The systems behind private intelligence

Where does plaintext exist? What gets retained? What can an infrastructure provider see? What changes when inference runs inside confidential hardware?

We research the boundaries behind the word private so they can be understood rather than assumed.

Diogenes Research compares open-weight and privacy-compatible models across capability, cost, speed, deployment options, and privacy characteristics.

Because the right model for a private workflow depends on more than how well it answers a benchmark.

Agents can read, research, browse, write, communicate, and eventually act. That makes permission architecture as important as intelligence.

We research how agents can become more useful without automatically gaining access to everything around them.

Persistent knowledge gives agents continuity. It also creates new questions around ownership, retrieval, provenance, portability, and what should be remembered in the first place.

Diogenes Research explores how long-term AI context can remain explicit, inspectable, and controlled by the people who created it.

Trusted execution environments and remote attestation can protect sensitive inference in ways ordinary cloud promises cannot. They also have limitations.

We research both sides: what confidential compute genuinely protects and what it does not.

Wallets can provide identity, authorization, payments, and pseudonymous access without recreating the traditional SaaS account stack.

We research where crypto meaningfully improves private AI, and where it simply adds complexity.