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AREYTech

AREYLight High Efficiency Ai Engine

Autonomous Lighting Technology

Redefine Energytech & Conserve electricity usage by ensuring that lighting units can be controlled remotely and efficiently throught the use of artificial intelligence

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Installation Sites

Preception Technology

Efficiency Rates

Divide & Union Engine

Save Rates

Learning Tech






AREYLight AI Engine vNext-∞ | Smart Lighting Neural Core


Intelligence at the Edge.
Insights in the Cloud.

From radar pulses to deep reports — AREYLight’s AI Engine is your city’s silent genius.

Multi-Layer AI Processing

Our AI Engine processes environmental data through specialized neural networks, delivering intelligent lighting responses in milliseconds.

Sensor Fusion

Radar, PIR, Lux sensors

Computer Vision

YOLOv9 object detection

Cognitive Reasoning

Qwen 3-VL analysis

AI PROCESSING DASHBOARD
Sensor Data Ingest
Live
Object Detection
YOLOv9
74% mAP50
Scene Analysis
Qwen 3-VL
±6% accuracy
Gemma 3 Assistant
Response time < 0.8s

Multi-Layered AI Architecture

From environmental sensing to intelligent response — our AI Engine processes data through specialized neural networks.

Sensor Layer

Trigger Events

  • Radar movement detection

  • PIR human presence

  • Lux ambient light measurement

  • Voltage monitoring

Vision Layer

Object Detection

  • YOLOv9 real-time detection

  • Pedestrian identification

  • Vehicle classification

  • Animal recognition

Reasoning Layer

Scene Analysis

  • Qwen 3-VL multimodal analysis

  • Risk assessment scoring

  • Optimal light level calculation

  • Contextual understanding

Insights Layer

Reporting & Optimization

  • DeepSeek R1 trend analysis

  • Energy savings optimization

  • Predictive maintenance

  • Natural language reports

Real-Time Processing Pipeline

Our AI Engine processes data through each layer in milliseconds, enabling intelligent lighting responses while maintaining strict privacy standards.

Sensor to Vision
~50ms
Vision to Reasoning
~120ms
Reasoning to Action
~30ms
AI PROCESSING TIMELINE
Edge Compute Node #4271

Sensor Activation
Radar detects movement at 15m
+0ms

Camera Activation
YOLOv9 processes frame
+20ms

Object Identified
Vehicle (92% confidence)
+70ms

Risk Assessment
Qwen 3-VL analyzes scene
+150ms

Light Adjustment
Output set to 85% intensity
+200ms

Real-World Scenario

See how our AI Engine responds to complex situations with intelligent lighting decisions.

EMERGENCY SCENARIO ACTIVATED

It’s 3AM in Synevyr, snowy, -18°C

High-Risk Situation Detected
Probability: 87%

A truck loses traction on icy road. YOLOv9 detects skidding motion while Qwen 3-VL analyzes weather conditions and road surface.

Immediate Response

RL agent boosts adjacent lights to 100% intensity, creating safe zone for recovery.

Automated Reporting

DeepSeek R1 generates incident report with timestamps, environmental data, and response metrics.

Operator Explanation

Gemma 3 explains the event in plain Ukrainian: “Світло увімкнено на 100% через небезпечне ковзання вантажівки.”

SCENARIO VISUALIZATION
Live
3D Render

Vehicle Skid Detected
3:02:17 AM
High Risk
-18°C • Snowy
Lights Activated
3
Response Time
210ms
Energy Used
1.2kW

Smart Rural Roads

Adaptive lighting for country roads that responds to vehicles, pedestrians, and wildlife while conserving energy.


60-80% energy savings vs static lighting

Urban Dynamic Lighting

Intelligent light adjustment based on pedestrian density, weather conditions, and special events.


±6% lighting accuracy for optimal visibility

Predictive Maintenance

AI predicts failures before they occur, optimizing maintenance routes and reducing downtime.


0.91 ROC AUC for failure prediction

Benchmark Performance

Our AI Engine combines cutting-edge models for unparalleled smart lighting intelligence.

Model Specifications

FunctionModelPerformance
Object DetectionYOLOv9mAP50 ≈ 74%
Image ReasoningQwen 3-VLLighting accuracy ±6%
Language Q&AGemma 3Response < 0.8s
Reports & ForecastDeepSeek R1Trend insights, budget optimization
Dim DecisionQ-learning + RL60–80% energy savings
Maintenance PredictionXGBoostROC AUC 0.91

RURACTIVE Challenge Fit

CriteriaResponse
Road SafetyYOLO + Qwen detect and reason in real-time
Energy SavingRL agent + DeepSeek optimizations
Community TrustNatural language summaries via Gemma
Cost-EfficiencyRetrofit model, low OPEX
MaintenancePredictive alerts + optimized field routing

Edge Deployment Specs

Hardware
Jetson Nano/Orin
Encryption
AES-128 + TLS
Updates
Delta OTA < 200MB
Privacy
No face data stored

Deep Integration

AREYLight AI connects sensor data with advanced AI models for comprehensive smart lighting intelligence.

Sensor Data Pipeline

Lux, motion, and voltage data streamed to DeepSeek R1 for historical analysis and pattern detection.

Visual Intelligence

YOLOv9 object detection feeds into Qwen 3-VL for contextual understanding and risk assessment.

Natural Language Interface

Gemma 3 enables operators to query the system: “Why did this streetlight turn on at 2:17 AM?”

