Knowledge, rules and approvals
Control
The agent answers from what you gave it, does only what you allowed, and asks before anything that costs money.
Governance that is not a prompt
Rules enforced in code, not prompts
A prompt instruction is a suggestion. Rules live outside the model at three points: a gate before any tool runs, a gate before anything sends, and a verifier on every reply.
- A rule the model cannot talk its way past
- Every refusal is recorded in the audit log
Never quote before lane and volume are captured
Tool blockedHazardous cargo always goes to a human
EscalatedDiscounts above 10% need a manager
Approval requiredMoney needs a human
Approvals for anything that spends
Requested ad campaigns, budget moves, discounts past a limit. A person decides, with the number attached.
- The agent can request, never apply
- Thresholds are yours to set, per rule
12% discount on quote Q-1148 · ₹4.2L
AIRequested by the agent mid-conversation. Above the 10% threshold, so it stops here.
The rest of control
Five effects, not free text
Block a tool until fields are captured, require a human first, force escalation on a topic, require approval above a threshold, or append an instruction. A closed set is auditable; free text is not.
The agent will not deny being an AI
It does not volunteer it either. Denial is refused by the verifier, outside the model, and is not configurable.
Grounded knowledge with citations
Chunks link back to the document they came from, so an answer can point at its source instead of asserting something unbacked.
Workflows as rows
Trigger, condition, action — edited like any other configuration. A visual canvas is a product in itself and is deliberately deferred.
See it with data in it
The demo is the real product, seeded with two businesses that sell completely differently.