Yozi Systems
A founder-led systems design and technology company focused on cross-domain problem solving through original architecture, mathematical reasoning, and practical engineering.
Yozi Systems explores difficult problems across disciplines and develops original systems architectures that challenge conventional assumptions. InvarOS is the first flagship platform emerging from that philosophy.
Governance is infrastructure, not software.
The prevailing approach to AI security treats governance as an application-layer concern — a proxy in front of the model, a filter on the prompt, or a dashboard aggregating post-hoc logs. These approaches share a fundamental flaw: they operate on a voluntary basis. An autonomous agent is not obligated to route its traffic through your proxy.
InvarOS starts from the physical layer. The native invarosd daemon runs directly on host network interfaces and system bridges. Topology evidence is gathered at the node level. Cryptographic receipts are generated locally. Air-gapped operation is not a feature — it is an architectural prerequisite.
Mathematical verification is not a marketing claim. InvarOS is built upon proprietary mathematical methods for deterministic AI governance, verification, and capability analysis. These methods enforce boundary conditions as topological invariants. When the mathematics says a transition is forbidden, that constraint is absolute — not probabilistic, not heuristic, not advisory.
Per-seat licensing and cloud-dependency are structural mismatches for the problem InvarOS addresses. Governance infrastructure should be priced accordingly — per-core, per-device, or per-sovereign deployment.
Deterministic over probabilistic
Mathematical guarantees, not likelihood scores. A topological invariant cannot be bypassed by rephrasing.
Infrastructure, not application
Governance operates beneath agent systems, not above them. The substrate is the enforcement layer.
Air-gap by design
Every design decision is tested against the question: does this work if the network never comes back up?
Evidence, not assertion
Cryptographic receipts, TBoM artifacts, and CBOM attestations — not compliance checkboxes.
Honest about current state
This site is a public soft launch. Downloads are coming. The engineering is real. We say precisely what is built and what is not.
Proprietary core. Open standards.
The InvarOS platform uses a layered IP boundary strategy that separates open interoperability from proprietary enforcement.
Open / Public Benefit Layer
- TBoM v3 JSON schemas
- Commitment schemas (intent, consent, refusal, settlement, envelope)
- CycloneDX 1.6 CBOM data structures
- in-toto statement schema definitions
- ZK compliance claim schema
- Federation recognition schema
- Graph invariant validation algorithms (atgs-validator)
- Basic MCP server wrappers
Proprietary / Confidential Layer
- C++20 mathematical verification solver (Eigen-based)
- Proprietary graph topology analysis engine
- Cycle analysis and entropy verification core
- Transient distribution solver
- C ABI isolation layer for encrypted plugin distribution
- Temporal Micro-Chain native implementation
- Hardware attestation binding (TPM interface — roadmap)
The mathematical core solves governance problems that cannot be expressed as classification tasks. Given a representation of an AI agent's permitted state transitions, the solver verifies whether the agent's observed trajectory satisfies the required topological invariants — the same invariants encoded in the governance policy.
This is not anomaly detection. It is structural verification. The difference is the difference between a security guard who matches faces to a watchlist and a border that can only be crossed through a gate whose geometry makes other paths physically impossible.
AI governance infrastructure as a public good.
InvarOS's commitment to public benefit licensing is not a marketing gesture. AI safety research institutions and independent researchers that build on open schemas and cryptographic receipt formats contribute to establishing shared measurement and verification standards — an outcome that benefits the field regardless of who builds the tooling.
Eligible academic institutions, non-profit AI safety laboratories, and independent researchers conducting non-commercial AI safety research may receive approved access under the InvarOS Public Benefit License.
We do not see open-core and commercial success as contradictory. The mathematical solvers, hardware attestation plugins, and enterprise control plane that require significant ongoing engineering investment are the commercial layer. The schemas, protocols, and measurement formats that benefit from wide adoption are the open layer.
Enterprise licensing combines an annual platform licence with deployment-specific runtime capacity where appropriate. Expansion pricing such as additional edge appliances or additional enterprise compute capacity is determined by deployment requirements.
Local and sovereign by design.
InvarOS is built on the premise that the organisations with the most demanding AI governance requirements — defence agencies, intelligence services, critical infrastructure operators, sovereign wealth funds — cannot compromise on local operation.
Every architectural decision in InvarOS has been tested against the hard constraint of complete network isolation. Topology discovery works offline. Receipt generation works offline. Policy compilation works offline. The air-gapped federation model works offline. The enterprise platform does not phone home.
We believe that AI governance infrastructure for sovereign and public-sector deployments must be auditable by the deploying organisation. This means transparent mathematical foundations, open schemas, and a clear separation between what is proprietary (the mathematical solver implementation) and what must be publicly verifiable (the governance schema definitions and verification protocols).
Work with us.
Pilot design, consulting engagements, research partnerships, and strategic discussions are all available now.