> ## 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.

# Signal Processing

> How AI-generated outputs are transformed into structured signals for deterministic governance evaluation

# Signal Processing

Signal Processing defines how raw AI outputs are converted into structured, verifiable inputs for the Governance layer.

It is the boundary between probabilistic AI systems and deterministic authorization systems.

***

## System Flow

```text theme={null}
AI Output → Signal Processing → Signals → Governance → Authorization Decision → Execution Runtime → Attestation
1. AI output generation

AI systems generate raw outputs such as:

recommendations
predictions
classifications
proposed actions

These outputs are unstructured and non-deterministic.

2. Signal transformation

Signal Processing converts AI outputs into structured signals.

Each signal is:

typed
schema-validated
normalized
deterministic in structure

This ensures downstream systems receive consistent inputs.

3. Signal validation

Signals are validated for:

schema correctness
completeness
integrity
provenance consistency

Invalid signals are rejected before entering Governance.

4. Governance ingestion

Validated signals are passed to the Governance layer (@parmanasystems/governance).

Governance evaluates:

policy constraints
authorization rules
execution conditions

No probabilistic computation occurs beyond this point.

5. Output to Authorization Decision

Governance produces an Authorization Decision based on processed signals.

This decision is:

deterministic
reproducible
policy-bound
Properties of Signal Processing
Deterministic structure

Signal format is consistent across all runtimes.

AI isolation

AI outputs are never directly used in decisioning.

Validation enforced

Invalid or incomplete signals are rejected early.

Reproducibility

Identical inputs always produce identical signals.

Summary

Signal Processing ensures a strict boundary between AI systems and governance systems.

It guarantees:

AI outputs are normalized into structured signals
Governance only receives validated inputs
Authorization decisions remain deterministic
```