Features
Guides for executives, IT and data owners. Understand visible risks, then define your Private AI scope together.
Private knowledge
Organize knowledge approved for AI with source, version and owner. Internal documents are not automatically accessible to every employee.
Learn moreMulti-tenant isolation
Multi-tenancy shares a platform across organizations or clients while defining data and access boundaries. Separate screens or folders alone are insufficient.
Learn moreWorkspaces for each context
A workspace groups users, documents and agents for one department, client or project, making scope visible and reducing accidental context mixing.
Learn moreRBAC: the right access
RBAC assigns permissions by role, linked to workspaces, sources and agents. Hiding buttons is not access enforcement.
Learn moreTraceable source citations
Citations connect answers to documents, sections, pages and versions so users can check support for a conclusion. A filename alone does not guarantee correctness.
Learn moreAudit and governance
AI records should identify who did what, with which data and model, when, and who may review it. Balance useful evidence against sensitive information in logs.
Learn moreModel flexibility
Separate model connections from data and user experience to evaluate local LLMs, private endpoints or commercial APIs by policy and tests. Confirm each model before production use.
Learn moreAPIs and integrations
ERP, CRM, DMS and database connections need defined reads, writes, identities and inherited rights. A service name on a diagram is not a ready-made connector.
Learn moreYour data, your boundary
Data control covers originals, chunks, embeddings, indexes, caches, answers and logs. Know where each lives, who may access it and how to delete or restore it.
Learn moreAgent workflows
An AI agent uses a model to select steps and tools toward a goal. Action permissions matter beyond document access: a model proposing an action does not authorize the user to perform it.
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