THE EXECUTION INTEGRITY LAYER FOR AIExplore our approach
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GOVERNANCE THAT REACHES EXECUTION

AI can decide.
Your business
sets the authority.

As AI agents move from answering questions to taking action, responsible AI needs more than policies and promises. Parmana helps ensure that systems execute only what has been explicitly authorized.

See how it works Our principles ↓
✓Explicit authority. Deterministic enforcement. Verifiable evidence.
EXECUTION CONTROL● CONTROL ACTIVE
AI
01 / PROPOSALAgent proposes an actionInitiate a $4,800 payment
REQUEST
✓
02 / AUTHORITY CHECKVerify before execution
VERIFIED
Approved actionPayment✓
Transaction limit$5,000 max✓
Authority statusValid✓
↗
03 / EXECUTIONAuthorized action proceedsDecision evidence recorded
ALLOWED
DECLARED AUTHORIZED EXECUTED PROVEN
THE GOVERNANCE GAP

Knowing what an AI system should do is not the same as controlling what it can execute.

01 — THE APPROACH

Responsible AI must work when it matters most.

AI safety, model evaluations, and policy frameworks all play important roles. But when an agent can issue a refund, change a customer record, approve a request, or move money, the business needs a control that applies to the action itself.

Parmana focuses on that boundary: checking whether the exact action is authorized before execution, and preserving evidence that can be verified afterwards.

Explore the control model
WATCH THE EXPLAINER

See how Parmana brings authority to execution.

A short introduction to the problem Parmana is built to solve.

02 — WHAT PARMANA DOES

From governance intent
to enforced control.

One execution integrity approach. Three connected capabilities.

01Authority
⌘

Define authority

Translate business intent into explicit, machine-checkable limits. Make clear which actions an AI agent may take, under which conditions, and on whose authority.

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02Deterministic control
⊙

Enforce at execution

Verify the proposed action against the approved authority before it reaches the business system. If authorization is missing, invalid, or ambiguous, do not proceed.

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03Evidence
⌁

Prove what happened

Preserve verifiable evidence connecting the declared authority, the decision, and the resulting execution, so teams can inspect and independently verify outcomes.

Discuss this capability
03 — THE PRINCIPLE

AI may propose.
Authority must be proven.

A model's confidence is not permission. A valid credential is not unlimited authority. A plausible interpretation is not approval.

01

Explicit authority
Actions are measured against defined business permissions and constraints.

02

Fail closed
Missing, invalid, or ambiguous authorization does not silently become permission.

03

Verifiable evidence
Records connect what was authorized with what the system actually did.

04

Human accountability
Exceptions and genuine discretion remain visible and accountable.

04 — WHERE IT MATTERS

When AI acts in the real world, authority matters.

Designed for environments where automated actions must stay within defined limits.

01

Financial services

Keep payments, refunds, and account actions within approved limits and delegated authority.

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02

Enterprise operations

Constrain actions across CRM, ERP, procurement, and other systems where mistakes carry real consequences.

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03

Public services

Help turn clearly defined rules into consistent digital decisions, with exceptions routed to accountable human judgment.

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THE PROMISE

Don't just ask AI
to behave. Control what
it can execute.

Build a clear boundary between what AI proposes and what your business authorizes. Make every permitted action accountable.

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DECLARED→AUTHORIZED→EXECUTED→PROVEN
05 — LET'S TALK

Put authority at the heart of your AI systems.

Exploring AI agents in financial services, enterprise operations, or public services? Let's discuss the execution integrity problem.

Discuss your use case