Enable cross-enterprise AI collaboration where insights are shared, but sensitive datasets never leave their sovereign control. Our MPC engineering services build the cryptographic infrastructure for secure, multi-party AI training and inference.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Engineer cryptographic protocols for joint AI model development across organizations without exposing raw data.
Enable cross-enterprise AI collaboration where insights are shared, but sensitive datasets never leave their sovereign control. Our MPC engineering services build the cryptographic infrastructure for secure, multi-party AI training and inference.
We design and implement distributed protocols using frameworks like MP-SPDZ and Obliv-C that allow multiple entities to:
This is the foundational layer for privacy-preserving consortium AI in sectors like:
Our approach delivers deterministic outcomes, not probabilistic privacy. We architect systems where data sovereignty is mathematically guaranteed, enabling you to unlock collaborative intelligence while mitigating the legal and competitive risks of data sharing. Explore our broader capabilities in Federated Learning Systems Engineering and Confidential Computing for AI Workloads.
Our Secure Multi-Party Computation (MPC) Engineering service enables strategic data partnerships without the legal and security risks of raw data sharing. We build cryptographic protocols that allow you to train superior AI models with partners, competitors, or suppliers while keeping all source data private and secure.
Collaborate with industry peers, research institutions, or supply chain partners on joint AI initiatives. Our MPC protocols enable training on combined datasets for richer insights, while cryptographic guarantees ensure no party can access another's raw data. This removes the primary legal and competitive barrier to data collaboration.
Train more accurate, robust, and generalizable AI models by leveraging diverse, multi-party datasets. MPC allows you to benefit from data variety and volume that would be impossible to access otherwise, leading to superior model outcomes without centralized data collection. Learn more about our approach to Federated Learning Systems Engineering, a complementary decentralized paradigm.
Maintain strict data sovereignty and comply with GDPR, CCPA, HIPAA, and industry-specific regulations. Our MPC implementations provide a technical guarantee that personal data and proprietary intellectual property never leave their secure origin, creating an auditable trail for compliance officers and legal teams.
Bypass complex data sharing agreements, liability clauses, and lengthy legal negotiations. MPC shifts the collaboration framework from contractual trust to cryptographic proof, dramatically simplifying partnership setup and ongoing governance. This is a core component of building a Privacy-Preserving AI Computation strategy.
Benchmark your AI models against industry aggregates without revealing your proprietary algorithms or data. MPC protocols enable privacy-preserving analytics and joint model evaluation, allowing you to understand your competitive position and identify improvement areas without exposing trade secrets.
Build an infrastructure-ready for emerging data collaboration mandates and consortium models. As industries move towards shared intelligence pools (e.g., for fraud detection, medical research, supply chain resilience), MPC provides the foundational layer for secure, scalable participation. Explore related techniques like Homomorphic Encryption AI Integration for encrypted cloud inference.
Our phased methodology ensures a secure, auditable, and production-ready MPC system, from initial design to ongoing maintenance.
| Project Phase | Key Activities | Primary Deliverables | Typical Timeline |
|---|---|---|---|
Phase 1: Discovery & Architecture | Requirements analysis, threat modeling, protocol selection (e.g., SPDZ, ABY), cryptographic library evaluation (e.g., MP-SPDZ) | Technical specification document, threat model report, high-level system architecture diagram, proof-of-concept scope | 2-3 weeks |
Phase 2: Protocol Implementation & Core Development | Custom MPC circuit/function development, integration with data sources, secure multi-party communication layer, unit testing | Core MPC protocol codebase, integration adapters, unit test suite, initial performance benchmarks | 4-8 weeks |
Phase 3: Security Hardening & Audit Preparation | Internal security review, side-channel analysis, formal verification of critical components, preparation for external audit | Hardened codebase, security review report, formal verification artifacts, audit-ready documentation package | 3-4 weeks |
Phase 4: External Security Audit | Coordination with accredited third-party cryptography audit firm (e.g., Trail of Bits, NCC Group), remediation of findings | Independent security audit report, patched code with all critical/high findings resolved | 3-5 weeks (external) |
Phase 5: Deployment & Integration | Environment provisioning (cloud/on-prem), CI/CD pipeline setup, load testing, integration with client applications | Deployed MPC service, operational runbook, integration SDK/client libraries, performance SLA report | 2-4 weeks |
Phase 6: Production Support & Maintenance (Ongoing) | Monitoring, incident response, cryptographic key rotation, periodic security updates, performance optimization | 99.9% uptime SLA, 24/7 monitoring dashboard, quarterly security review reports, optional retainer for updates | Ongoing |
Our Secure Multi-Party Computation (MPC) engineering services enable cross-organizational collaboration on sensitive data, unlocking new insights while maintaining strict cryptographic privacy. We build production-ready MPC systems for regulated industries where data sharing is a barrier to innovation.
