Services
Confidential Computing for AI Workloads
Confidential Computing for AI Workloads
Protection of sensitive AI data while actively in use via hardware-based Trusted Execution Environments, securing actual memory enclaves where AI inference and calculations occur. Sub-services include hardware-based TEE development, confidential computing for biometric AI processing, secure multi-party computation services, and encrypted enclave deployment for financial algorithmic modeling.
Confidential AI Inference Enclave Development
Secure deployment of AI models within hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV, ensuring model weights and sensitive inference data are protected from all other processes, including the host OS and cloud provider.
Secure Multi-Party AI Computation Services
Engineering of confidential computing systems that enable multiple organizations to jointly train or infer on combined datasets without exposing their private data to each other, using TEEs for secure aggregation and computation.
Hardware-Based TEE Integration for AI Workloads
End-to-end integration of confidential computing hardware (e.g., AWS Nitro Enclaves, Azure Confidential VMs) into existing AI pipelines, from data ingestion to model serving, with attestation and secure key management.
Encrypted AI Model Deployment and Management
Lifecycle management of AI models where they remain encrypted in memory and during computation, enabling secure model serving APIs and protecting proprietary algorithms in multi-tenant or untrusted environments.
Financial Algorithmic Modeling in Secure Enclaves
Execution of proprietary trading algorithms, risk models, and quantitative analytics within attested enclaves to protect intellectual property and sensitive market data from insider threats and infrastructure compromise.
Confidential AI for Edge and IoT Devices
Deployment of lightweight TEEs on edge devices and gateways to perform local AI inference on sensitive sensor data (e.g., video, audio) without sending raw data to the cloud, ensuring privacy-by-design.
Secure Enclave Orchestration for AI Pipelines
Development of Kubernetes operators and workflow engines (e.g., Kubeflow) to manage the lifecycle of confidential AI training and inference jobs across clusters of TEE-enabled nodes, including attestation verification.
AI Model Confidentiality for Regulatory Compliance
Implementation of confidential computing controls specifically to meet data-in-use protection mandates under regulations like GDPR, HIPAA, and the EU AI Act for AI systems processing personal data.
TEE-Based AI for Defense and Intelligence
Development of air-gapped, hardware-rooted AI systems for classified data processing, ensuring model integrity and preventing data exfiltration even on compromised infrastructure within secure government networks.
Confidential AI Data Pipeline Architecture
Design of end-to-end data pipelines where sensitive data is decrypted, processed by AI models, and re-encrypted entirely within the bounds of a TEE, never persisting in plaintext in storage or memory.
Hardware-Secured AI for Financial Trading
Ultra-low-latency integration of TEEs with FPGA/ASIC-based trading systems to protect high-frequency trading algorithms and market data while maintaining sub-microsecond inference speeds.
Confidential AI in Hybrid Cloud Architectures
Architecture design that splits AI workloads between on-premises TEEs and public cloud confidential computing instances, maintaining data sovereignty and security across the hybrid environment.
TEE-Enabled AI Model Fine-Tuning Services
Secure adaptation of foundation models (e.g., LLMs) on proprietary corporate data within enclaves, ensuring the fine-tuning data and resulting model weights are never exposed to the model provider or infrastructure host.
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How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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