Our Portfolio

Project:

Interview Buddy

Overview

Interview Buddy is an advanced AI-powered software solution designed to completely automate the interview process for candidate recruitment. Utilising the latest tools in computer vision, machine learning, and deep learning, Interview Buddy provides a comprehensive analysis of interviewees through multiple modalities, including visual assessment, speech analysis, question-and-answer evaluation, and other crucial recruiting metrics.

Challenges Addressed

♦ Multimodal Analysis: This method evaluates candidates using a combination of visual cues, speech patterns, and responses to interview questions for a holistic assessment.
♦ State-of-the-Art Technology: Utilises the latest advancements in computer vision, machine learning, and deep learning to ensure precise and effective candidate evaluation.
♦ Integrated Web Application: Features a seamless web app with various integrated tools that leverage AI to deliver exceptional results for recruiters.

Outcomes

♦ Simplified Recruitment Process: Interview Buddy automates the interview process, making recruitment more efficient and streamlined.
♦ Enhanced Candidate Evaluation: Provides thorough and accurate assessments of candidates, ensuring recruiters make well-informed decisions.
♦ Cutting-Edge AI Utilization: Demonstrates our commitment to using the latest AI technologies to enhance recruitment.
♦ User-Friendly Experience: The web application integrates seamlessly, providing an intuitive user experience that makes it easy for recruiters to utilise the tool effectively.
♦ Reliable and Effective Results: Delivers reliable and effective candidate evaluations, improving the overall quality of recruitment outcomes.

Interview Buddy showcases our ability to develop sophisticated AI solutions that enhance and simplify recruitment and ensure thorough and efficient candidate evaluations.

Project:

CreatMate

Overview

CreatMate is a cutting-edge mobile app that generates state-of-the-art AI content catering to various creative needs. From high-quality photos in diverse styles to AI-generated videos, reels, shorts, and realistic images, CreatMate empowers users to unleash their creativity efficiently.

Challenges Addressed

♦ Diverse Style Selection: Offers a wide array of styles for users, ensuring personalised and unique content creation.
♦ AI-Generated Media: Produces high-quality photos, videos, reels, shorts, and realistic images using advanced AI technologies.
♦ Versatile Applications: Demonstrates our expertise in Generative AI (GAI), providing innovative solutions across multiple fields and use cases.

Outcomes

♦ Enhanced Creativity: Users can easily create personalised, high-quality content in various styles, enhancing their creative capabilities.
♦ High-Quality Media Production: The app generates exceptional photos and videos, ensuring professional-level content creation.
Innovative AI Solutions: Showcases our commitment to leveraging the latest advancements in AI to deliver versatile and powerful content creation tools.
♦ Broad Application: CreatMate’s versatile nature allows it to be used in numerous fields, from marketing to personal projects, highlighting its wide-ranging utility.
♦ User Empowerment: CreatMate empowers users in every domain to produce top-tier creative content effortlessly by providing state-of-the-art tools.

CreatMate exemplifies our dedication to utilising cutting-edge AI technology to develop innovative, user-friendly tools that enhance creativity and content production across various industries.

Project:

Health Monitor Revamp

Overview

Health Monitor Revamp is a transformative project focused on refactoring and modernizing health monitoring algorithms and processors. Initially developed in TCL, C, and Julia, we meticulously converted these algorithms to the latest Python versions. This project showcases our ability to innovate, automate, and enhance the accuracy of health monitoring systems.

Challenges Addressed

♦ Language Modernisation: Successfully refactored complex algorithms from TCL, C, and Julia into modern Python, ensuring improved performance and maintainability.
♦ Innovation and Transformation: Introduced significant improvements in automation and accuracy, setting new standards in health monitoring technology.
♦ Skill Validation: Demonstrated our exceptional coding and development skills, proving our ability to handle challenging technical tasks.

Outcomes

♦ Enhanced Performance: Python’s modernised algorithms deliver better performance and are easier to maintain, ensuring long-term reliability.
♦ Improved Accuracy: The revamped system provides more accurate health monitoring and improved diagnosis.
♦ Automation: Increased automation within the health monitoring processes, reducing manual intervention and enhancing efficiency.
♦ Legacy System Modernisation: Successfully upgraded legacy systems to modern standards, ensuring they remain relevant and effective.
♦ Proven Expertise: The project validated our technical prowess and commitment to delivering high-quality solutions in the health monitoring field.

Health Monitor Revamp demonstrates our expertise in modernising legacy systems and our commitment to providing high-quality, accurate, and automated solutions in the health monitoring sector.

