The container reports normal
Cargo quality can deteriorate while the reefer continues operating exactly as configured.
Cargo Environment Management System
The Autonomous Environment Management System for Perishable Cargo
Turning refrigerated containers into intelligent, predictive and actively managed cargo environments — so quality loss is anticipated in transit rather than discovered at the destination.
- Sense
- Understand
- Predict
- Act
Pineapple · 20 pallets
Colombo → Jebel Ali · day 9 of 21
Cargo zone temperature
reefer set 7.0 °C · cargo ceiling 7.5 °C
- Zone 17.4 °C
- Zone 27.3 °C
- Zone 38.2 °C
- Zone 47.5 °C
SenseUnderstandPredictAct
Zone 3 · 8.2 °C · RH 88% · CO₂ 4.1%
Distributed probes reporting across four cargo zones
The Problem
Refrigerated containers control temperature. They don't understand the cargo.
Fresh produce stays biologically active for the whole journey. It continues to respire, ripen, lose moisture and respond to changes in temperature, humidity and atmospheric gases — none of which a set point describes.
The exporter finds out late
The problem is usually discovered at the destination, once the doors are opened and the cargo is graded.
The loss is already booked
By that point the commercial loss has occurred. There is nothing left to intervene in.
Spoilage is usually detected too late to prevent it.
A problem experienced firsthand
A pineapple shipment arrived with internal quality deterioration.
The fruit could appear acceptable externally while significant internal browning and quality loss existed inside. That shipment exposed a critical information gap in refrigerated logistics.
Mohamed Ashfaaq · Founder
Why does a container know its set temperature, but not the actual condition and deterioration risk of the cargo inside it?
The Gap in Today's Cold Chain
Every existing category stops short of protecting the cargo.
Traditional reefers
- Primarily maintain temperature and airflow.
- Limited understanding of cargo condition.
Monitoring systems
- Measure and report conditions.
- Usually alert after a threshold is crossed.
- Limited active intervention.
Controlled Atmosphere containers
- Can manage atmospheric conditions.
- Specialized equipment with availability and cost constraints.
PerishFlow AI aims to bridge the gap between monitoring and active cargo protection.
Introducing PerishFlow AI
An AI-powered Cargo Environment Management System.
A self-contained system being designed for deployment with refrigerated cargo — continuously understanding environmental conditions, predicting quality risks and activating appropriate onboard control modules.
- 01
Sense
Collect environmental and cargo data across the container, continuously rather than at checkpoints.
- 02
Understand
Interpret the cargo's condition by combining what is measured with what is being carried.
- 03
Predict
Estimate deterioration and shelf-life risk before the quality loss becomes visible.
- 04
Act
Activate configured environmental controls, then measure whether the response worked.
Not just monitoring. Predicting and actively managing cargo risk.
What It Measures & What It Concludes
From raw sensor data to cargo decisions.
The AI layer combines cargo characteristics with the environmental history of the shipment to estimate how conditions are affecting product quality.
Distributed sensing
- Temperature across multiple cargo zones
- Relative humidity
- CO₂ and O₂
- Ethylene where relevant
- Airflow
- Shock and vibration
- Door events
Cargo intelligence layer
- Produce type and variety
- Harvest date and maturity information
- Initial pulp temperature
- Voyage duration and route
- Historical environmental exposure
Instead of
“Temperature is 8.2 °C”
PerishFlow AI aims to answer
“What does 8.2 °C mean for this specific cargo?”
Potential outputs
- Remaining shelf-life estimate
- Ripening acceleration risk
- Hotspot detection
- Condensation and mould risk
- Gas accumulation risk
- Predicted quality deterioration
Cargo Intelligence, Illustrated
Every commodity carries a different definition of “safe”.
Select a cargo to see the band it must be held within, the failure modes the intelligence layer is designed to anticipate, and the control modules that would take priority.
Pineapple
Damage develops internally while the exterior still looks acceptable — the failure that started PerishFlow AI.