Jointly train anomaly detection models on encrypted transaction data from multiple financial institutions to identify sophisticated, cross-institutional fraud patterns without exposing raw customer data. This enables compliance with data sovereignty laws while improving collective security posture.
Learn more about our approach to Financial Services Algorithmic AI and Risk Modeling.
Enable pharmaceutical companies and research consortia to perform federated analyses on encrypted patient records from disparate healthcare providers. Our MPC protocols allow for secure statistical computation, accelerating drug discovery and treatment efficacy studies while preserving patient privacy under HIPAA and similar frameworks.
This architecture often complements our Federated Learning Systems Engineering services.
Allow competing manufacturers and logistics providers to collaboratively analyze encrypted supply chain data—such as inventory levels, shipment delays, and supplier reliability—to build predictive models for systemic disruptions. No single party reveals its operational data, fostering industry-wide resilience.
Explore our related work in Intelligent Supply Chain and Autonomous Replenishment.
Empower retailers or service providers in the same sector to compute aggregate market metrics—like total regional demand or average pricing—from their combined, encrypted sales data. This provides critical business intelligence for strategic planning without compromising competitive secrets.
See how this integrates with Retail and E-Commerce Hyper-Personalization strategies.
Develop and train machine learning models on datasets split between multiple data owners (e.g., government agencies, defense contractors) where the raw data cannot leave its secure enclave. Our MPC protocols compute gradients and updates over encrypted shares, enabling collaborative AI development on the most sensitive datasets.
This is a core component of our Privacy-Preserving AI Model Training offerings.
Build systems where multiple parties (e.g., banks, telecoms) can jointly verify a user's identity or check credentials against their combined, encrypted databases without revealing the user's full interaction history with any single entity. This reduces fraud while enhancing user privacy.
For foundational privacy technologies, review our work in Zero-Knowledge Proof AI Integration.
Build AI models across organizations without sharing raw data, enabling secure cross-enterprise collaboration.
Enable joint AI training and inference across multiple entities—such as competing banks or healthcare providers—where no single party ever sees another's sensitive data. MPC protocols like
SPDZandABYallow you to compute on combined datasets while keeping inputs cryptographically separated.
Our engineering delivers:
Typical outcomes include reducing data-sharing negotiation time from months to weeks and enabling new revenue streams from previously impossible data partnerships. We architect these systems to integrate with your existing data pipelines and AI infrastructure, ensuring a seamless transition from centralized to distributed, privacy-first AI. Explore our broader approach to Privacy-Preserving AI Computation or see how this complements Federated Learning Systems Engineering for decentralized training paradigms.
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 about our Secure Multi-Party Computation engineering process, timelines, security, and outcomes.
Our standard engagement for a production-ready MPC protocol is 6-10 weeks. This includes 1-2 weeks for threat modeling and architecture design, 3-5 weeks for cryptographic implementation and integration, and 2-3 weeks for security auditing and deployment. For complex cross-enterprise collaborations, timelines extend to 12-16 weeks to accommodate legal and infrastructure alignment. We provide a detailed project plan with weekly milestones during the 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.
