Free 30-minute system review for production AI teams

Guides on retrieval, evaluation, orchestration, and production AI delivery

Need help designing, building, or shipping a production AI system?

Get in touch

Compare architectures, tradeoffs, and implementation paths

See comparisons

Free 30-minute system review for production AI teams

Book a call

Guides on retrieval, evaluation, orchestration, and production AI delivery

Browse guides

Need help designing, building, or shipping a production AI system?

Get in touch

Compare architectures, tradeoffs, and implementation paths

See comparisons

Secure Multi-Party Computation (MPC) Engineering | Inference Systems

Services

Secure Multi-Party Computation (MPC) Engineering

Build cross-enterprise AI collaborations without exposing sensitive data. We develop distributed cryptographic protocols for joint model training and inference where no party sees another's raw data.

Leadership team gathered around a table reviewing an AI system plan.

SECURE MULTI-PARTY COMPUTATION (MPC)

Collaborate on AI Without Compromising Data Sovereignty

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:

Train a shared model on their combined datasets without any party seeing another's raw data.
Perform joint inference on encrypted inputs, revealing only the final aggregated result.
Maintain cryptographic proof of data privacy for regulatory compliance (GDPR, CCPA).

This is the foundational layer for privacy-preserving consortium AI in sectors like:

Financial Services: Collaborative fraud detection across banks.
Healthcare: Multi-institutional research on patient data.
Supply Chain: Optimizing logistics with partner data while protecting trade secrets.

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.

ENTERPRISE VALUE

Business Outcomes of MPC-Powered Collaboration

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.

Unlock New Data Partnerships

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.

Zero

Raw Data Exposure

100%

Cryptographic Guarantee

Accelerate Model Performance

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.

40-60%

Accuracy Lift Potential

Reduced

Bias & Overfitting

Ensure Regulatory & IP Compliance

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.

GDPR/CCPA

Compliant by Design

Full

IP Retention

Reduce Legal & Contractual Overhead

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.

Weeks → Days

Partnership Launch

Eliminated

Data Liability Risk

Gain Competitive Intelligence Securely

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.

Secure

Market Benchmarking

Protected

Algorithm IP

Future-Proof Your Data Strategy

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.

Scalable

To 10+ Parties

Foundation

For Consortium AI

Structured Engineering Approach

Typical MPC Project Phases and Deliverables

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

ENTERPRISE USE CASES

Industry Applications for Secure MPC

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.

Cross-Bank Fraud Detection Networks

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.

Zero Data Exposure

Cryptographic Guarantee

Regulatory Compliant

GDPR, CCPA, GLBA

Multi-Hospital Clinical Research

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.

HIPAA Compliant

Privacy by Design

Accelerated Studies

Faster Insights

Supply Chain Risk Intelligence

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.

Competitor Collaboration

Secure Data Pooling

Predictive Risk Models

Proactive Mitigation

Competitive Market Analytics

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.

Preserve Trade Secrets

Input Privacy

Actionable Insights

Aggregate Outputs

Secure AI Model Training for Sensitive Data

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.

Air-Gapped Compatible

High-Security Environments

End-to-End Encrypted

Training Process

Privacy-Preserving Identity Verification

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.

Reduced Identity Fraud

Collective Verification

User-Centric Privacy

Minimal Data Exposure

Contact

Talk to the team about your AI system.

Share what you are building, where you need help, and what needs to ship next. We will reply with the right next step.

NDA available

We can start under NDA when the work requires it.

Direct team access

You speak directly with the team doing the technical work.

Clear next step

We reply with a practical recommendation on scope, implementation, or rollout.

30m

working session

Direct

team access

Share the architecture, scope, and timeline so we can understand the work quickly.

Name

Work email

Phone

Budget

What are you building?

NDA availableDirect team accessClear next step

Secure Multi-Party Computation (MPC) Engineering

Collaborate on AI Without Compromising Data Sovereignty

Business Outcomes of MPC-Powered Collaboration

Unlock New Data Partnerships

Accelerate Model Performance

Ensure Regulatory & IP Compliance

Reduce Legal & Contractual Overhead

Gain Competitive Intelligence Securely

Future-Proof Your Data Strategy

Typical MPC Project Phases and Deliverables

Industry Applications for Secure MPC

Cross-Bank Fraud Detection Networks

Multi-Hospital Clinical Research

Supply Chain Risk Intelligence

Competitive Market Analytics

Secure AI Model Training for Sensitive Data

Privacy-Preserving Identity Verification

Secure Multi-Party Computation (MPC) Engineering

MPC Engineering: Frequently Asked Questions

What is your typical MPC engineering timeline from design to deployment?

How is pricing structured for MPC development projects?

What security standards and audits do you follow?

What happens after the MPC system is deployed?

What frameworks and technologies do you typically use?

Can you integrate MPC with our existing AI/ML pipelines?

What are the most common use cases you've built for enterprises?

How do you handle performance and scalability of MPC protocols?

Talk to the team about your AI system.