LIVE COGNITIVE DEMO

This Is Not A Chatbot.

90 seconds to understand what makes Aura different from every other AI system.

Not an LLM.Not Reinforcement Learning.This is Active Inference.

The brain doesn't optimize rewards. It minimizes surprise. So does Aura.

Running 100% locally -- No cloud connection

What You're About To See

Real-time probabilistic cognition -- not keyword matching
Context-indexed learning -- each sensor learns independently
Active information seeking -- the system knows what it doesn't know
Belief-driven policy -- actions from uncertainty, not thresholds

Why This Matters

vs LLMsNo hallucination. No token limits. Runs on $50 hardware.

vs RLNo reward hacking. No catastrophic forgetting. No training data.

vs RulesNo hardcoding. No false positives. Adapts to your home.

Perception

Sensor data encoded via semantic similarity -- not keyword matching

Beliefs

Context-indexed weights -- each sensor learns independently

Policy

Actions generated from beliefs, not raw sensor data

Active Inference

System identifies what it doesn't know and requests specific data

Sensor Channels

(Click bar to spike sensor value)

Semantic Distress Scoring

Acute Danger

0.0%

Health Risk

0.0%

Env. Hazard

0.0%

Intrusion

0.0%

Inactivity Alert

0.0%

Active Queries

No high-uncertainty sensors -- system is confident

Policy Actions

No actions required -- all distress levels nominal

How It Actually Works

Semantic Perception

Instead of 'if temperature > 35, danger', we compute semantic similarity between sensor contexts and distress prototypes. The system understands what 'gas_concentration: 800' MEANS relative to concepts like 'acute_danger'.

Context-Indexed Learning

Each sensor context has its own weight set. When gas data arrives, only gas weights update -- not temperature, not heart rate. This prevents catastrophic forgetting.

Active Information Seeking

The model doesn't passively wait for data. It identifies beliefs with high uncertainty and generates specific perception requests. It knows what it doesn't know.

Belief-Driven Policy

Actions come from probabilistic beliefs, not sensor thresholds. Soft sigmoid activations -- everything is continuous, no discrete mode switching.

Simplified visualization of the Aura Home cognitive architecture built on Archotec. Production system uses PyTorch, semantic embeddings, and gradient-based online learning.

Controls

Speed

SlowFast

Parameters

Learning Rate0.08

Attention Gain0.60

Prior Strength0.30

Uncertainty Threshold0.60

Global Metrics

Free Energy1.000

Total Uncertainty1.000

Learning Progress0.000

Distress Level0.000

Steps0

Free Energy

Total Uncertainty

Distress Level

This is not rule-based automation.

This is local probabilistic cognition.

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Aura HomeSmart home cognitive companion with 100% local AI

CompareSee how Archotec differs from traditional AI

CompatibilitySupported devices, sensors and protocols

ARCHOTEC

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LIVE COGNITIVE DEMO

This Is Not A Chatbot.

90 seconds to understand what makes Aura different from every other AI system.

Not an LLM.Not Reinforcement Learning.This is Active Inference.

The brain doesn't optimize rewards. It minimizes surprise. So does Aura.

Running 100% locally -- No cloud connection

What You're About To See

Real-time probabilistic cognition -- not keyword matching
Context-indexed learning -- each sensor learns independently
Active information seeking -- the system knows what it doesn't know
Belief-driven policy -- actions from uncertainty, not thresholds

Why This Matters

vs LLMsNo hallucination. No token limits. Runs on $50 hardware.

vs RLNo reward hacking. No catastrophic forgetting. No training data.

vs RulesNo hardcoding. No false positives. Adapts to your home.

Perception

Sensor data encoded via semantic similarity -- not keyword matching

Beliefs

Context-indexed weights -- each sensor learns independently

Policy

Actions generated from beliefs, not raw sensor data

Active Inference

System identifies what it doesn't know and requests specific data

Sensor Channels

(Click bar to spike sensor value)

Semantic Distress Scoring

Acute Danger

0.0%

Health Risk

0.0%

Env. Hazard

0.0%

Intrusion

0.0%

Inactivity Alert

0.0%

Active Queries

No high-uncertainty sensors -- system is confident

Policy Actions

No actions required -- all distress levels nominal

How It Actually Works

Semantic Perception

Context-Indexed Learning

Each sensor context has its own weight set. When gas data arrives, only gas weights update -- not temperature, not heart rate. This prevents catastrophic forgetting.

Active Information Seeking

The model doesn't passively wait for data. It identifies beliefs with high uncertainty and generates specific perception requests. It knows what it doesn't know.

Belief-Driven Policy

Actions come from probabilistic beliefs, not sensor thresholds. Soft sigmoid activations -- everything is continuous, no discrete mode switching.

Simplified visualization of the Aura Home cognitive architecture built on Archotec. Production system uses PyTorch, semantic embeddings, and gradient-based online learning.