Bring AI to the data you can't move
Your most regulated records sit on the wrong side of the AI line because every model has to decrypt to compute.
Encrypted context, memory, and vector DB for every model and agent
Context stays sealed end-to-end. Retrieval, embeddings, RAG, long-running memory. The model receives encrypted context, never your source material.
Computes on data it can never see
Financial servicesHealthcare & pharmaEnterprise SaaSEngineering leadHead of AIAgent buildersSaaS embedding AI
The problem
Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.
Every copilot and agent you ship turns your context into plaintext the moment it's loaded. One prompt-leak from exposure, and the more useful the model gets, the more of your data sits at runtime.
Mirror restores Encryption · FHE · computes on data it can never see
How it works
What changes once Mirror is in the loop.
- 01
Seal at rest
Vector stores, knowledge bases, conversation history, and agent memory are sealed by VectaX. The substrate is never plaintext, whatever the retrieval pattern.
- 02
Serve sealed
Retrieval, ranking, embedding similarity, RAG, and tool-call context all run over ciphertext. The model and the runtime receive encrypted context, not source material.
- 03
Answer
The response is decrypted only by the caller who holds the key. No component in the loop ever sees what fed the answer.
Get started
See encrypted AI security in action.
FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.