Vivendur is an enterprise AI control plane designed to help organizations use modern language models while maintaining control over sensitive context, access boundaries, and auditability.

Vivendur is built for security, compliance, and governance-conscious organizations, especially teams handling client work, regulated data, or high-value IP (e.g., legal, finance, consulting, biotech, and security-forward tech).

AI adoption is outpacing governance. Employees and internal copilots can unintentionally expose proprietary or regulated information to external model providers, and organizations lose visibility into what was shared, under what policy, and by whom.

Most tools focus on filtering prompts and outputs. Vivendur is designed to address the deeper issue: controlling AI context and “memory” as privileged data so teams can scale AI usage without creating an unmanaged data layer.

Vivendur is designed so that sensitive memory and context remain under customer control and governance. Data handling is policy-driven and can be configured to match your risk posture and deployment requirements.

Vivendur does not build or train foundation models. Our role is to govern how your organization uses models. Model-provider behavior varies; Vivendur is designed to support policy-based routing and modes that reduce or avoid disclosure of sensitive information.

Yes. Vivendur is built to enforce least-privilege context disclosure; sharing only what is necessary for the task, subject to policy.

“Stateless” means the model is treated as a compute engine, not a memory store. Vivendur is designed to separate intelligence (model inference) from memory (customer-controlled context), so organizations can govern and audit what’s used each time.

Vivendur supports compartmentalization, so organizations can separate work by client, project, team, or dataset and enforce boundaries between them.

Yes. Vivendur is designed to be provider-agnostic and to support policy-controlled model selection, so you can align provider choice to sensitivity, cost, performance, or internal requirements.

Yes. Vivendur supports policy modes that can restrict or prevent external processing for high-sensitivity contexts, depending on your configuration and deployment.

Yes. Vivendur is designed to generate an auditable record for AI interactions so security and compliance teams can answer: what was shared, which policies were applied, and who initiated the request.

A receipt is an audit artifact for an AI request, intended to provide traceability and accountability for enterprise governance.

Vivendur is designed with retention and deletion controls so organizations can align AI usage with internal governance, legal holds, and data minimization policies.

Vivendur is designed to integrate where work happens, starting with common enterprise touchpoints (e.g., browser-based usage and team collaboration environments). Exact integration options depend on your environment and pilot scope.

Most pilots are structured to move quickly: connect Vivendur to a limited set of workflows, define governance policies, and validate visibility and control in real usage.

Typical success criteria include: reduced shadow AI usage, improved audit visibility, clear policy enforcement, and adoption by target teams without slowing work.

Healthcare is on the roadmap after enterprise traction and compliance maturity. If you have a healthcare use case, we can discuss the right pilot boundaries and risk controls.

We can run an evaluation with synthetic data, constrained workflows, and conservative policy settings to validate governance and auditability first.

Request a demo or a pilot. We’ll align on your risk posture, target workflows, and what “good governance” means in your environment.