Tinfoil

Tinfoil makes it easy to make your AI workloads provably private, without changing your code. You get the privacy of on-prem deployments, while running on the cloud.

We’ve built a full-stack platform on top of the latest NVIDIA GPUs offering confidential computing capabilities, meaning that you don’t have to tradeoff performance for privacy.

Under the hood, we integrate a suite of recent advances in secure hardware technologies, in particular NVIDIA’s confidential compute mode available on Hopper and Blackwell. When combined with Tinfoil’s software stack, companies can prove their security claims rather than relying on unenforceable statements like "trust us, we don’t log your queries.” Tinfoil guarantees that all data always stays private and cannot be accessed by anyone other than the end user : not even by Tinfoil or the cloud provider it’s processed on.

The founding team combines deep academic and industry experience in security and internet protocols. Tanya was previously a systems engineer at Cloudflare, where she built Internet security protocols used by billions, and contributed to the Workers AI platform. Jules and Sacha recently completed their PhDs at MIT where they worked on secure hardware and privacy-preserving technologies. Jules has also worked at NVIDIA on their confidential computing team. Nate has deep infrastructure experience, starting in high school when he built a mini-CDN that got him an internship at Cloudflare. Tinfoil was born from our personal frustration with the false choice between access to powerful AI and data privacy.

As AI becomes tightly integrated into our lives and business workflows, it will touch an ever increasing amount of sensitive personal and proprietary data. Right now, the only solutions are DPAs, band-aids like PII redaction, or the nuclear option of running everything on-prem. These solutions are not scalable. By making it easy to deploy provable privacy, Tinfoil promises to unlock significantly deeper AI adoption and integrations, just as TLS on the Internet enabled e-commerce to flourish by securing credit cards on the network.

If OpenAI is creating “God in a box”, we are putting God in a blackbox, so Satan can’t spy.

High-Level Overview

Tinfoil is a company that builds a confidential computing platform designed to make AI workflows secure, verifiable, and private. Their technology enables privacy-preserving AI inference and web analytics by running AI models inside secure hardware enclaves, ensuring that sensitive data and user queries remain confidential throughout processing. This allows enterprises, startups, and developers—especially those handling sensitive or regulated data—to leverage powerful AI capabilities without compromising privacy or compliance. Tinfoil’s platform offers provable zero data access and retention, balancing robust security with cloud-scale performance and ease of integration into existing AI workflows[1][3][4].

Origin Story

Founded by a team with deep expertise in cryptography, security, and infrastructure, Tinfoil’s founding members include Tanya Verma (ex-Cloudflare engineer), Jules Drean (PhD in secure hardware from MIT), and Sacha Servan-Schreiber (PhD in cryptography from MIT). The idea emerged from their frustration with inadequate privacy solutions like PII redaction and legal contracts, which failed to provide real security guarantees for AI data. They leveraged recent advances in confidential computing, especially NVIDIA’s secure GPU enclaves, to build a platform that guarantees zero data access and retention without sacrificing performance. This approach addresses enterprise and government security concerns that often stall AI adoption[4][5].

Core Differentiators

• Provable Privacy: Uses hardware-based secure enclaves and cryptographic techniques to ensure zero data access and zero data retention, verifiable directly by users.
• Seamless Integration: Compatible with open-source and custom AI models, offering a drop-in OpenAI-compatible API for private AI inference.
• Performance: Negligible overhead despite strong security guarantees, enabling cloud-scale AI workloads with privacy.
• Developer & Enterprise Focus: Provides tiered pricing models for individuals, startups, and enterprises, supporting compliance, data sovereignty, and zero-trust security at scale.
• Expert Team & Advisors: Founders with PhDs from MIT and industry experience at Cloudflare, Microsoft Research, and NVIDIA; backed by notable investors including Paul Graham and industry leaders in cryptography and AI[1][3][4][5].

Role in the Broader Tech Landscape

Tinfoil rides the growing trend of confidential computing and privacy-preserving AI, which is critical as AI adoption expands into sensitive sectors like healthcare, finance, and government. The timing is crucial because traditional cloud AI services require trust in providers that may expose sensitive data, creating barriers to enterprise adoption. By guaranteeing privacy through hardware enclaves, Tinfoil enables broader AI use cases that were previously stalled by security and compliance concerns. This innovation parallels how TLS encryption enabled secure e-commerce, potentially unlocking a new wave of AI applications that handle private data confidently. Their work influences the ecosystem by setting new standards for AI data privacy and compliance, encouraging adoption of zero-trust architectures in AI workflows[1][3][4].

Quick Take & Future Outlook

Looking ahead, Tinfoil is well-positioned to capitalize on increasing regulatory scrutiny and enterprise demand for secure AI solutions. As confidential computing hardware becomes more widespread and AI models grow in complexity and sensitivity, Tinfoil’s platform could become a foundational technology for private AI across industries. Trends such as data sovereignty, zero-trust security, and privacy-preserving machine learning will shape their growth trajectory. Their influence may expand beyond AI inference to broader confidential computing applications, further embedding privacy as a core principle in cloud and edge computing. Ultimately, Tinfoil aims to make private AI as ubiquitous and trustworthy as encrypted internet communications, unlocking new possibilities for innovation without compromising user trust[4].