A world where data never has to be exposed to be useful. Not even to AI.
AI made data more powerful, and more exposed than ever. We're building the privacy-preserving layer where intelligence and confidentiality no longer trade off.
To use AI, you've had to give your data away.
Generative AI exploded the value of the data enterprises hold. But to unlock that value, you've had to hand plaintext to the model: as a prompt, as a retrieved document, as fine-tuning data. The most sensitive information flows out through the plaintext gap. The faster AI moves, the wider that exposure grows.
“That trade shouldn't exist.”
The more data AI touches, the more there is to lose.
Shadow AI & prompt leakage
Employees paste sensitive data into external LLMs, and prompts and outputs linger inside third-party models.
RAG & knowledge exposure
The moment internal documents, contracts, and records are wired into retrieval, plaintext is exposed to the model and the infrastructure around it.
Autonomous agents
Agents act over sensitive data on their own, widening the attack surface and the odds of an incident.
Regulation vs. AI
Healthcare, financial, and government data often can't go near AI at all. So the most valuable data becomes the least usable.
“Your most valuable data is your most exposed.”
Sensitive data should never need to be exposed. Not even to AI.
Not a wall built around encrypted data, but a layer that lets data stay encrypted while it is used. Data is never decrypted, whether in transit, at rest, or in use.
“You no longer have to choose between intelligence and confidentiality.”
The data cloud redefined where data lives. We're redefining how it's used with AI.
A privacy-preserving layer beneath all of AI, analytics, and collaboration. Invisible, but essential.
AI without exposure
Collaboration without surrender
Personalization without surveillance
For 30 years, homomorphic encryption proved it could work. We proved it could be used.
General-purpose FHE solved the function with an algorithm, and it works brilliantly in papers and on GPU clusters. But a proven function that needs a GPU cluster reaches no one. The last gap was never math. It was usability. We closed it at a different layer: not just an algorithm, but an algorithm and a protocol. So the operations enterprises actually run work on standard CPUs, in air-gapped networks, today. Thirteen years of elliptic-curve mathematics, now running in production, not lab demos.
Shaped through joint research and pilots with the Bank of Korea's CBDC pilot, LG CNS, Koscom, Seoul National University Hospital, and the Hedera and Linux Foundation communities.
Built to stay safe through the AI era and the quantum one.
The same protocol-first thinking changes the quantum question too. Instead of betting everything on a heavier algorithm, we remove the keys from the exposed boundary entirely. We call it a No-Key architecture. At the server boundary where real breaches happen, confidentiality holds information-theoretically, against any adversary, quantum included.
“Built to stay safe through the AI era and the quantum one.”