Digital Chemical Safety & IH Risk Management Platform

A Bimodal Edge-AI Ecosystem for Predictive Occupational Health

TRL 4 Validated Patent PI 2026000049 SME Ready

01. Problem Statement: The Temporal Gap

Static vs Dynamic Risk

Current Chemical Health Risk Assessments (CHRA) are static annual reports. They fail to capture 95% of daily hazard spikes occurring between audits, leaving workers vulnerable for 364 days a year.

Synergistic Blindness

Standard sensors ignore Synergistic Effects—where combined low-level hazards (VOCs + Heat + Dust) cause Sick Building Syndrome (SBS), resulting in high absenteeism and medical leave costs.

02. The Solution: Predictive Safety Ecosystem

Edge-AI Hubs

On-site hardware nodes for continuous, autonomous health monitoring.

SaaS Dashboard

Centralized command center for digital compliance and risk heatmaps.

Bimodal App

Worker-centric interface for real-time symptom data fusion.

03. Technology: Patent PI 2026000049

"Bimodal Edge-AI Prediction Engine"

Our core innovation lies in the synchronous fusion of bimodal data streams directly at the hardware edge:

Objective Stream: Real-time telemetry from Metal-Oxide Semiconductor (MOS) gas arrays and laser Particulate Matter (PM) sensors.
Subjective Stream: Subjective occupant health scores retrieved via the General Assessment of Symptoms (GAS) API.
Processing: TinyML Neural Networks embedded in ESP32-S3 chips.

04. Innovation: Edge vs Cloud Processing

Zero-Latency Prediction

Traditional systems suffer from 10-30s cloud latency. Our system calculates the "Health Risk Estimate" in 0.1 seconds locally on the device.

Industrial Resilience

Factory environments often have poor WiFi. Our Edge-AI continues to protect workers and issue local alerts even if the internet goes offline.

05. Features: The Edge-AI Hub

7-in-1 Sensor Array: Comprehensive monitoring of PM2.5, PM10, TVOC, CO2, Temperature, Humidity, and Formaldehyde (HCHO).
OLED Display: Crisp, real-time onsite hazard metrics and immediate warnings displayed directly on the hub.
Edge Intelligence: Runs machine learning models locally without needing continuous internet access.

06. Features: Bimodal Worker App

Planned Prospect Design Mobile App UI

The Human Sensor

Workers provide subjective symptoms (e.g., eye irritation, headache) which act as a Validation Proxy for the AI model. This bimodal feedback loop significantly reduces false positives and improves prediction accuracy.

07. Features: SaaS Risk Dashboard

Heatmaps: Visualize factory hazard clusters in real-time.

Auto-Chemical Exposure: Generate digital compliance logs for DOSH audits.

08. Uniqueness & Competitive Advantage

Bimodal Validation

We are the only platform that uses worker biological feedback to cross-verify sensor data, ensuring the most accurate health risk profile in the industry.

Defensible IP

Our exclusive license of MyIPO Patent PI 2026000049 creates a legal barrier that prevents competitors from replicating our bimodal fusion method.

Proprietary Dataset

TinyML Edge Moat

USM R&D Backbone

09. Technology Matrix: Creator vs Suppliers

Unlike standard industry players who act as product ambassadors for imported tech, Quinz x USM builds intelligent products trained on Malaysian occupational data.

Capability	Quinz x USM (Technology Creators)	Current Market Brands (Suppliers/Ambassadors)
Algorithm Design	Trained on Local Industrial & Epidemiological Data	Imported/ Generic Algorithms
Data Fusion	Bimodal (Hardware + Human Health)	Monomodal (Hardware Only)
Processing Architecture	Edge-AI (Zero Latency, Offline Ready)	Cloud-Dependent
Customization	Deep Hardware & Software Control	Locked Ecosystems

10. Value Proposition: A Global Differentiator

Why This is Unique Globally

Standard environmental sensors worldwide only measure what is in the air. We measure how the air actually affects the workforce. By fusing objective 7-in-1 sensor data with real-time physiological symptom reporting directly at the edge, we turn reactive audits into predictive health management.

Medical Cost Reduction

Drastically cuts downtime and sick leave linked to unrecognized synergistic hazards.

100% Digital Compliance

Automated data logging ensures constant readiness for JKKP (DOSH) audits.

Operational Efficiency

Saves an average of 15 hours per month on manual industrial hygiene logging.

11. Current Stage: TRL 4 Validated

The project is currently at Technology Readiness Level 4:

Algorithms successfully validated at USM National Poison Centre labs.
Edge-AI hardware prototype fully functional on ESP32-S3.
Bimodal data fusion successfully tested with simulated stimuli.

12. 12-Month Development Roadmap

Months 1-4: UI/UX & Backend Finalization

Months 5-8: Industrial Hub Fabrication

Months 9-12: SME Field Pilot & Launch

13. Technological Validation & Accuracy

92% Prediction Accuracy

During lab trials, the bimodal AI model demonstrated a 92% success rate in correlating TVOC spikes with subjective respiratory discomfort, compared to only 65% when using objective data alone.

14. Collaboration & Strategic Partners

Universiti Sains Malaysia

Role: Technology Provider, R&D Backbone & IP Licensor.

ERALAB Sdn Bhd

Role: ISO 17025 Accredited Calibration & Testing Partner.

15. Revenue & Pricing Strategy

A flexible, scalable model tailored for SMEs and heavy industries.

Basic Package

RM 299 /mo

Standard Hazard Assessment
SaaS Compliance Reporting App
Manual Data Logging

Best Value

Predictive Package

RM 999 /mo

2 Units of 7-in-1 Edge Hubs
1-Year System Subscription
Standard Assessment Included
Bimodal AI Analytics Dashboard

16. Go-To-Market Strategy & Upselling

Phase 1: Direct Conversion

Immediate migration of existing manual consultancy clients to the SaaS platform, transitioning them to a recurring revenue model.

Phase 2: Hardware Upsell

Converting SaaS users to the Predictive Package by introducing Edge Hubs as a "Hardware-as-a-Service" upgrade for real-time tracking.

Phase 3: Network Leverage

Strategic channel partnerships with DOSH-registered safety training providers (SSTP) to distribute the technology nation-wide.

17. Traction: Real-World Foundation

50

Prospect Industrial Clients

Ready for digital compliance onboarding.

3

Interested Industrial Partners

Committed to field pilots in the next couple of months.

18. Future Growth: Scaling & Licensing

Product Scaling

Transitioning from low-volume 3D-printed hub fabrication to injection molding mass manufacturing to fulfill high-volume SME orders across Malaysia.

Technology Licensing

Licensing our proprietary Edge-AI algorithms and Bimodal validation models to 3rd party hardware manufacturers globally, creating a highly scalable software revenue stream.

19. Management Team & Ask

Ts. Fera Shima Aziz

MD & Commercial Lead

Quinz

Mr. Abdullah Jainuin

Operation Manager

Quinz

Ts. Dr. Syazwan Aizat

Technical Advisor (Inventor)

USM

UI/UX Development

Hardware Integration

Market Adoption

The Ask: RM 100,000 - RM 120,000

Protecting the Workforce through Malaysian Innovation.