Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Jointly train or infer on combined datasets using hardware-secured enclaves, keeping all private data confidential.
Enable strategic partnerships and consortiums without data exposure. Our secure multi-party AI computation services use hardware-based Trusted Execution Environments (TEEs) like Intel SGX and AMD SEV to create a neutral, verifiable computation space. > All parties contribute data, but no single party—including the infrastructure provider—can access the raw inputs.
This approach solves critical collaboration barriers in sectors like multi-hospital clinical trials, cross-bank fraud detection networks, and supply chain optimization where data sensitivity prevents traditional data pooling. It transforms proprietary data from a liability into a secure, shared asset.
For related architectures, explore our services on Federated Learning Systems Engineering and Confidential AI Inference Enclave Development. To protect models themselves, see Encrypted AI Model Deployment and Management.
Our engineering delivers secure, collaborative AI systems that unlock new data partnerships while eliminating the risk of exposing proprietary information. Move from theoretical possibility to production-ready, compliant solutions.
A structured roadmap for engineering a confidential computing system where multiple parties can jointly compute on combined datasets without exposing private data.
| Phase & Deliverables | Starter (Proof-of-Concept) | Professional (Production-Ready) | Enterprise (Multi-Organization) |
|---|---|---|---|
Project Duration | 6-8 weeks | 10-16 weeks | 20+ weeks (custom) |
Core Architecture Design | |||
TEE Environment Setup (e.g., Intel SGX, AMD SEV) | Single cloud provider | Multi-cloud or hybrid | Cross-cloud with attestation orchestration |
Secure Multi-Party Computation Protocol Implementation | Basic secure aggregation | Advanced MPC with malicious security | Custom protocol with formal verification |
Integration with Existing Data Pipelines | 1-2 data sources | 3-5 federated data sources |
|
Attestation & Key Management Service | Basic remote attestation | Automated, policy-driven attestation | Centralized governance for multiple organizations |
Performance Benchmarking & Optimization | Latency & throughput baseline | Optimized for production scale | Continuous optimization SLA |
Security Audit & Penetration Testing | Internal review | Third-party audit report | Continuous red teaming program |
Deployment & Orchestration | Manual deployment scripts | Kubernetes operator for TEEs | Enterprise-grade orchestration platform |
Ongoing Support & Maintenance | Email support | 24/7 SLA with 99.9% uptime | Dedicated engineering team & roadmap planning |
Typical Engagement | Feasibility study & POC | End-to-end system deployment | Strategic partnership for network expansion |
Our confidential computing systems enable secure collaboration on sensitive datasets. Organizations can jointly train models and run inferences without exposing their private data, unlocking new value while maintaining strict compliance and security.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers on timelines, security, and process for our confidential multi-party AI systems.
From initial architecture to production deployment, projects typically take 8-12 weeks. This includes 2 weeks for requirements & threat modeling, 3-4 weeks for TEE integration and protocol development, 2 weeks for testing/attestation, and 2 weeks for deployment and handoff. We provide a fixed-scope project plan after the initial discovery phase.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
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.
The first call is a practical review of your use case and the right next step.