AI CHAT INTERFACE
Gemma 3 Assistant
AREYLight AI Assistant
Hello! I’m your AREYLight AI assistant. How can I help you with your smart lighting system today?
Operator
Why did streetlight #4271 turn on at 2:17 AM?
AREYLight AI Assistant
At 02:17:03, YOLOv9 detected a pedestrian with 89% confidence. Qwen 3-VL analyzed the scene as high-risk due to icy conditions (-12°C). The RL agent increased lighting to 85% for 4 minutes until the pedestrian left the area.
Operator
Show me the energy savings for this zone last week
AREYLight AI Assistant
Zone 14-B saved 68% energy vs static lighting (42.7 kWh saved). DeepSeek R1 forecasts 72% savings this week with similar activity patterns.

Multi-Layered
AI Flow

From sensor input to intelligent response in milliseconds

Sensors

Radar, PIR, Lux

Trigger events (movement, darkness)

Vision

YOLOv9

Detect objects (pedestrians, vehicles)

Reasoning

Qwen 3-VL

Analyze scene, suggest light levels

Insights

DeepSeek R1

Generate reports, risk scores

Interaction

Gemma 3

Chatbot & dashboard assistant

Seamless AI Integration

The AREYLight AI Engine processes data across multiple specialized AI models in real-time, creating a cohesive system that understands, decides, and acts autonomously.

  • Edge Processing: 90% of decisions made locally within 50ms

  • Context Awareness: Understands weather, traffic, and special events

  • Explainable AI: Natural language explanations for every decision

AI Architecture

The Neural Network of Smart Cities

Processing 1.2M decisions per second across 50+ cities

Live Scenario:
Emergency Response

“It’s 3AM in Synevyr, snowy, -18°C. A truck skids. YOLOv9 detects it. Qwen flags high risk. RL agent boosts light to 100%. DeepSeek logs the event. Gemma explains why the light was on — in plain Ukrainian.”

03:00:12

Sensor Activation

Radar detects vehicle moving at 62km/h in snowy conditions

03:00:14

Object Detection

YOLOv9 identifies truck with 94% confidence, calculates trajectory

03:00:15

Risk Assessment

Qwen 3-VL analyzes scene: icy road, poor visibility, high risk

03:00:16

Light Adjustment

Reinforcement learning agent boosts light to 100% illumination

03:00:18

Event Logging

DeepSeek R1 generates detailed report with risk factors

03:00:20

Operator Query

“Чому цей ліхтар увімкнувся о 3:17 ранку?”
“Ліхтар реагував на небезпечну ситуацію – вантажівка ковзала по обледенілій дорозі. Підвищена освітленість допомогла водієві краще бачити дорогу. Повний звіт надіслано до вашого кабінету.”
Emergency scenario

Real-Time Decision Making

From detection to action in under 200ms

High Risk Detected

Vehicle skidding detected • 94% confidence

Icy conditions • 62km/h
Emergency protocol activated

Models
& Metrics

Specialized AI components working in concert for optimal performance

Object Detection

YOLOv9 achieves state-of-the-art performance in real-time object detection

mAP50
≈ 74%
Latency
18ms
Edge Support
Jetson, NUC

Image Reasoning

Qwen 3-VL provides contextual understanding of visual scenes

Lighting Accuracy
±6%
OCR Accuracy
92%
Risk Prediction
88% AUC

Language Q&A

Gemma 3 delivers instant, accurate responses to operator queries

Response Time
< 0.8s
Languages
28+
Edge Support
Yes

Reports & Forecast

DeepSeek R1 generates actionable insights from operational data

Data Points/Day
2.4M
Energy Savings
60-80%
Budget Accuracy
±3%

Dim Decision

Reinforcement learning optimizes lighting levels continuously

Energy Savings
60-80%
Adjustments/Day
12,500+
Safety Compliance
100%

Maintenance Prediction

XGBoost models predict failures before they occur

ROC AUC
0.91
Lead Time
14.5 days
Cost Reduction
42%

RURACTIVE Challenge 18 Compliance

CriteriaResponse
Road SafetyYOLO + Qwen detect and reason in real-time
Energy SavingRL agent + DeepSeek optimizations
Community TrustNatural language summaries via Gemma
Cost-EfficiencyRetrofit model, low OPEX
MaintenancePredictive alerts + optimized field routing

Security &
Edge Optimization

Enterprise-grade security meets edge efficiency

AES-128 Encryption

All sensor data and communications are encrypted end-to-end with military-grade encryption.

Delta OTA Updates

Updates as small as 200MB, minimizing bandwidth usage while keeping systems current.

Privacy Filters

No face data stored – only anonymized object detection metadata retained.

Edge Hardware

Runs efficiently on Jetson Nano/Orin and single GPU NUC devices.

Security architecture

Secure by Design

From silicon to cloud

Deep Integration: Sensors + AI + Speech

The AREYLight AI Engine creates a seamless pipeline from raw sensor data to actionable insights and natural language interaction.

  • Sensor Fusion: Lux, motion, and voltage data feed directly into DeepSeek R1 for system health monitoring

  • Visual Pipeline: YOLOv9 object detection streams to Qwen 3-VL for contextual reasoning

  • Natural Interface: Gemma 3 enables operators to query the system in plain language



AREYLight AI Assistant

AREYLight AI Assistant
Hello! I can answer questions about our AI Engine and smart lighting solutions. What would you like to know?



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