Overview
Alkahest explores private LLM inference with end-to-end encryption and trusted execution environment infrastructure.
How It Works
- The product direction centers private model interaction rather than generic hosted chat.
- The infrastructure direction uses trusted execution environments as the privacy boundary.
- The public footprint includes the site and open source organization for the project.
Hard Parts
The challenge is making the privacy model legible: users should understand what is protected, what infrastructure is trusted, and where the boundaries are.
Results
Alkahest remains a useful portfolio signal for privacy-oriented AI infrastructure and trusted execution work, especially alongside Mystery Gift and BS-Bench.
