Use case CodePrism

Let AI review code that never leaves your machine

FHE code review where keys stay local, the model reasons on ciphertext, and decryption only ever happens on the developer's machine.

Computes on data it can never see

Financial servicesDefence & intelligenceHealthcare & pharmaEnterprise SaaSEngineering leadDeveloper tooling

The problem

To use an AI code tool you usually hand it your source, secrets, and proprietary logic. And trust a retention promise.

To use an AI code reviewer you hand over your source, secrets, and proprietary logic, and trust a retention promise you can't verify.

Mirror restores Encryption · FHE · computes on data it can never see

How it works

01

Encrypt locally

Your code is sealed on the developer's machine. Keys never leave it.
02

Review blind

CodePrism's model reasons over the ciphertext. and analyses the code without ever reading it.
03

Decrypt local

Findings decrypt only back on your machine. Your source never existed in the clear on anyone's server.

Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.

Domain models need real customer data to be useful. Training in plaintext means you hold what you swore you wouldn't.

Your most regulated records sit on the wrong side of the AI line because every model has to decrypt to compute.

Get started

FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.