> ## Documentation Index
> Fetch the complete documentation index at: https://docs.manthan.systems/llms.txt
> Use this file to discover all available pages before exploring further.

# Who it is for

\# Who Parmana is For

Parmana is built for organizations deploying AI systems that can trigger real-world actions.

These are systems where execution has consequences.

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\## Primary users

\### 1. AI product teams

Teams building AI agents that:

\- trigger workflows

\- take actions in production systems

\- interact with external APIs

They need controlled execution boundaries.

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\### 2. Fintech and financial systems

Organizations handling:

\- payments

\- transfers

\- approvals

\- risk decisions

They require deterministic authorization before execution.

\---

\### 3. Enterprise infrastructure teams

Teams managing:

\- cloud infrastructure

\- deployment pipelines

\- internal automation systems

They need verifiable execution control.

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\### 4. Healthcare and regulated systems

Systems where actions affect:

\- patient records

\- approvals

\- compliance workflows

They require strict auditability and authorization enforcement.

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\### 5. AI platform companies

Companies building:

\- agent frameworks

\- autonomous systems

\- AI orchestration layers

They need a trust and authorization boundary between AI and execution.

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\## What all these users have in common

They all deploy systems where:

\- AI generates actions

\- actions have real-world consequences

\- execution must be controlled

\- auditability is required

\- compliance matters

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\## The pain they share

All these systems struggle with:

\- unclear execution authority

\- lack of deterministic enforcement

\- no cryptographic audit trail

\- fragmented approval logic

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\## Why they choose Parmana

Parmana provides:

\- deterministic authorization before execution

\- cryptographic verification of decisions

\- strict separation of AI and execution

\- independent auditability

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\## Ideal system boundary

Parmana becomes necessary when:

> AI output is not just information — it becomes execution intent.

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\## Summary

Parmana is for systems where:

\- AI proposes actions

\- humans define authority

\- execution must be verifiable

\- mistakes are not acceptable