Deploy AI in Production Without Compliance Nightmares

Overhead varies by workload. CPU- and I/O-bound agents are typically in the low single-digits; memory-intensive operations can reach the low-to-mid teens due to encryption. We publish benchmarks for common AI patterns (LLMs, embeddings, CV pipelines)—request the latest report.

Cold-start initialization adds a brief one-time hit. For latency-sensitive paths we offer persistent enclaves to keep agents warm and avoid cold-start spikes.

Both. Deploy your fine-tuned models, use our vetted library, or connect to external models via our secure proxy layer. Full flexibility within the trust boundary.

All data processing happens within secure enclaves using confidential computing technology. Your data is encrypted end-to-end and never leaves your control. Even we cannot access your data or queries.

No. Cryptographic attestation ensures even we cannot access enclave contents. The hardware enforces this—not policy or pinky promises.

We follow secure development practices, conduct regular audits, and maintain a bug bounty program. Attestation reports show exactly what code is running so you can verify before trusting.

HIPAA, SOC 2 Type II, ISO 27001, GDPR, CCPA, PCI DSS, FedRAMP, and ITAR. Our architecture is designed to meet the strictest regulatory requirements.

Yes. We sign BAAs and our architecture exceeds HIPAA's technical safeguards. PHI never exists unencrypted outside the enclave.

Yes. Enterprise plans include compliance packages with audit reports, attestation certificates, and documentation for your compliance team.

Yes. Enclave AI offers REST APIs, SDKs for major languages, and pre-built integrations for common enterprise systems. Deploy on-premises, in your private cloud, or use our secure cloud infrastructure.

On-premises deployment in your data center, private cloud deployment (AWS, Azure, GCP), hybrid cloud with secure enclaves, or our fully-managed secure cloud infrastructure.

Yes. Full on-prem deployment with no external dependencies. We provide offline model updates via secure transfer mechanisms.

State-of-the-art language models optimized for accuracy and efficiency, including GPT-4 class models and specialized models for healthcare, legal, and financial domains. All running within secure enclaves.

Intel SGX, AMD SEV, AWS Nitro Enclaves, Azure Confidential Computing, and ARM TrustZone. We abstract the differences so you can deploy anywhere.

Depends on the platform. SGX supports up to 1TB EPC, SEV up to full VM memory, Nitro up to 768GB. We help optimize memory usage for your workload.

Healthcare (HIPAA), Financial Services (SOC 2, PCI DSS), Legal (attorney-client privilege), Government (FedRAMP, ITAR), and any organization handling sensitive or regulated data.

Medical diagnosis assistance, financial document analysis, legal contract review, customer support with PII, internal knowledge bases, code analysis with proprietary algorithms.

Unlike public AI services, Enclave AI never stores or trains on your data. Everything runs in isolated secure enclaves with cryptographic attestation. Your sensitive data stays completely private and under your control.

Flexible pricing based on usage and deployment model. Starter plan for small teams, Professional for growing organizations, and Enterprise with custom pricing, unlimited usage, and dedicated support.

Yes. We offer a 14-day free trial with full features and personalized demos for enterprise customers. Contact our team to schedule a demo tailored to your use case.

24/7 technical support for Enterprise customers, dedicated success manager, implementation assistance, compliance documentation support, and regular security updates.

We're currently in private beta with select partners. General availability is planned for Q1 2025. Join the waitlist for early access and exclusive launch pricing.

Basic setup takes minutes with our cloud offering. On-prem deployments typically complete in 1-2 weeks depending on your infrastructure. We provide hands-on support throughout.

For cloud: just an API key. For on-prem: compatible hardware (Intel SGX, AMD SEV, etc.), container orchestration (K8s preferred), and network connectivity for model updates.