Zohal

Zohal provides a private AI chat interface where users can upload and query sensitive documents — PDFs in particular — without the session persisting beyond the interaction. The core workflow, as described on the vendor page, starts by generating a secure temporary session before any content is loaded, isolating each interaction from shared infrastructure. From there, users chat against their documents and receive AI-generated responses scoped to that session’s content.

The differentiating claim is the ephemeral session model: rather than storing uploaded documents or query history in a persistent workspace tied to a shared environment, Zohal creates a sandboxed context per session. For teams handling documents under NDA, attorney-client privilege, or HR confidentiality requirements, this architecture matters more than feature count — the question is not what the tool can do but what it does with the data afterward.

Zohal is best positioned for teams that need audit-ready evidence of document handling practices, or for individuals who regularly work with PDFs they cannot send through a general-purpose AI assistant. Where it strains is at the edge of organizational scale: the vendor page references team-based workflows, but architectural specifics about role-based access, admin controls, and integration with enterprise identity providers are not surfaced in available documentation. Teams inside a larger procurement process will hit a due-diligence gap before they can sign off on deployment.

The freemium entry point lowers the barrier to test the interface against a real document, but features required for team workflows and audit logging are paid-only features. The self-hosted option does not exist, meaning data residency is determined entirely by Zohal’s infrastructure choices — a blocking issue for organizations under regional data sovereignty rules.