Custom cold chain monitoring
Appfront builds custom cold-chain monitoring platforms for transport and logistics companies that carry temperature-sensitive cargo. Real-time temperature, humidity, CO2 and position of cool crates, trailers and containers, combined with edge AI for anomaly detection and fail-over routing as soon as an excursion threatens. Demonstrably compliant with GDP for pharma, HACCP for food and the ATP Agreement for international refrigerated transport.
What is a cold-chain monitoring platform?
A cold-chain monitoring platform is a dedicated, continuous monitoring layer for temperature-sensitive goods in transit. Sensors in cool crates, on pallets, in trailers and in shipping containers measure temperature, humidity, CO2 levels, door openings and GPS position. These readings flow via NB-IoT, LoRa, BLE or satellite to a central platform that stores and visualises them, checks them against thresholds and, when extended with AI, predicts when an excursion is likely. The result is a verifiable chain of custody from loading point to recipient, with an audit trail for each shipment.
For many hauliers, this is a logical next step after choosing a TMS and planning API. A TMS organises orders and drivers, a planning API allocates routes, but the actual condition of the cargo en route often remains in standalone sensor loggers that are only read out on receipt. A cold-chain platform closes that gap: it makes the condition of the cargo as continuously visible as the position of the vehicle. For businesses already running their own transport planning API, the cold-chain platform connects to it naturally — fail-over routing in the event of an excursion can consult the existing VRP model directly.
Appfront designs these platforms so they do not become black boxes. Sensor data, model versions, calibration history and evaluation metrics can be inspected by your own quality team and auditor. The integrations run over REST and webhooks, documented to the OpenAPI standard, so new sensor suppliers, customer portals or a customs system can connect without trouble. You can read more about this integration approach on our page on smart API integrations, and we describe the wider context for the whole sector at getting transport software built.
Continuous temperature, not end-point measurement
Sensors transmit readings throughout the entire journey, not only on delivery. Excursions become visible as they happen, not in a report afterwards, so the team can intervene before the legal limit is breached.
Edge AI for anomaly detection
Models learn the normal temperature profile per vehicle, cargo and season. A refrigeration unit fault is recognised before the threshold value is reached, and the planner receives an actionable alert with a suggested diversion route straight away.
Open integration with your TMS and eCMR
The platform runs alongside Transics, MendriX or Carrierweb. Temperature alerts flow back into the TMS, deviations appear on the digital consignment note via TransFollow, and the receiver tracking link shows the live graph to the customer.
Our development process for a cold-chain platform
A good cold-chain platform does not start with sensors or dashboards, but with the people who monitor the chain every day: quality managers, planners, drivers and the QP for pharma. We spend several days alongside them to establish which deviations are actually occurring now, which legal limits apply and which actions should follow an alert. Only then do we select the sensors, network, data model and integrations. In this way the platform becomes not a collection of loose meters, but a workable control instrument that the team trusts.
We join the quality and planning processes, map out legal limits (GDP, HACCP, ATP) and determine which measurements, thresholds and retention periods are mandatory.
For each cargo and route type we determine the right sensor (NB-IoT, LoRa, BLE, satellite) and data model. For pharma we validate the chain in line with GDP.
Iterative development with a pilot on a limited fleet. Edge AI models are first trained in shadow mode on your own data before they are permitted to generate active alerts.
Controlled rollout per depot or customer segment, with monitoring of false positives, sensor failures and model drift. Ongoing management and expansion follow thereafter.
What a cold-chain platform concretely delivers
Every implementation is set up specifically for the type of cargo, the routes and the existing systems. Below are the components we see consistently — to be combined as suits your scenario. For the AI layer we work to the broader approach described on AI development, adapted to the specific requirements of cold-chain logistics.
Temperature, humidity & CO2
Multi-parameter sensors record temperature, relative humidity, CO2 levels and light exposure at pallet or crate level. For products with dual-band specifications (e.g. +2 to +8 °C for vaccines), each parameter is monitored and audited independently.