Carriage band
7 – 10 °C
−2 °C16 °C
Chilling injury below 7 °C
- Relative humidity
- 85–90%
- Ethylene sensitivity
- Low
- Typical voyage
- 18–28 days
What the system watches for
- Internal browning
- Chilling injury
- Translucency progression
- Zone hotspots
Control modules prioritised
- Auxiliary air circulation
- Humidity management
Storage ranges shown are widely published horticultural guidance for shipping fresh produce, included to illustrate the intended system. They are indicative reference values, not PerishFlow AI measurements.
Active Environmental Control
A self-contained approach that leaves the reefer's certified system alone.
PerishFlow AI is intended to actively influence the cargo environment without interfering with the refrigeration container's certified refrigeration control system.
The closed loop
- Monitors
- Predicts
- Responds
- Measures the outcome
Potential control modules
- Auxiliary air circulation
- Ethylene filtration or scrubbing
- CO₂ management
- Humidity management
- Configurable ventilation interfaces where permitted
- Automated control logic on cargo-specific thresholds
Initial Market
Fresh produce first.
A platform designed around cargo intelligence, not a single commodity — starting where biological activity in transit is highest and the commercial loss is most immediate.
Entry commodities
- Pineapple
- Mango
- Banana
- Table grape
- Berries
- Avocado
- Citrus
- Cut flowers
Each of these ships under a different band, a different set of failure modes and a different control priority — which is precisely what a fixed set point cannot express.
Future applications
- Seafood and meat
- Dairy
- Pharmaceutical and temperature-sensitive products
Business Model
Hardware + intelligence + recurring revenue.
Hardware
Sale or leasing of PerishFlow AI units.
Per-shipment intelligence
AI monitoring and predictive analytics priced by shipment.
Subscription dashboard
For exporters and logistics companies.
Enterprise fleet analytics
Cross-shipment patterns for operators at scale.
Cargo-condition reports
Historical records and claims investigation data.
Platform API
Future integration with logistics platforms.
Make advanced cargo intelligence accessible without requiring every exporter to buy a specialized container.
Why Now
Three technologies are converging.
- AI can interpret complex environmental patterns.
- Industrial IoT sensors are becoming more accessible.
- Cold-chain operators increasingly require visibility and traceability.
Logistics is moving from passive transport toward intelligent, data-driven supply chains.
PerishFlow AI sits at the intersection of AI, logistics, food security and climate technology.
Why Abu Dhabi / Hub71
A strategic environment to build and validate.
- Logistics infrastructure and global trade routes
- Food security initiatives
- AI and deep-tech ecosystem
- Potential access to ports, logistics operators and strategic partners
Our commitment
Build PerishFlow AI’s engineering, data and commercial capabilities from Abu Dhabi while targeting global refrigerated logistics.
Roadmap
Sense first, predict second, control third, scale fourth.
PerishFlow AI is pre-MVP. The sequence below is the build plan, not a description of shipped capability.
- Phase 1Current
MVP
- Build distributed sensing and cloud/edge data platform
- Deploy on real fresh-produce shipments
- Create baseline cargo-condition datasets
- Phase 2
Predict
- Develop cargo-specific deterioration and shelf-life models
- Validate predictions against arrival quality
- Phase 3
Control
- Integrate airflow, gas and humidity management modules
- Test closed-loop environmental responses
- Phase 4
Scale
- Commercial pilots with exporters and logistics partners
- Expand to multiple commodities and markets
The Vision
Today, containers transport cargo. Tomorrow, they should understand it.
- Every container knows what is inside it.
- Every shipment understands its deterioration risk.
- The system predicts what will happen next.
- Where safe and permitted, the cargo environment responds before quality is lost.
Making perishable cargo intelligent, predictive and actively protected.
Mohamed Ashfaaq · Abu Dhabi, UAE
Request a conversation
For investors, exporters, logistics operators and research partners interested in early pilots.