> VELUM_LABS
Zero-Trust
AI.
Train and run state-of-the-art neural networks directly on fully encrypted data—at plaintext speed and global scale.
[ ENCRYPTING... ]
ZERO_KNOWLEDGE • FULLY_HOMOMORPHIC
> 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.
> 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.
> 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
Ready to Experience
Privacy-First AI?
Seats in our private beta are limited. Secure yours today.
REQUEST_EARLY_ACCESS()