing regulatory audits or compliance assessments"],"limitations":["Expert determination process is not documented — unclear who the independent experts are, what standards they use, or how long reports take","No documented cost for expert determination reports — likely enterprise-only add-on","Reports are not pre-generated — appear to require manual expert review and custom report generation","No documented template or standard report format"],"requires":["Enterprise plan with dedicated support","Request for expert determination during onboarding or as add-on service","De-identified dataset and documentation of de-identification methodology","Specification of regulatory framework (HIPAA, GDPR, CCPA, etc.)"],"input_types":["de-identified dataset","de-identification methodology documentation","regulatory framework specification"],"output_types":["expert determination report","compliance validation documentation","regulatory audit-ready report"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_8","uri":"capability://tool.use.integration.python.sdk.for.programmatic.de.identification.integration","name":"python sdk for programmatic de-identification integration","description":"Provides a Python SDK for integrating de-identification capabilities directly into Python applications, data pipelines, and ML workflows. The SDK abstracts API complexity and enables developers to call de-identification functions with simple Python method calls, handle responses programmatically, and integrate de-identification into data processing pipelines without managing HTTP requests or authentication directly.","intents":["Integrate de-identification into Python data processing pipelines and ETL workflows","Call de-identification functions from Python applications without managing REST API calls","Build privacy-preserving ML training pipelines that de-identify data before model training","Automate de-identification in batch processing jobs and scheduled tasks"],"best_for":["Python developers building data pipelines and ETL workflows","Data scientists preparing datasets for ML training with privacy requirements","Teams using Python-based ML frameworks (scikit-learn, TensorFlow, PyTorch)","Organizations automating de-identification in scheduled batch jobs"],"limitations":["SDK documentation is not provided — no API reference, examples, or installation instructions available","SDK version and maturity level are unknown — unclear if it's production-ready or beta","No documented SDK features — unclear what methods are available or how they map to API endpoints","No documented error handling or exception types in SDK","Python version requirements not specified"],"requires":["Python 3.x (exact version not specified)","Private AI / Limina Python SDK (installation method not documented)","API key for authentication","Network access to Private AI / Limina endpoints (if using SaaS) or local container (if using on-premises)"],"input_types":["Python strings","file paths (for document processing)","pandas DataFrames","lists or dictionaries of text"],"output_types":["Python dictionaries with detected entities","de-identified strings or documents","entity metadata and confidence scores"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__cap_9","uri":"capability://tool.use.integration.rest.api.with.high.throughput.processing","name":"rest api with high-throughput processing","description":"Exposes de-identification capabilities through a high-throughput REST API supporting real-time and batch processing of PII detection and redaction requests. The API processes billions of requests per month in production (per product claims) and supports concurrent requests with documented rate limiting and quota management. API endpoints handle text, documents, images, and audio with configurable response formats and transformation strategies.","intents":["Integrate de-identification into web applications and microservices via REST API","Build real-time PII detection for user-generated content moderation","Process high-volume batch de-identification jobs with concurrent API requests","Integrate de-identification into third-party applications and platforms via REST endpoints"],"best_for":["Web application developers integrating de-identification into backend services","Platform teams building content moderation systems with real-time PII detection","Organizations processing high-volume de-identification workloads","Teams integrating de-identification into third-party applications via REST"],"limitations":["API documentation is not provided — no endpoint specifications, request/response schemas, or examples available","Rate limiting and quota information is not documented — throughput constraints unknown","No documented latency SLA or performance guarantees","Authentication method is not documented — unclear if API key, OAuth, or other mechanism is used","Error handling and error codes are not documented"],"requires":["API key (authentication method not documented)","Network access to Private AI / Limina API endpoints","HTTP client library (curl, requests, etc.)","Understanding of request/response format (not documented)"],"input_types":["JSON payloads with text","multipart form data with documents or images","audio files"],"output_types":["JSON response with detected entities","de-identified text or documents","entity metadata and confidence scores"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"private-ai__headline","uri":"capability://data.processing.analysis.privacy.preserving.data.processing.api","name":"privacy-preserving data processing api","description":"A comprehensive API that detects and redacts over 50 types of Personally Identifiable Information (PII) across various formats, ensuring compliance for sensitive data usage in AI training without compromising privacy.","intents":["best privacy-preserving API","API for redacting PII","data processing API for sensitive information","best API for compliance in AI training","PII detection API for developers"],"best_for":["organizations handling sensitive data","developers needing compliance solutions"],"limitations":[],"requires":[],"input_types":["text","documents","images","audio"],"output_types":["redacted text","redacted documents","redacted images","redacted audio"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["API key (authentication method not documented)","Network access to Private AI / Limina endpoints or on-premises deployment infrastructure","For on-prem: Docker/container runtime and customer VPC or on-premises infrastructure","API key and network access to Private AI / Limina endpoints","For document processing: supported file format (PDF, DOCX, XLS, XLSX, PPTX, XML, JSON, CSV)","For image processing: TIFF, PNG, or JPEG format with readable text","Input text in one of 52 supported languages (specific list not provided)","For code-switched text: no explicit language specification required (auto-detection assumed)","Image file in TIFF, PNG, or JPEG format","Minimum image quality (not specified) for accurate OCR"],"failure_modes":["Accuracy degrades on heavily corrupted or severely malformed input (e.g., severely garbled OCR output)","No documented maximum input size or token limits — throughput constraints unknown","Language support is 52 languages but specific list not published; coverage for low-resource languages unknown","Contextual detection may miss PII in highly ambiguous or domain-specific contexts without fine-tuning","Redaction strategies are not documented — unclear which transformation methods are available (masking, pseudonymization, synthetic generation)","No documented control over redaction consistency across documents or time periods","Image redaction relies on OCR accuracy — redaction quality degrades with poor image quality or handwriting","No streaming or real-time redaction documented — batch processing latency unknown","Specific list of 52 supported languages is not published — unclear which languages are included","Code-switching support is mentioned but not detailed — unclear which language combinations are tested","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=private-ai","compare_url":"https://unfragile.ai/compare?artifact=private-ai"}},"signature":"XgjP3RXYDuLvVsAZ+yYgxOViGTlpsD9+Sqk8AbvHUjQctImZNJndTM86TFJe7oJcJIl+GMoD31X7YfgDj4R7BQ==","signedAt":"2026-06-21T18:43:57.050Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/private-ai","artifact":"https://unfragile.ai/private-ai","verify":"https://unfragile.ai/api/v1/verify?slug=private-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}