Use case VectaX

Fine-tune on customer data without ever seeing it

Encrypted training and fine-tuning that never reveals the source records. A model your customers can audit but you can't extract.

Computes on data it can never see

Financial servicesHealthcare & pharmaEnterprise SaaSHead of AIEngineering leadSaaS embedding AIInference providers

The problem

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

To get a useful domain model you train on real customer data, and the instant you do, you hold exactly what you swore you never would.

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

How it works

01

Seal the set

Training data is encrypted before it touches the pipeline. VectaX never materialises the source records.
02

Train blind

Fine-tuning runs on ciphertext. The model learns from the data without the data ever being readable.
03

Ship auditable

You ship a domain model customers can verify was trained on their data, and that you can't extract back out of it.

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

Distillation attacks turn your API into someone else's competing model in weeks.

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.