Cornami for Generative AI & LLMs (Plaintext & Encrypted)
LLMs are transforming digital workflows—from AI assistants and code generation to enterprise search and document intelligence. Whether operating in open environments or handling sensitive data, these models demand ultra-fast, secure, and scalable compute. Cornami accelerates both plaintext and encrypted LLMs, enabling real-time inference and privacy-preserving AI at scale.
AI at Speed—With or Without Encryption:
- Ultra-low latency for inference, fine-tuning, and RAG
- Encrypted LLM execution using Fully Homomorphic Encryption (FHE)
- Privacy-preserving AI for finance, healthcare, legal, and defense
- Supports edge, cloud, and hybrid deployments—no OS or cloud-layer risks
- Built for post-quantum secure AI pipelines and compliant ML workflows
Key Challenges
- Centralized AI infrastructure adds cost, latency, and risk
- Sensitive prompts create compliance and data leakage concerns
- Privacy-preserving ML is too slow for real-time use today
- Most stacks lack zero-trust, post-quantum-secure execution
The Cornami Advantage
Cornami delivers deterministic, secure, and high-performance compute for LLMs—no matter the environment or data sensitivity.
Use Cases
LLM Acceleration: Cut costs and latency for GenAI workloads
AI Agents: Power real-time copilots and assistants
Private LLM Inference: Run encrypted prompts and documents securely
PPML at Scale: Enable federated learning and compliant AI deployment