Integrate Quickly
Connect existing applications and workflows without building a private AI infrastructure stack from scratch.
Build with AI.Keep your data private.
Your application shouldn't have to send sensitive data to a public AI service just to use a powerful model. Enclave AI provides private AI inference through an enterprise-ready API.
The API Private Layer
The API experience of public AI. The control of private infrastructure.
Enclave AI gives developers a simple way to integrate powerful AI while keeping sensitive workloads inside controlled, isolated infrastructure. Build the application you want. Let Enclave AI handle the private inference environment behind it.
- 01
Private Inference
Run sensitive AI workloads inside controlled environments rather than relying exclusively on public AI endpoints.
- 02
Simple Integration
Connect applications and workflows through an API-based interface designed to fit into modern development stacks.
- 03
Production Ready
Connect applications and workflows through an API-based interface designed to fit into modern development stacks.
How It Works
One request. One protected path.
The workflow is deliberately simple: connect, protect, process, return, and govern.
- 1RequestYour application sends a prompt or inference request to the API, which is securely transmitted into the configured environment.
- 2Transmit and DecryptThe request is sent to the gateway as ciphertext, passed into the TEE, and then decrypted.
- 3ProcessInference takes place inside the designated private compute environment.
- 4ReturnThe response is encrypted within the TEE, returned to your application, and decrypted on the client side.
- 5GovernUsage, access, and operational activity can be monitored through enterprise controls.
Integration
Private AI without rebuilding your application.
Keep your application architecture flexible while moving the AI execution layer into a managed private environment.
Use Advanced Models
Access supported models for reasoning, generation, extraction, classification, summarization, and other AI workloads.
Stay Flexible
Keep your application separate from the underlying model and infrastructure layer so your AI stack can evolve.
Scaled with Demand
Start with the capacity you need and move toward dedicated infrastructure as inference requirements grow.
Built for Protection
Protect sensitive prompts and outputs throughout the inference lifecycle with confidential computing.
Enterprise Control
Apply the operational visibility and deployment controls enterprise AI teams need as usage grows.
Public AI vs Enclave AI
The difference is where inference happens.
The API call may look similar. The security boundary does not have to be.
Security
Inference should not be a blind spot in your security architecture.
Your application already has security controls. Your network has security controls. Your databases have security controls. The AI inference layer should have them too.
- 1Encrypted TransportProtect data as it moves between your application and the inference environment.
- 2Protected ExecutionRun inference inside isolated environments designed to reduce unnecessary exposure.
- 3Confidential ComputingTrusted Execution Environments can provide hardware-backed protection for workloads during processing.
- 4Access ControlRun inference inside isolated environments designed to reduce unnecessary exposure.
- 5Operational visibilityMonitor inference activity to support security, governance, and troubleshooting.
- 6Data governanceMaintain greater control over where sensitive AI workloads are processed and how they are handled.
Regulated Industries
One API. Sensitive workloads across your organization.
EncAI is designed for organizations where the value of AI is high - and the cost of exposing sensitive information is even higher.
Healthcare
AI without compromising patient privacy.
Support clinical documentation, medical knowledge, internal assistants, administration, analytics, research, and other sensitive healthcare workflows.
Legal
AI without compromising confidentiality.
Power document analysis, contract intelligence, legal research, matter workflows, knowledge management, and more.
Finance
AI without surrendering control of financial intelligence.
Apply private inference to research, risk, compliance, document intelligence, internal knowledge, and financial workflows.
Models
The infrastructure should notlock you into one models
AI is evolving quickly. Your architecture should remain flexible enough to use the right model for the right workload.
Run private AI models of your choice.
OpenAI-compatible LLM endpoints with private prompts and verifiable runtime states - enabling secure deployment of your own custom Docker workloads.
Enterprise Governance
Built for applications handlingsensitive information.
The AI layer needs to fit into the organization's broader security and governance model, especially when applications handle regulated or confidential data.
Compliance
ISO/IEC 27001:2022
Requirements for information security management, data protection, privacy, and responsible handling of sensitive information.
Request for DocumentationCompliance
HIPAA Compliant
Designed to support HIPAA requirements for safeguarding sensitive patient information and maintaining healthcare data privacy.
Request for DocumentationCompliance
GDPR
GDPR-ready requirements for data protection, privacy, and responsible handling of personal information.
Request for DocumentationContact Us
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