GPS & position tracking
Real-time position of trailers, containers and individual pallets, including geofence events for loading, unloading and transhipment zones. For ocean cargo, the platform switches automatically to satellite as soon as 4G drops out, so there is no gap in the chain.
Anomaly detection & forecasting
Edge AI or cloud AI models learn the normal temperature profile and detect drift from the baseline. An excursion is predicted before the threshold is reached, so the planner can intervene or deploy fail-over routing.
Fail-over & alternative routing
If an excursion looms, the platform immediately consults the VRP model to show alternatives: the nearest transhipment point with cold-storage capacity, an alternative route with a faster ETA, or an ad hoc swap with another trailer in the network.
Audit trail & compliance reports
A report is generated automatically for each shipment: temperature profile, mean kinetic temperature, any excursions and the corresponding corrective actions. For pharma, it can be sent directly to the QP; for food, it serves as HACCP evidence for the receiver.
Receiver tracking & customer portal
Recipients receive a personalised link showing the live temperature graph, GPS position and estimated time of arrival. On delivery, the driver collects the digital signature via eCMR, and the temperature evidence is automatically attached to the consignment note.
Typical scenarios in practice
A cold-chain monitoring platform looks different in each sector. We see a number of scenarios recurring structurally, and for each we have a recognisable approach that takes into account the specific rules, restrictions and parties common in that market. Read also our broader vision of this sector in transport & logistics.
Food logistics (meat, dairy, fresh produce)
Distributors and carriers of fresh food where HACCP evidence accompanies every delivery. The platform monitors per pallet or per compartment, records door openings across multiple drops and gives the receiver a readable temperature report. Excursions during waiting times at the border or during inspections are mapped out, so insurance claims, complaints and recurring problem routes stop being a matter of debate.
Pharma cold chain & GDP
3PL providers for pharmaceutical wholesale, vaccine distribution and clinical trial logistics. The platform implements GDP-EU 2013/C 343/01 with validated sensor calibration, an audit trail per shipment, mean kinetic temperature calculation and controlled QP access. For the broader strategic preparation in this domain, we offer a separate AI workshop on pharma cold chain & GDP.
Bulb & ornamental horticulture export
Bulb exporters and flower transport companies, where ethylene sensitivity and humidity bands matter just as much as the temperature itself. The platform monitors humidity and CO2 per crate environment and connects with customers worldwide through a multilingual portal, plus customs data via Portbase for sea and air exports.
Chemicals & vaccine distribution
Transport of chemicals, biologicals and vaccines with strict temperature restrictions, ADR requirements and, for vaccines, ultra-cold-chain routes down to -70 °C. The platform combines specialised sensors with fail-over routing to certified dry-ice depots and complies with both ADR and GDP-EU. Customer portals display batch-level lot data and the complete chain history.
Technology we use
We tailor the technical stack to your scale, cargo type and existing IT landscape. Where possible we use proven open-source components, certified sensors where required, and our own models where they add real value. The platform is built in a language and framework that your own team can keep managing: no vendor lock-in to proprietary sensor clouds, no secret AI sauce. For the wider TMS and planning context, we often work on the foundation we describe at custom transport software development.
Why choose Appfront for your cold chain platform?
Appfront has been building custom software for the transport and logistics sector for years, from TMS extensions to route planning APIs and sensor platforms. We know that a cold chain platform only becomes valuable when planners and QPs trust its alerts, that an AI model for anomaly detection only works when it is trained on your own cargo types, and that a sensor stack only delivers value when its data also lands in your TMS and customer portal. That is why we start with your quality and planning processes, not with an empty dashboard.
With every integration we write clear documentation and OpenAPI contracts so that your own team or a future supplier can understand and manage what is running. No black box, just transparent code and clear agreements on sensor calibration, model versions, evaluation metrics, monitoring and maintenance. That keeps you independent and in control of your cold chain logic at all times, which matters more and more legally under the AI Act, particularly for algorithms that intervene in routing or compliance actions.
