GRAMIN GUARD
Predictive Intelligence for Rural Infrastructure
Primary users: Cold storage operators, vaccine depot managers, rural infrastructure operators
Farmers and healthcare beneficiaries gain downstream impact
The Critical Gap
Most infrastructure failures degrade silently before temperatures fail.
Traditional monitoring only alerts when it's too late - when temperatures have already breached safe limits. By then, vaccines are already compromised.
Reactive Monitoring
Alerts trigger only after temperature breaches occur - too late for prevention.
No Pattern Recognition
Current systems don't learn from historical failures or recognize early warning patterns.
Human-Dependent
Requires constant human vigilance in resource-constrained environments.
How Gramin Guard Thinks
Failure Timeline Simulation
Current Deviation Detected
No temperature breach yet
System detects 15% increase in compressor current draw during cooling cycles.
Cooling Recovery Slows
Temperature stable, but pattern emerging
Compressor takes 40% longer to reach target temperature after door openings.
Risk Score Escalates
Temperature still within limits
Correlation detected with voltage fluctuations and reduced cooling efficiency.
Conventional Alarm Triggers
Temperature breach occurs
Temperature exceeds 8°C - vaccines already compromised. Compressor failure imminent.
The Intelligence Difference
Gramin Guard alerts on Days 1-2, not Day 4.
By detecting electrical pattern anomalies before temperature breaches,
we provide a 2-3 day warning window for preventive maintenance.
Demo Scenarios
Adaptive Learning Through Feedback
Initial Sensitivity
High sensitivity catches all anomalies but creates alert fatigue for operators.
After Technician Feedback Applied
System thresholds adapt after technician-confirmed faults, improving accuracy over time.
Closed-Loop Intelligence
When a technician confirms or dismisses an alert, that feedback is used to refine detection models. This creates a continuous improvement loop where the system becomes more accurate for each specific installation environment.
How It Works
Non-Invasive Sensing
Clip-on current transformers monitor power consumption patterns without wiring modifications.
Edge Intelligence
Statistical anomaly detection and correlation models run locally on Raspberry Pi Zero.
Offline-First Design
Stores data locally and syncs when connectivity is available. Works entirely offline when needed.
Intelligence Stack
Core Detection Methods
- Current waveform analysis
- Time-series pattern recognition
- Multi-variable correlation
Adaptive Features
- Historical pattern learning
- Technician feedback integration
- Dynamic threshold adjustment
Impact & SDG Alignment
SDG 3: Good Health & Well-being
Direct Contribution: Preventing vaccine spoilage through early detection of refrigeration failures.
Target Alignment:
- • Target 3.8: Achieve universal health coverage
- • Target 3.b: Support vaccine availability and distribution
SDG 9: Industry & Infrastructure
Innovation Focus: Retrofit intelligence onto existing rural infrastructure without replacement.
Target Alignment:
- • Target 9.1: Develop reliable, sustainable infrastructure
- • Target 9.b: Support domestic technology development
User Ecosystem
Primary Users
Cold storage operators, vaccine depot managers, rural infrastructure operators
Secondary Users
Healthcare workers, maintenance technicians, supply chain managers
Downstream Impact
Farmers, patients, communities receiving reliable vaccines and perishables
Business Model: Target infrastructure operators who bear the cost of spoilage and downtime.
Farmers and patients benefit through improved supply chain reliability.
Technical Specifications
| Component | Specification | Purpose |
|---|---|---|
| Current Sensor | Non-invasive clip-on CT sensor | Monitor power consumption without wiring |
| Processing Unit | Raspberry Pi Zero 2 W | Edge computing for local data processing |
| Detection Algorithm |
Adaptive anomaly detection (edge-based) Statistical analysis + pattern correlation |
Identify equipment failure patterns pre-failure |
| Learning Mechanism | Technician feedback integration | Continuous improvement of detection accuracy |
Development Status
Currently in prototype phase with working anomaly detection models. Seeking pilot partnerships for field validation.
Key Judge Takeaways
Predictive, Not Reactive
2-3 day early warning vs. post-failure alerts
Adaptive Intelligence
Learns from technician feedback to reduce false alerts by 75%
Practical Deployment
Non-invasive, offline-first, works with existing infrastructure
Ready to See It In Action?
We're seeking pilot sites and collaborators to validate our predictive intelligence system in real-world rural infrastructure settings.
Pilot Sites
Rural cold storage facilities, vaccine depots
Implementation Partners
NGOs, healthcare organizations, infrastructure operators
Research Collaboration
Academic institutions for field validation
Email: partnerships@graminguard.tech