O
About
Opaque Systems is a confidential AI platform designed to help enterprises innovate fearlessly with their most sensitive data. At its core, Opaque leverages confidential computing to create a secure environment where AI workloads—including agentic Retrieval-Augmented Generation (RAG), AI guardrails, and analytics pipelines—can operate without exposing raw data to external systems or even cloud providers. The platform is purpose-built for organizations in regulated industries such as finance, insurance, telecommunications, and healthcare, where data privacy and compliance are non-negotiable. Opaque allows teams to connect disparate data sources, apply granular policies, and run confidential AI workflows end-to-end, all while retaining full control over their data. Key capabilities include confidential agentic RAG for secure knowledge retrieval, enforcement of AI guardrails to maintain compliance, and multi-party data collaboration without data exposure. The platform integrates easily into existing enterprise environments and is designed for fast onboarding—teams can get started within minutes. Opaque has demonstrated real-world impact through partnerships with ServiceNow (reducing AI task timelines from days to seconds), Accenture (enabling cross-industry secure data sharing), and RiskStream (enhancing insurance fraud detection). The platform also offers a content hub, documentation, webinars, and an AI Confidential Podcast to support practitioners and decision-makers exploring confidential AI adoption.
Key Features
- Confidential Agentic RAG: Run Retrieval-Augmented Generation pipelines on sensitive enterprise data within a confidential computing environment, ensuring data is never exposed during AI inference.
- AI Guardrails Enforcement: Apply and enforce customizable AI policy guardrails to ensure outputs remain compliant with regulatory and organizational requirements.
- Confidential Computing Foundation: Leverages hardware-level trusted execution environments (TEEs) to protect data in use, preventing exposure to cloud providers, vendors, or internal unauthorized parties.
- Multi-Party Data Collaboration: Enables secure data sharing and joint analysis across organizations or departments without any party exposing their raw data to others.
- Enterprise-Ready Integration: Connects to existing enterprise data sources and infrastructure with fast onboarding, allowing teams to deploy confidential AI workloads in minutes.
Use Cases
- A financial services firm runs confidential AI models on customer transaction data to detect fraud without exposing personally identifiable information to the cloud provider.
- An insurance consortium uses Opaque to enable multi-party data sharing for fraud detection analytics across member organizations without any party revealing their raw data.
- A healthcare organization deploys agentic RAG on confidential patient records to power AI-driven clinical decision support while maintaining HIPAA compliance.
- A telecommunications enterprise partners with Opaque to apply AI guardrails and privacy controls across its AI pipelines, ensuring regulatory compliance in every market it operates.
- An enterprise software company integrates Opaque to let customers run AI workloads on sensitive business data without losing control or violating data residency requirements.
Pros
- True Data Privacy in AI Workloads: Confidential computing ensures sensitive data is never exposed during AI processing, making it suitable for the most regulated industries.
- Proven Enterprise Adoption: Real-world deployments with ServiceNow, Accenture, and RiskStream validate the platform's scalability and compliance capabilities.
- Comprehensive AI Trust Layer: Combines data privacy, policy enforcement, and secure agentic workflows in a single platform, reducing the complexity of enterprise AI governance.
Cons
- Enterprise-Focused Pricing: The platform targets large organizations and requires a demo request, making it inaccessible or cost-prohibitive for smaller teams or individual developers.
- Steep Learning Curve for Confidential Computing: Teams unfamiliar with confidential computing concepts and hardware-based TEEs may require additional onboarding time and expertise to fully leverage the platform.
Frequently Asked Questions
What is confidential computing and why does Opaque use it?
Confidential computing uses hardware-based trusted execution environments (TEEs) to protect data while it is being processed. Opaque uses this technology so that AI workloads can run on sensitive data without exposing it to cloud providers, vendors, or unauthorized parties—even during active computation.
What types of AI workloads can run on the Opaque platform?
Opaque supports confidential agentic RAG (Retrieval-Augmented Generation), AI guardrail enforcement, analytics pipelines, and multi-party data collaboration workloads—all executed within a secure, privacy-preserving environment.
Which industries is Opaque best suited for?
Opaque is designed for regulated industries including financial services, insurance, telecommunications, healthcare, and any sector where data privacy, compliance, and governance are critical requirements for AI adoption.
How does Opaque differ from standard AI platforms?
Unlike standard AI platforms that require data to be shared or transferred in plaintext, Opaque ensures data remains protected even during AI model inference and analysis, acting as a trust layer that enables organizations to use their most sensitive datasets without risk.
How do I get started with Opaque?
Organizations can request a demo via the Opaque website. The platform is designed for fast onboarding, and Opaque's team works with enterprise clients to connect data sources, configure policies, and deploy confidential AI workloads.