You work with a dedicated point of contact who understands the sensor, AI and integration sides alike. This keeps lines short, prevents miscommunication between the sensor supplier, TMS partner and your own IT, and speeds up decisions when choices must be made during the build around networks, models or fail-over scenarios. The cold chain platform is therefore not only technically sound, but also fits your actual working processes.
See also our broader approach to AI development and how we handle API integrations in general. Cold chain monitoring is a specialised application of both: one where the physical world is watched directly and where the impact of a fault is immediately measurable in spoiled cargo, discarded vaccines or a failed audit.
- Experience with IoT platforms in production for transport
- Specialist in cold chain and pharma logistics software
- Proprietary anomaly detection models trained on your historical trip data
- Experienced with Transics, MendriX and Carrierweb adapters
- Sensor-agnostic: Sensitech, ELPRO, Tive, Berlinger and proprietary sensors
- Multi-network: NB-IoT, LoRa, BLE, satellite
- OpenAPI 3 contracts, no undocumented endpoints
- Secure by default: API keys, scoped permissions, rate limiting
- EU-resident hosting (Azure West Europe, AWS Frankfurt, Dutch cloud)
- EU GDP, HACCP, ATP and AI Act preparation included
- A fixed point of contact, no account managers passed around
- Ongoing maintenance, monitoring and model updates
Compliance: GDP, HACCP, ATP, GDPR and AI Act
Cold-chain data is sensitive. Beyond its operational value for your own team, it also serves as evidence for regulators, customers and insurers. For pharma, the European GDP guidelines (GDP-EU 2013/C 343/01), Annex 11 for computerised systems and, for exports to the US, 21 CFR Part 11 apply. For food, the platform operates in line with HACCP and the European guidelines on temperature monitoring of foodstuffs during transport. For international road refrigerated transport, the ATP Agreement applies, setting requirements for the refrigerated vehicles used and their monitoring. Appfront builds to the OWASP ASVS and configures the platform so that all these regulations are demonstrably safeguarded.
We host within EU residency by default — Azure West Europe, AWS Frankfurt or a Dutch cloud of your choice — and encrypt data in transit and at rest. Sensor data, GPS routes and driver details fall under the GDPR; we document the data flows so your record of processing activities remains complete, and only functions that genuinely need access are granted it. For pharma applications, we deliver a validated system, with Installation Qualification, Operational Qualification and Performance Qualification protocols, calibration history per sensor and a demonstrable audit trail of key actions.
For the AI components — anomaly detection and excursion prediction — we supply model cards, a description of training data provenance and evaluation metrics. Under the EU AI Act, cold-chain monitoring typically falls under limited risk, but we provide the full documentation up front so that any tightening of regulation does not catch you off guard. For optional use of driver-level fail-over routing, we deliver an impact assessment and human-oversight steps. See also our information security policy and CVD policy for the full picture.
- Compliant with GDP-EU 2013/C 343/01 — IQ/OQ/PQ protocols
- Annex 11 & 21 CFR Part 11 for pharma exports
- HACCP reporting per shipment for food logistics
- ATP Agreement compliant for international refrigerated transport
- GDPR-compliant processing of driver and cargo data
- EU residency: Azure West Europe, AWS Frankfurt or Dutch cloud
- AI Act: model cards, dataset provenance, evaluation metrics
- Compatible with EU mobility data spaces (Gaia-X)
- Validated sensor calibration with history
- Mean kinetic temperature calculation per shipment
- eCMR-compliant integration via TransFollow
- Encryption in transit (TLS 1.2+) and at rest
- Audit logs with traceable data flows
- Documentation for your record of processing activities
Frequently asked questions about cold chain monitoring
Answers to the questions we receive most often about custom cold-chain monitoring platforms for transport and logistics.
Cold chain monitoring is the continuous, automated tracking of temperature, humidity, CO2 levels, door openings and the location of temperature-sensitive goods in transit. Sensors in refrigerated crates, on pallets, in trailers and in sea containers send readings to a central platform that detects deviations, issues alerts and — when extended with edge AI — predicts when an excursion is likely. The result is a demonstrable, traceable chain from loading point to recipient, compliant with GDP for pharma, HACCP for food and the ATP Agreement for international refrigerated transport.
