Contactless check-in: IoT device management for Seamless Air Travel
 

An air travel solutions provider partnered with Gstamp to develop a fully contactless check-in experience at airports in an African nation. The goal was to enhance passenger safety, reduce wait times, and improve operational efficiency through advanced IoT integration. 

Our team of five engineers designed, developed, delivered a robust software solution that seamlessly integrates with key airport devices: passport readers, face recognition cameras, and biometric scanners. The system authenticates passengers automatically, triggering check-in upon successful identity verification. Dashboard provides airport staff with real-time visibility into the passenger list and their check-in status, streamlining operations and improving passenger flow. Integration with the Digital Control System for managing IoT devices helps to remotely monitor the device status. The initial development was accelerated using simulators, allowing rapid iteration before deploying with actual hardware. 

The entire solution was implemented in six months demonstrating fast, reliable digital transformation showcasing the power of IoT-driven automation. 

AI/ML model for ransomware detection solution 

A product division of an Indian software services company partnered with a global cloud enterprise to co-develop a cutting-edge ransomware detection solution. Recognizing that traditional backups are no longer sufficient—since ransomware often lies dormant until it infiltrates backup images—the teams set out to design a smarter defence mechanism. 

The software solution leveraged an AI/ML model that scanned every file across backup images. The model was trained using attributes such as file size, type, access history, and entropy. By monitoring changes in file entropy across successive backups and detecting sudden spike in entropy—a sign of encryption—indicated an anomaly and a potential ransomware indicator. The AI model continuously learned from new patterns and improved its detection accuracy over time. This proactive approach isolates suspicious backup images and those will not be used for recovery. Several key engineers from the project have since joined Gstamp.  

AI modelling reduces downtime and removes malware which is not possible with traditional signature-based tools.

Lift and Shift Migration 

A global enterprise’s IT division needed to migrate server workloads and petabyte-scale data from multiple on-premises sites to the cloud. The heterogeneous environment, consisting of various Linux and Windows OS versions, posed challenges—these systems couldn’t boot directly as cloud virtual machines. Moreover, replicating vast amounts of data over the network was not feasible. 

To address this, a robust server migration software was deployed. It enabled seamless workload migration by replicating entire servers and injecting necessary device drivers to ensure bootability in the cloud. For the large-scale data, an encrypted offline migration strategy was adopted using a dedicated storage appliance. The appliance ensured data security by leveraging customer-owned encryption keys. Post-transfer, it was securely reimaged following NIST guidelines, allowing reuse across multiple migration cycles without compromising data integrity or compliance. 

This lift-and-shift approach reduced migration time, ensured operational continuity, and upheld stringent security standards, making it a repeatable migration model.