Encrypted context, memory, and vector DB for every model and agent
Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.
For SaaS embedding AI
An AI feature you can sell into your strictest customer.
The customer that asks the hardest security questions is also the largest deal. "We added AI" reads as risk to their security review until you can show what it cannot see.
Mirror under your AI features. The trust marker your customers' security team will accept.
Use cases for this audience
5 scenarios
Fine-tune on customer data without ever seeing it
Domain models need real customer data to be useful. Training in plaintext means you hold what you swore you wouldn't.
One endpoint for every model, with policy at the door
Each team hits its own provider its own way. Keys sprayed across services, prompts egressing in the clear, no single place that can say what was asked or whether it was allowed.
Pick the encryption posture per workload, not per gateway
Some routes need raw speed. Others need FHE all the way through. You shouldn't have to pick one for the whole org.
Stop your model from being cloned out the back door
Distillation attacks turn your API into someone else's competing model in weeks.
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See encrypted AI security in action.
FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.