That depends on the type of cargo and the route. For short-haul road transport we often choose NB-IoT or LTE-M for low power consumption and good coverage across Europe. For in-trailer loggers in dense shade, LoRaWAN or a mesh of Bluetooth Low Energy beacons with a gateway on the trailer works well. For ocean cargo and sea freight we move to satellite (Iridium, Globalstar or Inmarsat), because 4G largely drops out at sea. For air freight we use ULD-certified loggers that comply with the prevailing aviation guidelines. We determine the mix per project based on battery requirements, data quality and cost per shipment.
An edge AI or cloud AI layer learns the normal temperature, humidity and cooling-cycle pattern per vehicle, cargo type and season. When a sensor reading slowly drifts from the baseline (for example, a cargo space warming by a few degrees an hour because of a faulty refrigeration unit), the model raises an alert before the legal temperature limit is breached. For pharmaceutical cold chains we use models that account for mean kinetic temperature, not just absolute thresholds. When an excursion is predicted, the platform can suggest fail-over routing, for instance diverting to the nearest certified transhipment point.
We build the platform as an independent service that communicates with your existing systems via REST, webhooks and message queues. For Transics, MendriX, Carrierweb or any other TMS, we supply adapters that exchange trip context, temperature setpoints and alerts. For eCMR, we integrate with TransFollow so that deviations are carried through the digital consignment note and can be demonstrated on delivery. For the customer portal, we generate a recipient tracking link with a live temperature graph and proof of delivery. ERP integrations run through the common connectors (SAP, Microsoft Dynamics, Exact, AFAS), so batch data, lot numbers and serial numbers stay visible.
Costs are determined by the number and type of sensors, the network subscription (NB-IoT, LoRa, satellite), the scale of your fleet, the number of systems to integrate, and the extent to which you want your own AI models for anomaly detection or excursion prediction. Compliance requirements (GDP, HACCP, ATP), customer portal functionality and ongoing maintenance also play a role. After a no-obligation analysis, we prepare a clear quote with scope and phased planning, with no surprises later on.
Yes. We host within EU residency and build in line with OWASP ASVS. For pharmaceutical cold chains we implement the requirements of EU GDP 2013/C 343/01: validated sensor calibration, an audit trail of all measurements, controlled access and retention periods aligned with Annex 11 and, for export to the US, 21 CFR Part 11. For food logistics we work in accordance with HACCP and the relevant EU directives on temperature monitoring. For the AI components (anomaly detection, excursion prediction) we supply model cards, training data provenance and evaluation metrics in line with the AI Act. Cold chain monitoring falls under limited risk, but we provide the full documentation in advance regardless.
Yes. We regularly integrate with existing sensor fleets from suppliers such as Sensitech, ELPRO, Tive or Berlinger. For a fleet changeover we carry out a data migration that preserves historical measurements, so compliance audits continue seamlessly. For companies running on a closed SaaS platform who wish to switch, we review the current set-up and draw up a migration plan that does not disrupt ongoing shipments.
A custom cold chain platform pays off most when you handle temperature-sensitive shipments every day and standard SaaS loggers no longer suffice. Typical profiles: food logistics (meat, dairy, fish, fresh produce), pharma cold chain (vaccines, biologicals, clinical trial samples), 3PL providers for pharmaceutical wholesale, bulb exporters, chemical transport with temperature restrictions, and specialists in vaccine distribution. For smaller fleets, a focused set of sensor integrations and a lighter dashboard may be enough.
Talk to us about your cold-chain platform
Tell us which type of cargo you carry, which TMS you use, which sensors you already run and which compliance you already manage, whether GDP, HACCP, ATP or a combination. We're happy to think along with you on scope, sensor and network choices, the AI layer and alignment with your existing suppliers. A no-obligation first conversation gives you a clear picture of what is feasible for your situation and which quick wins you can secure straight away.