RedPill

Product Introduction

1. RedPill is a privacy-first AI platform that executes all AI workloads within secure hardware enclaves (Trusted Execution Environments) to ensure end-to-end data encryption and verifiable privacy. It provides cryptographic proofs for every Large Language Model (LLM) query, enabling users to audit data handling without relying on blind trust. The platform supports seamless integration via a simple SDK/API, allowing developers to deploy confidential AI solutions across 200+ models, including GPT-5, Claude 4, and Gemini 2.5 Pro.
2. The core value of RedPill lies in its ability to guarantee zero data retention, provable privacy, and compliance with stringent data sovereignty requirements. By leveraging hardware-backed security and open-source transparency, it eliminates risks associated with third-party data exposure while maintaining cloud-native scalability and cost efficiency.

Main Features

1. RedPill ensures all AI computations occur in hardware-enforced Trusted Execution Environments (TEEs), isolating sensitive data from even cloud providers or internal administrators. This architecture prevents unauthorized access or memory leaks during model inference, fine-tuning, or RAG operations.
2. Every LLM query generates cryptographic attestation proofs, which users can independently verify to confirm that data remained encrypted and unmodified throughout processing. These proofs are anchored to secure hardware signatures, providing auditable compliance for regulated industries.
3. The platform offers unified API access to 200+ public and private AI models, including GPU-accelerated TEE variants of GPT-5, DeepSeek V3, and Qwen2.5-VL, with per-token pricing starting at $0.04/M input. Developers can deploy models on-premises or in-cloud while maintaining subsecond latency and 128K+ token context windows.

Problems Solved

1. RedPill addresses the critical challenge of maintaining data privacy in AI workflows, where traditional cloud APIs expose sensitive inputs/outputs to vendor logging or third-party breaches. It eliminates compliance gaps for industries like healthcare, finance, and legal services that handle PII, PHI, or proprietary data.
2. The product serves enterprises requiring GDPR/HIPAA-compliant AI, developers building confidential applications, and individuals seeking private AI interactions. Use cases include secure document analysis, encrypted customer support chatbots, and confidential RAG pipelines for internal knowledge bases.
3. Typical scenarios involve processing medical records through TEE-hosted models without HIPAA violations, executing financial predictions on encrypted market data, and deploying on-prem AI copilots that retain zero conversation history.

Unique Advantages

1. Unlike conventional AI APIs that rely on contractual data promises, RedPill provides mathematically verifiable privacy through hardware-rooted cryptographic proofs and open-source auditability. Competitors lack equivalent attestation mechanisms or TEE-optimized model deployments.
2. The platform uniquely combines enterprise-grade security with cloud convenience, offering 163K-token context windows in TEEs and per-request cost tracking. Its MXFP4-quantized models like GPT OSS 120B achieve near-native performance while running entirely within enclaves.
3. Competitive differentiators include Phala Network’s GPU-TEE infrastructure for scalable confidential computing, cross-model privacy routing, and compliance tooling for export-controlled data workflows. RedPill reduces AI deployment costs by 60% compared to traditional on-prem solutions.

Frequently Asked Questions (FAQ)

1. How does Confidential AI ensure my data is truly private? RedPill uses hardware TEEs to encrypt data during processing and provides cryptographic proofs for each query, ensuring no third party (including RedPill) can access raw inputs/outputs. All models run in memory-isolated environments with automatic data shredding post-execution.
2. Is there any performance impact compared to regular AI APIs? TEE-optimized models like GPT OSS 20B achieve <500ms latency through MXFP4 quantization and MoE architectures, matching standard cloud API speeds. Benchmarks show <15% throughput difference versus non-TEE deployments for most workloads.
3. How difficult is it to integrate Confidential AI into my existing application? Developers can migrate in under 10 lines of code using RedPill’s OpenAI-compatible API, which supports drop-in replacement for endpoints like /chat/completions. The SDK provides prebuilt attestation validators and Kubernetes operators for on-prem clusters.
4. What models are available through Confidential AI? The platform hosts 200+ models, including TEE variants of GPT-5, Claude 4, Qwen2.5-VL (128K context), and Google Gemma 3 27B. All models are open-source or provider-authorized, with dynamic routing based on privacy/performance requirements.
5. How can I verify that my data was actually protected? Each API response includes a SGX/TPM-signed attestation report detailing the TEE environment, model hash, and data-handling policies. Users validate these proofs locally using RedPill’s verifier library or third-party tools like Amber.
6. What are the pricing differences compared to standard AI APIs? TEE execution adds a 5-20% premium over base model costs (e.g., GPT-5 at $1.25/M input tokens), but eliminates data residency/compliance expenses. Volume discounts apply for >10M monthly tokens, with custom pricing for on-prem deployments.