Explorer
200 NIVQRA Credits / monthFree
For evaluation and learning.
- 1 workspace
- 2 active agents
- 5 active mandates
- 200 NIVQRA Credits / month
- 30-day evidence history
Agents need delegated authority, not unrestricted access. NIVQRA is the layer where you define what each agent can access, spend, execute and approve — enforced deterministically at the moment of action. Permitted actions execute, exceptions route to a named human, prohibited actions are refused. Every action is attributable and every permission is revocable in one click.
The demo opens instantly, no sign-up required. Take the five-step guided tour or read how the control model works.
Every agent has an identity and a written mandate: what it may access, the categories it may act in, the counterparties it may use, a per-action ceiling and a monthly limit.
Authority is enforced deterministically at the moment of action — not by model judgement. Permitted actions execute, exceptions route to a named human, prohibited actions are refused.
Each action leaves a timestamped, attributable record: who instructed, which rule applied, who approved, what executed. Authority can be revoked instantly, and the revocation is recorded.
No language model decides whether a purchase is allowed. The rules are ordinary, readable conditions you write once and can inspect at any time.
This is the exact configuration you can operate in the demo workspace.
Three mechanisms carry the whole control model. Each one is visible in the demo, and each one is stated plainly here — including what this pilot does not do.
Every request is checked by fixed rules in a fixed order — budget remaining, per-purchase ceiling, permitted category, permitted vendor, prohibited list. No language model takes part in the decision, so the same request always produces the same outcome and the reason is always stated in full.
See it on the Policies screen
Anything outside the delegated mandate stops and waits for a person. Each rule names the individual who owns that exception, so an approval is never anonymous and never implicit. Nothing settles while a request is in review, and a refusal ends the request outright.
See it on the Requests screen
Each purchase writes a timestamped chain of events as it happens: the request, the policy evaluation, the decision, any human approval, the settlement attempt and the budget drawdown. Entries are append-only in the product model — nothing is edited or removed after the fact — and the whole ledger exports to CSV.
See it on the Audit trail screen
Stage 1 of 4: Deterministic · no model judgement
01 · Instruction
Agent proposes a purchase — vendor, category, amount.
02 · Policy check
Budget · ceiling · category · vendor · recurrence. No model judgement.
03 · One of three outcomes
Cleared · approval required · refused.
04 · Audit trail
Decision, rule and accountable person are written to the ledger.
Deterministic · no model judgementNothing has been proposed yet. The control model is fixed in advance — the same request always produces the same outcome.
Cleared
Within budget, category and vendor rules. Settles immediately.
Approval required
Threshold, new vendor or recurring commitment. Routed to a named approver.
Refused
Prohibited category. No approval is offered and nothing settles.
Settlement in this pilot is modelled, not executed. No card, wallet, bank rail or customer funds are involved, and no money moves at any point. Amounts, vendors and balances are realistic sample data so the control model can be evaluated honestly — the approvals, the policy engine and the audit trail are real working software; the payment step at the end is a simulation.
Pick a control below. You get the exact request the demo runs, the outcome the policy engine produces, and the questions that outcome answers. Each one opens the matching case in the guided demo.
Within mandate selected. Simulated request for Research Agent, €12.50. Outcome: Approved automatically — Approved automatically.
Approved automatically: Approved automatically
Inside the monthly budget, under the per-purchase ceiling, permitted category and known vendor. Settled in simulation and written to the audit trail with the rule that allowed it.
Open Guided demo — Case 001Walk the six-stage chain from instruction to audit entry.
No. An agent can only spend against a mandate you create: a monthly budget, a per-purchase ceiling, permitted categories and permitted vendors. A request that falls outside any of those is either routed to a named approver or refused outright — the agent never has a spending path around the rules. You can see the three pilot mandates on the Agents screen and edit their limits on the Policies screen.
No. The policy engine is deterministic: budget, ceiling, category and vendor are evaluated in a fixed order, and the same request always produces the same decision. A model may draft or initiate a request, but it has no influence on the outcome. That is why the three demo cases reproduce identically every time you reset them.
Every approved purchase draws down the agent's monthly budget at settlement, and each new request is checked against the remaining balance rather than the original allowance. Small purchases that individually clear the ceiling still stop once the budget is spent. Remaining balances are shown per agent on the Control and Identity screens.
An agent spends only inside written authority — and when it is inside, no human is needed. See all questions.
Authority itself is never metered — mandates, approvals and revocation always work. NIVQRA Credits meter the authority intelligence layer: the questions you ask of the control plane and the structured drafts it prepares for human confirmation. GPT interprets. NIVQRA authorizes. Humans govern.
Free
For evaluation and learning.
€99/ month
For founders and small AI teams.
Custom
For organisations managing high-consequence machine authority.
What a credit buys
Demo workspace: execution is simulated, billing is not connected, and no production financial rails are attached. Safe and Base settlement is a future module.
Every answer points at something you can open in the demo. Where the pilot is simulated rather than live, it says so plainly.
No. An agent can only spend against a mandate you create: a monthly budget, a per-purchase ceiling, permitted categories and permitted vendors. A request that falls outside any of those is either routed to a named approver or refused outright — the agent never has a spending path around the rules. You can see the three pilot mandates on the Agents screen and edit their limits on the Policies screen.
Each rule names the human who owns the exception. In the pilot, spend above an agent's per-purchase ceiling and every recurring subscription route to a named approver, who approves or refuses in the Requests screen. The approval carries the approver's name, the decision, the rationale and the timestamp, and is written to the audit trail as a distinct event. Case 002 in the guided demo walks through a live escalation.
It sits in front of it, not instead of it. The mandate encodes the delegated authority your finance policy already grants; anything beyond that delegation becomes a normal human approval with an owner and a paper trail. Requests and the audit trail export to CSV so the evidence can be filed alongside your existing procurement and month-end records.
No. The policy engine is deterministic: budget, ceiling, category and vendor are evaluated in a fixed order, and the same request always produces the same decision. A model may draft or initiate a request, but it has no influence on the outcome. That is why the three demo cases reproduce identically every time you reset them.
Prohibited categories are a hard refusal, not an escalation. In the pilot the Operations Agent is barred from fund transfers and crypto assets; that request is refused with a stated reason and cannot be approved through the interface by any user. Case 003 in the guided demo shows the refusal and the record it leaves.
Every approved purchase draws down the agent's monthly budget at settlement, and each new request is checked against the remaining balance rather than the original allowance. Small purchases that individually clear the ceiling still stop once the budget is spent. Remaining balances are shown per agent on the Control and Identity screens.
No. This pilot has no custody of any kind — no wallets, no cards, no customer funds and no live payment execution. Settlement is simulated end to end so the control model, the approval chain and the evidence can be evaluated without moving money or onboarding a payment provider.
Realistic but entirely simulated pilot data: three agents, their mandates, the policy rules and the canonical demo cases. Nothing here is production or customer-facing, and no real vendor is invoiced. If you request pilot access we collect only the contact details you submit, and use them solely to respond.
The trail is append-only in behaviour: each decision, approval and settlement is written as a separate timestamped event with its actor, and the interface offers no way to alter or delete an event once recorded. Reset restores the canonical starting state for demonstration purposes rather than editing history in place. You can export the full trail as CSV for independent review.
It demonstrates one working rail end to end. There is no live money movement, no production hardening, no vendor integrations and no SSO or enterprise access management yet. Those are exactly the questions the pilot is intended to surface — the control model is the part being validated here.
The pilot is open to a small number of finance and compliance teams already delegating spend to AI agents. Tell us about your setup and we will get back to you.
Prefer to look first? Open the live demo — same product, simulated data.