About
Limina AI (formerly Private AI) is a context-aware data de-identification solution built for regulated industries. Unlike pattern-matching tools that drop to 60–70% accuracy on real-world data, Limina reads full context to catch names that don't look like names, entities that span sentences, and credit card numbers spoken across conversational turns — achieving 99.5% accuracy on complex data like physician conversations. The platform supports 50+ entity types including names, SSNs, credit cards, diagnoses, medications, and passport numbers across 52 languages with multilingual and code-switching support. It handles messy, unstructured data sources such as ASR transcripts, OCR output, handwritten forms, and chat logs — the kinds of inputs that break most other tools. Limina can redact, pseudonymize, generate synthetic PII, or generalize entities depending on your compliance needs. It integrates natively with AWS, Azure, Snowflake, and NVIDIA NeMo, and produces independent expert determination reports to satisfy auditors under HIPAA, GDPR, CPRA, and other global regulations. Deployed as a container in your own VPC or on-premises, data never transits external servers. This makes Limina ideal for AI teams at healthcare systems, pharmaceutical companies, financial institutions, and contact centers that need to unlock restricted datasets for model training, analytics, or LLM pipelines without legal or compliance blockers.
Key Features
- Context-Aware Detection: Reads full sentence and conversational context to identify PII that pattern-matching tools miss, achieving 99.5% accuracy on real-world data like physician conversations and call transcripts.
- 50+ Entity Types Across 52 Languages: Covers PII, PHI, and PCI in a single API — names, SSNs, credit cards, diagnoses, medications, passport numbers, and international variants, with multilingual and code-switching support.
- Private Infrastructure Deployment: Runs as a container inside your own VPC or on-premises environment so sensitive data never leaves your infrastructure, satisfying even the strictest data residency requirements.
- Flexible Transformation Options: Redact, pseudonymize, generate synthetic PII, or generalize entities depending on your use case — outputs are ready for expert determination without manual review.
- Compliance-Ready Audit Reports: Generates independent expert determination reports accepted by auditors for HIPAA, GDPR, CPRA, and other global data protection regulations.
Use Cases
- Healthcare systems anonymizing physician conversation transcripts and electronic medical records before using them to train clinical AI models.
- Pharmaceutical companies reducing medical inquiry response times by de-identifying patient data to enable faster, compliant analysis.
- Financial institutions scrubbing PCI and PII from call center transcripts and chat logs to feed analytics and quality-assurance workflows.
- AI teams unlocking restricted enterprise datasets for LLM fine-tuning or RAG pipelines without triggering legal and compliance blockers.
- Contact centers pseudonymizing customer interaction data in real time to meet GDPR and CPRA requirements without disrupting existing workflows.
Pros
- Best-in-Class Accuracy: Context-aware approach delivers 99.5% accuracy on complex real-world data, far exceeding the 60–70% typical of pattern-matching alternatives like AWS Comprehend or Google DLP.
- True Data Residency: Containerized deployment in your own VPC or on-prem means data never transits third-party servers, making compliance sign-off straightforward for legal and privacy teams.
- Broad Coverage Out of the Box: 50+ entity types across 52 languages with native integrations for AWS, Azure, Snowflake, and NVIDIA NeMo means minimal engineering effort to get production-ready.
Cons
- Enterprise Pricing: Designed primarily for enterprise use cases; pricing and self-hosted deployment may be cost-prohibitive for small teams or individual developers.
- Infrastructure Overhead: Self-hosted container deployment requires DevOps resources to provision, maintain, and scale, which adds operational complexity compared to fully managed SaaS alternatives.
Frequently Asked Questions
How is Limina AI different from AWS Comprehend or Google DLP?
Limina reads full context rather than matching patterns. Cloud tools like Comprehend, Presidio, and DLP typically achieve 60–70% accuracy on real-world data. Limina achieves 99.5% by understanding sentence structure and conversational context, catching entities those tools miss.
Does my data leave my servers when using Limina AI?
No. Limina deploys as a container inside your own VPC or on-premises infrastructure. All processing happens within your environment, and data never transits Limina's servers.
What types of sensitive data can Limina detect and redact?
Limina covers 50+ entity types including PII (names, SSNs, addresses, passport numbers), PHI (diagnoses, medications, patient IDs), and PCI (credit card numbers, bank accounts) across 52 languages.
What compliance standards does Limina AI support?
Limina produces independent expert determination reports accepted by auditors for HIPAA, GDPR, CPRA, and other global data protection regulations, covering use cases in healthcare, pharma, financial services, and beyond.
Can Limina handle messy or unstructured data?
Yes. Limina is specifically built to handle ASR transcription errors, OCR mistakes, handwritten forms, and conversational disfluencies — the types of noisy, real-world inputs that cause accuracy to drop in other tools.
