One sensor, one number
Conventional reefers control on return-air temperature. Hotspots near the doors or deep in the stow go unseen.
AI cargo environment management for reefers
The cold chain that knows what's inside.
Reefer containers hold a set temperature. PerishFlow understands the cargo — sensing every zone, predicting deterioration before it's visible, and acting to protect shelf life in transit.
- 07
- Signals sensed
- 08
- Launch commodities
- 04
- Control modules
Reefer MSKU-40R · Mango · Day 9 of 14
Cargo condition console
Simulated data
Front · reefer unitDoors
A111.9°
B112.1°
C111.9°
D112.1°
A212.0°
B212.0°
C212.1°
D211.9°
Set point 12.0°C · airflow front → doors
- Humidity
- 88%
- CO₂
- 0.86%
- Ethylene
- 0.40 ppm
Predicted shelf-life risk12/100 · Nominal
- T+18Zone D2 back in band — response verified
- T+13Ethylene scrubber engaged
- T+10Airflow boosted to zone D2
The problem
A container knows its set point. Not the condition of its cargo.
Quality loss in transit is usually discovered at the destination — when it is too late to do anything but file a claim.
Invisible chemistry
Ethylene build-up and shifting CO₂ / O₂ accelerate ripening long before a single piece of fruit looks different.
Found too late
Without a record of cargo condition, damage surfaces at discharge — and becomes a dispute instead of a decision.
~14%
of the world's food is lost between harvest and retail — much of it perishables moving through cold chains that cannot see the cargo they carry.
Source: FAO, The State of Food and Agriculture 2019.
The system
Sense → Understand → Predict → Act
A closed loop that turns a refrigerated box into an actively managed cargo environment.
- 01
Sense
Distributed probes across multiple cargo zones — not one return-air sensor — capture the real environment around the fruit.
- Temperature
- Humidity
- CO₂
- O₂
- Ethylene
- Airflow
- Shock
- 02
Understand
Raw readings are interpreted in context: what the cargo is, when it was harvested, and how long it still has to travel.
- Produce type
- Harvest date
- Voyage duration
- 03
Predict
Models estimate shelf-life risk, ripening acceleration, hotspots and condensation before any damage is visible.
- Shelf-life risk
- Ripening
- Hotspots
- Condensation
- 04
Act
Control modules respond to the prediction — then PerishFlow measures whether the response actually worked.
- Air circulation
- Humidity
- Ethylene filtration
- CO₂ management
| Capability | Conventional reefer | PerishFlow |
|---|---|---|
| Knows its set temperature | ||
| Measures conditions in every cargo zone | ||
| Tracks ethylene, CO₂ and O₂ around the produce | ||
| Understands what the cargo is and how old it is | ||
| Predicts deterioration before it is visible | ||
| Acts on a specific zone and verifies the result |
Commodities · Reefer 45R1
Every cargo spoils differently.
PerishFlow starts with high-value fresh produce, each with its own temperature band and control priorities. Seafood, dairy and pharmaceuticals follow.
Pineapple
Indicative carriage profile- Temperature band
- 7 – 10 °C
- Relative humidity
- 85 – 90%
- Ethylene profile
- Low producer
Too cold and internal browning appears days after discharge — invisible at the port.
PerishFlow control priorities
- 01Chilling-injury avoidance
- 02Humidity balance
- 03Zone uniformity
- Chilling-sensitive
Ranges are indicative and vary by cultivar, maturity and voyage length.
Business model
Hardware in the box. Intelligence on every voyage.
Built for exporters, shipping lines, logistics operators and importers — with revenue that compounds as the fleet and dataset grow.
Hardware
Sensing and control modules, sold or leased per container.
Per-shipment analytics
AI condition monitoring priced per voyage.
Dashboard subscriptions
Live and historical views for exporters and importers.
Fleet analytics
Enterprise insight across lanes, vessels and seasons.
Cargo-condition reports
Evidence for claims, insurers and quality disputes.
Platform APIs
Condition data piped into TMS, ERP and trading systems.
Roadmap
From first sensor to fleet scale.
PerishFlow is pre-MVP. We're building the data foundation first — because good predictions start with honest measurements.
- 01Loading now
Sensing & baseline data
Build the multi-zone sensing infrastructure and create baseline datasets from real shipments.
- 02Queued
Predictive models
Train deterioration and shelf-life models on commodity-specific data.
- 03Queued
Active control
Integrate airflow, humidity, ethylene and atmosphere control with closed-loop verification.
- 04Queued
Commercial scale
Scale with shipping lines, exporters and logistics partners across key trade lanes.
Open the doors
Let's protect cargo together.
We're speaking with investors, exporters, logistics operators and research partners who want to shape the first cargo-aware cold chain.
- Pilot a sensing kit on a live lane
- Share commodity expertise or data
- Discuss investment
Prefer email? info@aflatus.com