> VELUM_LABS
AI Without
Exposure.
Train and run state-of-the-art neural networks directly on fully encrypted data—at plaintext speed and global scale.
[ ENCRYPTED_COMPUTE ]
> VALUE_PROPOSITION
Three-Line Elevator Pitch
Zero-Trust by Design
Our fully homomorphic encryption (FHE) stack keeps every byte of data encrypted end-to-end, eliminating the need for secure enclaves or masking tricks.
Plaintext Performance
Novel symmetry-aware kernels execute encrypted linear algebra up to 1000× faster than existing FHE libraries.
Ready for the Real World
A drop-in PyTorch/JAX backend that scales from a single GPU to multi-node clusters, with no model rewrites required.
> THE_BOTTLENECK
The Problem
Banks, hospitals, and defense agencies hold petabytes of sensitive data they can't freely use. Traditional FHE is 10³–10⁶ × slower, so “privacy-preserving AI” never leaves the lab.
> OUR_BREAKTHROUGH
Our Solution
Velum Labs combines high-dimensional group symmetries with an optimized FHE scheme to slash ciphertext noise growth and cut compute overhead to near-plaintext latency. Private training and inference become practical for models with billions of parameters.
> HOW_IT_WORKS
Three Simple Steps
Encrypt
Data owners encrypt datasets locally with our open-source CLI; keys never leave their premises.
Compute
Our runtime executes training or inference entirely in ciphertext on commodity GPUs/TPUs.
Decrypt
Only the rightful key-holder can reveal final predictions or model weights—auditable, mathematically provable privacy.
> CORE_FEATURES
Built for Scale
• FHE-Native Autograd
Automatic differentiation on encrypted tensors.
• Equivariant Primitives
Rotation-invariant layers that minimize multiplicative depth.
• Dynamic Noise Budgeting
Adaptive relinearization keeps accuracy high over long training horizons.
• Cloud or On-Prem
Deploy in your VPC, on Velum Cloud, or on air-gapped clusters.
> USE_CASE_SNAPSHOTS
Real-World Applications
Anti-money-laundering models trained on encrypted transaction graphs across multiple banks.
Federated drug-discovery pipelines that combine proprietary assay data without revealing IP.
Secure language models for classified document analysis with no de-classification risk.
> WHY_VELUM_LABS
Velum vs. Legacy FHE
Velum Labs
• GPU-accelerated tensor cores
• Supports transformers & GNNs
• Provably post-quantum secure
• Drop-in PyTorch / JAX
Legacy FHE Toolkits
• CPU-bound bigint arithmetic
• Small MLPs only
• No post-quantum guarantees
• Custom DSLs, steep learning curve
> FOUNDERS
Meet the Team
Benjamin
Cryptography Lead
Stanford Physics & Math. Author of our post-quantum security proofs.
Alen
CTO
Minerva Math & CS. Built geometric ML systems at Vanderbilt and TetraMem.
“We've built together before—shipping cancer-detection AI adopted by Samsung. Now we're bringing privacy-first AI to every industry.”
Ready to Experience
Privacy-First AI?
Seats in our private beta are limited. Secure yours today.
BOOK_TECHNICAL_DEMO()