NIVQRA V0.1 · Authority & control layer

Human authority for autonomous systems.

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.

What this is
A working control layer with realistic, simulated data
Payments
Simulated end to end — no live payment execution
Custody
None. No wallets, no cards, no customer funds
Workspace
Northstar Systems — demonstration organisation
Data
Sandbox only. Nothing here is production or customer-facing
Status
NIVQRA V0.1
Fig. 01 — Issuance sealRev. 2.0
01Identity & mandates

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.

02Permissions & approvals

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.

03Audit & revocation

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.

How it works

Five steps, the same way every time.

No language model decides whether a purchase is allowed. The rules are ordinary, readable conditions you write once and can inspect at any time.

  1. 01IdentityAn agent is registered and given an identity inside the workspace.
  2. 02MandateYou grant delegated authority: access, categories, counterparties, ceilings and limits.
  3. 03EvaluationEvery proposed action is evaluated against the mandate in a fixed, published order.
  4. 04DecisionExecuted automatically, routed to a named approver, or refused — always with a reason.
  5. 05Audit & revokeThe chain is written as it happens, and authority can be revoked at any moment.
Example — the Northstar Systems workspace

Three agents, three mandates, three different outcomes.

This is the exact configuration you can operate in the demo workspace.

01
Procurement Agent #03
€5,000 / month
€250 per action. Auto-executes under €100; €100–€250 needs human approval. Approved vendors only: AWS, OpenAI, Notion, Datadog. New beneficiaries blocked.
02
Research Agent
€1,000 / month
€50 per action. APIs, data and research permitted. Any new vendor requires human approval.
03
Operations Agent
€10,000 / month
€500 per action. Approval required above €250. Approved operations tooling only; transfers and digital assets refused.
Trust — how the controls work

Fixed rules, named approvers, a record of everything.

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.

01

Deterministic policy evaluation

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

  • Rules are evaluated in a published, fixed order
  • Identical inputs always produce an identical decision
  • Every decision carries the rule that produced it
  • A refusal names what was violated, not a generic error
02

Named human approvals

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

  • Exceptions route to a named approver, not a shared queue
  • Approval records the person, the decision and the rationale
  • Requests in review cannot settle or draw budget
  • Approvers can refuse, and the reason is kept with the case
03

Complete audit trail

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

  • Six-stage event chain per purchase, written as it happens
  • Timestamps and actor attribution on every entry
  • Append-only: no edits, no deletions after the event
  • Requests and audit trail export to CSV for your own records
Fig. 02 — Path of a single purchaseDeterministic · no model judgement

Stage 1 of 4: Deterministic · no model judgement

  1. 01 · Instruction

    Agent proposes a purchase — vendor, category, amount.

  2. 02 · Policy check

    Budget · ceiling · category · vendor · recurrence. No model judgement.

  3. 03 · One of three outcomes

    Cleared · approval required · refused.

  4. 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.

Boundary of the MVP

Payments are simulated in this MVP.

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.

  • No live payment execution and no payment credentials
  • No custody: no wallets, no cards, no customer funds
  • All organisations, agents, vendors and amounts are sample data
  • Sandbox environment — nothing here is production
Controls in action

Every answer has a screen behind it.

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 automaticallyApproved automatically.

Simulated request

Agent
Research Agent
Vendor
Eurostat API
Amount
€12.50
Purpose
Euro-area money-market rate series — weekly refresh

Outcome

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 001

Walk the six-stage chain from instruction to audit entry.

Questions this answers

Can an agent spend money we have not authorised?

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.

Does a language model decide whether a purchase is allowed?

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.

What stops budget from being quietly exceeded across many small purchases?

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.

Plans & NIVQRA Credits

Start free. Meter the intelligence, not the authority.

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.

Explorer

200 NIVQRA Credits / month

Free

For evaluation and learning.

  • 1 workspace
  • 2 active agents
  • 5 active mandates
  • 200 NIVQRA Credits / month
  • 30-day evidence history

Operator

1,000 NIVQRA Credits / month

€99/ month

For founders and small AI teams.

  • 1 workspace
  • 5 active agents
  • 50 active mandates
  • 1,000 NIVQRA Credits / month
  • 90-day evidence history

Enterprise

Custom credit pool

Custom

For organisations managing high-consequence machine authority.

  • Custom agents and credit pool
  • Advanced retention
  • SSO and advanced admin controls — contracted
  • Audit APIs
  • Custom policy templates

What a credit buys

Ask, search or explanation
1 credit
Multi-agent comparison or deep authority analysis
3 credits
Draft mandate or draft revocation
5 credits
Evidence / compliance-style report
10 credits

Demo workspace: execution is simulated, billing is not connected, and no production financial rails are attached. Safe and Base settlement is a future module.

Questions — procurement, controls & security

What procurement and security teams ask first.

Every answer points at something you can open in the demo. Where the pilot is simulated rather than live, it says so plainly.

Read the full control model.

Procurement
01Can an agent spend money we have not authorised?+

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.

02Who signs off on exceptions, and how is that recorded?+

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.

03How does this fit our existing purchase-approval process?+

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.

Controls
04Does a language model decide whether a purchase is allowed?+

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.

05Can prohibited spend be overridden by anyone?+

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.

06What stops budget from being quietly exceeded across many small purchases?+

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.

Security
07Does this hold funds, cards or wallets?+

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.

08What data is in the environment?+

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.

09Can the audit trail be edited after the fact?+

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.

10What are the limits of this pilot?+

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.

Request pilot access

Run the control model on your own agents.

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.

We use your details only to respond to this request.