Blyss is an AI model provider built on strong security guarantees. We run AI workloads in GPU-based secure enclaves, which keep data encrypted even while it's being processed. Our customers get a privacy model similar to on-premises deployment, without ever having to think about AI infrastructure or GPU supply. The Blyss team has strong experience in cryptography and AI from Stanford, Apple, Nvidia, and more. We think data privacy is fundamental to safe, trustworthy AI, so it's our job to make end-to-end encrypted AI a no-compromise option - just like Signal and WhatsApp did for messaging.
Worked at Yubico. Wrote ia.cr/2022/368. Stanford CS '19 BS + MS.
I've spent my whole career making computers go fast, especially for deep learning. I love large scale compute and care deeply about democratizing this power. Previously, I worked on the Apple Neural Engine, and NVIDIA's cuDNN. Stanford '19, BS and MS in EE.
We are Samir and Neil, the founders of Blyss.
Privacy is a competitive advantage. There’s a billboard on the 101 explaining how WhatsApp can’t read your messages, millions of consumers use VPNs to secure their everyday browsing, and Apple doesn’t finish a keynote without touting new, frontier-pushing privacy practices.
But unless you’re at big tech, the most powerful new privacy tools are almost completely inaccessible. Chrome and Edge use homomorphic encryption to scan unsafe URLs and check passwords, but a startup wanting to offer similar features would need a team of PhDs to build them in-house.
The Blyss SDK offers developers a key-value store with unusually strong privacy guarantees. Create S3-like buckets, fill them with data, and then make cryptographically secure retrievals. No entity, not even the Blyss service itself, can learn which items are retrieved from a Blyss bucket.
Private retrieval is a simple but powerful new primitive. Using it,
People are building lots of new web apps with Blyss - try some of the best ones at the Blyss playground!
Try our SDK - it’s a self-serve, cloud storage service (feels just like S3), that transparently handles encryption and privacy for you. And if it isn’t quite clear how to fit privacy into your app, we’ll help you!
Tell us about apps/services that feel icky because they collect too much data. It may not be obvious how to fix them, but your gut feelings on privacy are super valuable to us.
If you know companies that are trying to make privacy their competitive advantage (password managers, VPNs, etc), introduce us (founders@blyss.dev)!
We met eight years ago on Day 1 of freshman year at Stanford; we’ve been best friends since. Neil was previously at Apple working on ML; Samir worked at Yubico on applied security, and before that did research on homomorphic encryption with Dan Boneh. We published the fastest-ever scheme for private retrieval at a top cryptography conference (Oakland). Now, we’re working to make advanced privacy technology available to everyone.