Your cold room
will breach in
40 minutes.
AI that learns the pattern of each equipment. It used to be an alarm. Now it is a prediction with lead time, probable cause and suggested action.
An alarm that fires after the breach is not intelligence, it is an audit.
Traditional monitoring systems fire when the limit has already been crossed. At that point, the batch is compromised, the non-conformity has already happened and all that is left is to document the loss.
We woke up on Monday with the alarm firing since Saturday night. The entire cold room was compromised. With Predict, we would have received the alert 40 minutes ahead with a suggested action, still on Saturday, still in time.
Three layers of AI. One reliable prediction.
A layered architecture: point anomaly detection, temporal drift analysis and a predictive breach model. Each layer activates at different moments of the failure pattern.
Peak detection
Layer 1: identifies readings that are statistically anomalous relative to the equipment historical pattern. Active from the first day of data.
Drift detection
Layer 2: analyzes the trend of the last N readings. Detects gradual warming patterns before they cross the limit. Far more sensitive than a fixed threshold.
Breach prediction
Layer 3: a model trained on the historical pattern of that specific equipment. It projects the future temperature with an uncertainty band and estimates the breach ETA.
Suggested action
Based on the equipment type, the history of similar failures and the probable cause, the system suggests the action: check the compressor, clean the condenser, call maintenance.
AI that learns from every hospital, not from generic data.
Model per equipment
Each cold room, freezer or climate-controlled room has its own model, trained on the historical behavior of that specific equipment. There is no one-size-fits-all.
- Continuous retraining
- Adaptation to seasonal changes
- Customization by load type
ETA and probability
Each prediction comes with an ETA (time to breach), probability and confidence interval. It is not a binary alarm, it is a quantified estimate.
- ETA with confidence interval
- Breach probability
- Calculated urgency level
Probable root cause
Based on the degradation pattern, the system infers the most probable cause: a compressor problem, an open door, a load overload, a power failure.
- 5 root-cause categories
- History of similar causes
- Suggested action per cause
Explainability
For each prediction, the system explains which variables contributed most to the alert. The head pharmacist (RP) understands why, not just the alert.
- Feature importance per prediction
- Variable contribution chart
- Auditable report
Integration with alarm systems
Predictions notify all subscribers at the same time: push in the DROME app (iOS, Android, web), WhatsApp, SMS, voice call and email.
- Push in the DROME app
- WhatsApp, SMS, voice call and email
- Webhook for your own systems
No manual configuration
The model learns the pattern of each equipment automatically. There is no threshold to configure, no rules to define. It detects peaks from day one.
- Zero initial configuration
- Learns from historical data
- Improves with each event
Auditable predictions, a complete evidence trail.
Each prediction is stored with a timestamp, the variables used, the probability and the action taken. ANVISA and ONA auditors find not just the alarm history, but the history of predictions and decisions.
Frequently asked questions about DROME Predict.
For a live demo of the predictions, talk to our team.
Want to see the DROME Predict module in action?
Book a demo with our team and see live predictions in your own environment.