AI at the Edge.
Intelligence Where It Matters.
WGTech's AIIoT solutions embed AI directly into devices and sensors — enabling real-time decisions, on-device inference, and intelligent automation without cloud dependency.
Each solution runs AI inference on-device — low latency, data privacy by design, and continuous operation independent of cloud connectivity.
01
AI-Powered Audio Recognition System
Voice commands and acoustic events — detected and acted on at the device, in real time.
An edge AI audio recognition system that identifies spoken voice commands and predefined acoustic events — triggering appliance control actions with low latency and no reliance on a cloud connection. All inference happens on-device, keeping audio data local and response times fast.
Designed for industrial and commercial environments where reliable audio-triggered automation must operate even during network interruption — and where sending voice data to external servers is not an option.
EDGE AI
On-device inference · No cloud dependency
Detection Capabilities
Voice Command Detection
Alarm Signal Recognition
Acoustic Event Detection
Predefined Sound Profiles
Speaker Audio Feedback
Decision Intelligence
Threshold-Based Decision Logic
Confidence Score Validation
Robust Activation / Deactivation Logic
False Positive Minimisation
System Characteristics
Voice-Triggered ON/OFF Actuation
Secure Local Processing
Eliminates Cloud Dependency
Data Privacy by Design
Low-Latency Response
Appliance Control Integration
02
AI-Driven Smart Farming
Precision cultivation monitoring
— tailored to each growing environment's distinct requirements.
Cultivation 01
Hydroponics
Monitors
-
Temperature
-
Humidity
-
pH Level
-
TDS (Nutrient Concentration)
Automated Controls
Nutrient dosing, irrigation cycles, climate regulation, and ventilation based on real-time sensor thresholds.
Cultivation 02
Mushroom Cultivation
Monitors
-
Temperature
-
Humidity
-
CO₂ Concentration
Automated Controls
Heater, cooler, humidifier, and exhauster — triggered automatically to maintain the optimal fruiting environment.
A single AIIoT platform monitors temperature, humidity, CO₂, pH, and nutrient levels in real time across three distinct cultivation environments — each with its own sensor profile, control logic, and anomaly thresholds.
Edge AI-powered analytics detect drift and anomalies before they affect yield. Automated controls respond instantly. Remote monitoring and real-time alerts keep operators informed from anywhere.
Cultivation 03
Sericulture
Monitors
-
Temperature
-
Humidity
-
Air Quality
-
Light Intensity
Automated Controls
Light and ventilation management calibrated to silkworm lifecycle stages for consistent cocoon yield.
A two-phase AI approach to real-time inline quality monitoring — starting with unsupervised anomaly detection, then graduating to a supervised deep learning model trained on lab-verified quality labels. ≥95% prediction accuracy. ≤10 second inference latency.
Inline Food Quality Monitoring
03
AI Protein & Fat Prediction — at production speed, without lab delays.
Pressure
Temperature
Moisture
Multi-Sensor RS-485 Network
Flow
Live sensors monitored
Phase 1
Drift Detection & Anomaly Alerts — Before Labels Exist
Sensor data is continuously ingested and analysed using unsupervised machine learning — clustering patterns across flow, pressure, temperature, and moisture to detect drift and surface anomalies without any manually labelled data. Operators receive alerts and stability indices from day one of deployment.
Pattern Learning Across Sensor Streams
Drift Detection
Phase 2
Protein & Fat Prediction — Lab-Verified, Production-Deployed
Lab-verified protein and fat measurements are used to train a deep learning model on the same sensor streams. Once deployed, the model predicts protein % and fat % for every production batch in real time — with a confidence score feedback loop that flags when retraining is required.
≥95% Prediction Accuracy
Operator-Approved Retraining
Confidence Score Feedback Loop
Stability Index
≤10s Inference Latency
Protein % Prediction
Fat % Prediction
Anomaly Alerts
What AIIoT changes at the operational level
Real-Time Decisions at the Source
AI inference at the edge means decisions are made within milliseconds of a sensor reading or voice command — not after a round-trip to a remote server.
Reduced Cloud Dependency
On-device processing lowers bandwidth requirements, eliminates latency from cloud inference, and ensures uninterrupted operation in low-connectivity environments.
Continuous Quality & Compliance
Inline food quality monitoring replaces lab sampling cycles with continuous, per-batch AI predictions — reducing quality lag from hours to seconds.
Automated Cultivation Control
Smart farming deployments eliminate manual environment checks — sensors trigger precise control actions that maintain optimal growing conditions around the clock.
Secure Operational Data
Voice data, sensor streams, and quality metrics are processed on-device — protecting proprietary operational data from exposure through external cloud systems.
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Let's Work
Together
+91 63620 37962
support@wgtech.ai
WG Tech, Bengaluru
WG Tech Solutions Pvt Ltd
Rainmakers Workspaces, No. 759, 3rd Floor, 8th Main Rd, KSRTC Layout, 3rd Phase, J. P. Nagar, Bengaluru,
Karnataka 560078
+1 (408) 391-2142
support@wgtech.ai
WG Tech, Delaware
WG Tech Solutions Inc,
8, The Green, #11166, Dover,
DE 19901, USA
Disclaimer: All images and video content displayed on this website are for representational and illustrative purposes only. Actual software interfaces and hardware performance may vary. For a live demonstration or to view production-grade output, please write to us at support@wgtech.ai to schedule a demo.
© 2026 by WG Tech Solutions Pvt.Ltd
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