Ask your corpus. Get a cited answer.
Multi-turn grounded chat across your documents. Anchored to retrieved context, the model says "not found" rather than inventing an answer.
AI chat that stays inside what you know.
Grounded answers only
Every answer is anchored to context retrieved from your corpus. The model says "not found in corpus" rather than fabricate. It cannot hallucinate a regulation because there is nothing else for it to draw from.
Tenant-isolated threads
Thread history stays scoped to your tenant. No cross-customer data leakage is architecturally possible. A thread cannot reference a document from a different workspace even if the model is prompted to try.
Role-scoped system prompts
System prompts and available tools are configured per role, not per user. A finance analyst sees finance context; a legal reviewer sees legal documents. The model cannot cross the boundary regardless of what the user types.
Every conversation is accountable.
Inference audit
Every turn is logged: prompt, retrieved context, model and template version, full completion. Append-only with cryptographic chaining.
Model governance
Model version pinned per tenant. Version changes require an explicit admin decision and are logged with rationale. Rollback is a single operation.
Tenancy
Thread data never crosses tenants. RLS enforced at the database engine, not the application layer. A bug in the app cannot cause cross-tenant leakage.
See it answer a question from your documents.
We run a scoped pilot on your corpus. 45 minutes, no slide decks.