In today’s digital and remote work landscape, ensuring security and compliance with workplace policies presents new challenges. As companies shifted to remote working environments, the need for effective solutions to monitor and protect their virtual setups became critical. The sudden transition introduced vulnerabilities and inefficiencies that traditional systems couldn’t address. Proktor emerged as an innovative solution to these challenges, effectively bridging the gap between agile practices and machine learning (ML)-enabled system development.
Our client operates within the technology sector, specializing in creating solutions for secure and efficient workflows. As a company adapting to the remote work revolution, they faced unique challenges, particularly around monitoring employee activities without compromising privacy, ensuring security compliance, and maintaining productivity. These challenges highlighted the urgent need for a robust, AI-powered system capable of addressing these requirements in real time.
The primary objective was to develop a secure, scalable, and privacy-conscious solution that seamlessly integrates into the remote work environment. Proktor was envisioned to:
Proktor was developed as an advanced ML-enabled workplace proctoring system, designed to create a secure virtual workspace for employees. Our solution utilized cutting-edge technologies such as edge AI, computer vision, and sophisticated data management techniques to monitor and analyze employee activities without intruding on personal privacy.
Facilitating ongoing improvements.
Enabling seamless communication between teams.
Allowing for flexibility in response to evolving client needs.
The Planning Phase
The development of Proktor began with a comprehensive understanding of the client’s needs. Through multiple stakeholder meetings, we identified three core challenges:
Based on these findings, the team at Founding Minds Software (FMS) conceptualized a framework that would address these issues effectively.
Ensuring robust monitoring while respecting privacy was a critical challenge. The team implemented advanced image anonymization techniques, where raw video data was transformed into anonymized representations. This innovative approach safeguarded user privacy while maintaining functionality.
Delivering sub-second responses to potential threats required optimizing algorithms for speed and accuracy. Our edge AI technology allowed local processing of data, reducing latency and enhancing performance.
Variations in lighting, camera quality, and workspace setups posed significant challenges. We developed adaptive computer vision models capable of functioning reliably across a wide range of conditions.
Many clients relied on legacy IT systems with limited flexibility. To address this, we designed Proktor with a modular architecture that seamlessly integrated with existing infrastructures, ensuring minimal disruption.