Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Scalable, low-latency AI endpoints that process user data without storing or exposing it.
Consumer-facing AI applications demand trust. We architect and deploy inference systems that apply secure enclaves and on-premise deployment strategies, ensuring sensitive data is processed but never persisted. This is critical for compliance with GDPR, CCPA, and emerging regulations like the EU AI Act.
Process user data in memory, not in logs. Achieve 99.9% uptime with sub-100ms latency while eliminating data leakage vectors.
confidential computing and secure multi-party computation for cross-enterprise collaborations.Move beyond basic API wrappers. Our approach integrates directly with your existing Retrieval-Augmented Generation (RAG) systems and multimodal data pipelines, ensuring privacy is a foundational layer, not an afterthought. Explore our broader capabilities in Confidential Computing for AI Workloads and Federated Learning Systems Engineering.
Our privacy-preserving AI inference services deliver concrete, measurable business results—from accelerated product launches to guaranteed compliance—without compromising on performance or security.
Deploy production-ready, privacy-compliant inference endpoints in under 2 weeks using our battle-tested architectures for secure enclaves and on-premise deployment, eliminating months of custom engineering.
Achieve and demonstrate compliance with GDPR, CCPA, and the EU AI Act through auditable privacy-preserving techniques like homomorphic encryption and differential privacy, backed by verifiable technical documentation.
Lower total cost of ownership by processing sensitive data in secure, low-latency inference endpoints without expensive data duplication or complex legal agreements for data sharing and storage.
Build user confidence and increase product adoption by publicly committing to privacy-by-design. Process user data without storing or exposing it, a critical differentiator for consumer-facing applications.
Deliver sub-100ms inference latency even with advanced cryptographic privacy layers, ensuring your application's user experience remains seamless and responsive under load.
A clear breakdown of the phased delivery for our privacy-preserving AI inference services, from initial architecture to production deployment and ongoing support.
| Phase & Deliverables | Timeline | Key Outcomes |
|---|---|---|
Phase 1: Architecture & Threat Modeling | 1-2 weeks | Detailed technical design document, threat model, and compliance gap analysis for regulations like GDPR. |
Phase 2: Core Infrastructure Setup | 2-3 weeks | Deployed secure enclaves or on-premise inference endpoints, integrated with your data sources. |
Phase 3: Model Integration & Optimization | 2-4 weeks | Your production AI model running with privacy-preserving techniques (e.g., FHE, TEEs), achieving target latency. |
Phase 4: Security Audit & Penetration Testing | 1 week | Third-party audit report and remediation of identified vulnerabilities in the inference pipeline. |
Phase 5: Staging Deployment & Load Testing | 1-2 weeks | Validated system performance under load, final SLA documentation, and team training. |
Phase 6: Production Go-Live & Monitoring | Ongoing | Live system with 99.9% uptime SLA, real-time monitoring dashboard, and incident response playbook. |
Ongoing Support & Evolution | Monthly/Quarterly | Optional retainer for model updates, scaling infrastructure, and adapting to new privacy regulations. |
Our privacy-preserving AI inference services are engineered for industries where data sensitivity and regulatory compliance are non-negotiable. We deploy secure, low-latency architectures that process data without storing or exposing it, enabling innovation without risk.
Deploy HIPAA-compliant AI for real-time diagnostic support and clinical decision systems. Process patient data via secure enclaves or on-premise endpoints, ensuring PHI never leaves your controlled environment while enabling faster, more accurate insights.
Learn more about our approach to Healthcare Clinical Decision Support and Ambient AI.
Implement real-time fraud detection and algorithmic risk modeling without centralizing sensitive transaction data. Our architectures use secure multi-party computation and homomorphic encryption to protect PII and financial records during AI inference.
Explore our work in Financial Services Algorithmic AI and Risk Modeling.
Build robust, air-gapped AI systems for secure communications and geospatial intelligence analysis in contested environments. We specialize in sovereign AI infrastructure and confidential computing to protect classified data during processing.
See our capabilities for Defense and National Intelligence AI.
Automate contract analysis and predictive litigation workflows while preserving attorney-client privilege. Our privacy-preserving NLP models enable document review and compliance checking without exposing sensitive case data to third-party clouds.
Discover our solutions for Legal and Compliance Workflow Automation.
Enable hyper-personalized customer experiences and real-time inventory management without aggregating consumer PII. Process behavioral data at the edge or via encrypted inference to drive revenue while maintaining CCPA/GDPR compliance.
Understand our applications in Retail and E-Commerce Hyper-Personalization.
Accelerate drug discovery and genomic analysis using privacy-preserving AI. Our solutions enable secure collaboration on sensitive research data across institutions via federated learning and synthetic data generation, protecting intellectual property and patient privacy.
Learn about our Bio-AI and Generative Biology Solutions.
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 clear answers on how we deploy secure, low-latency inference endpoints that protect your most sensitive data.
Standard deployments take 2-4 weeks from kickoff to production-ready API. This includes architecture design, integration of privacy-enhancing technologies like secure enclaves or homomorphic encryption libraries, and performance benchmarking. Complex multi-party computation setups may extend to 6-8 weeks. We provide a detailed project plan during the initial technical 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.