Project:

Custom Non-English Chatbot

Overview

Our Custom Non-English Chatbot project involved developing and deploying a specialised chatbot tailored to meet the unique needs of a transport business. This bespoke solution ensured effective communication and non-English customer engagement, showcasing our versatility and expertise in chatbot development.

Challenges Addressed

♦ Language Proficiency: Developed the chatbot to fluently understand and respond in the customer’s native language, ensuring smooth interactions and high user satisfaction.
♦ Custom Solutions: Designed and implemented unique features and functionalities tailored for the transportation industry to improve operational efficiency.
♦ Advanced NLP Integration: Utilised advanced Natural Language Processing (NLP) technologies to accurately interpret and respond to user queries, providing reliable and contextually appropriate answers.
♦ User-Friendly Interface: Created an intuitive and engaging user interface to facilitate easy and enjoyable end-user interactions.

Outcomes

♦ Effective Communication: The chatbot’s proficiency in the non-English language ensured seamless communication with customers, improving engagement and satisfaction.
♦ Operational Efficiency: Custom features and functionalities tailored to the transport business’s needs enhanced operational efficiency and streamlined processes.
♦ Accurate Responses: Advanced NLP integration provided accurate and contextually appropriate answers, ensuring reliability and trustworthiness.
♦ Enhanced User Experience: The user-friendly interface made interactions intuitive and enjoyable, fostering positive customer experiences.
♦ Versatile Solutions: Demonstrated our ability to deliver tailored, high-quality solutions that address clients’ specific needs, regardless of language barriers.

The Custom Non-English Chatbot project underscores our commitment to providing innovative, client-specific solutions that enhance communication and operational efficiency, even in non-English language contexts.

Project:

Vendors and Contracts Management System

Overview

Devsort developed an advanced application to automate vendor and contract management, replacing manual processes previously handled in Excel. The system offers web and desktop versions, efficiently manages external vendors and contractors, and provides seamless integration capabilities.

Challenges Addressed

♦ Design Consistency: Ensured consistent design and functionality across web and desktop platforms.
♦ Data Validation: Implemented robust data validation to maintain accuracy and reliability.
♦ Database Optimisation: Optimised the database for better performance and scalability.
♦ Cross-Platform Testing: Conducted thorough testing to ensure smooth operation across all platforms.

Outcomes

♦ Enhanced Efficiency: Developed a fully functional application that automates vendor and contract management, significantly improving efficiency.
♦ Accuracy Improvement: The system’s automated processes reduced errors, enhancing the accuracy of vendor and contractor management.
♦ User-Friendly Design: Ensured a consistent and intuitive user experience across web and desktop versions.
♦ Seamless Integration: Provided integration capabilities, allowing for smooth data flow and interoperability with other systems.
♦ Client Satisfaction: Delivered a solution that effectively meets client requirements and supports their operational needs.

This project demonstrates our ability to develop comprehensive automation solutions that replace manual processes, improve operational efficiency, and ensure data accuracy across multiple platforms.

Project:

HR and Consultants Management System

Overview

Devsort revolutionised HR and consultant management by developing a centralised database, offering insightful analysis, and providing alerts and notifications for workforce engagement. The system ensures robust data security and integrity while featuring user-friendly interfaces for managing staff and consultant data.

Challenges Addressed

♦ Interface Design: Creating intuitive and user-friendly interfaces for desktop and smartphone versions.
♦ Data Security: Implementing industry-standard data security measures to safeguard sensitive information.
♦ Website Integration: Ensuring seamless system integration with existing website infrastructure for comprehensive access and functionality.

Outcomes

♦ Enhanced Efficiency: The user-friendly app significantly improved HR’s efficiency and accuracy in managing data.
♦ Improved User Experience: The intuitive interface design enhanced the overall user experience for staff and consultants.
♦ Robust Data Security: The system’s stringent data security measures ensured the protection and integrity of sensitive information.
♦ Centralised Management: The centralised database facilitated streamlined management and insightful analysis of workforce data.
♦ Real-Time Alerts: The alert and notification features improved workforce engagement and responsiveness.

This project demonstrates our expertise in developing comprehensive HR management solutions that enhance efficiency, data security, and user experience. It also showcases our commitment to innovative and user-centric design.

Project:

Budget Management System

Overview

Devsort developed a comprehensive module for managing, analysing, and generating budget reports across various departments and time periods. The system allows users to customise budget analysis and reports, providing valuable insights for informed decision-making.

Challenges Addressed

♦ Interface Design: Created an intelligent and user-friendly data entry and display interface.
♦ Data Accuracy and Integrity: Ensured high data accuracy and integrity levels throughout the system.
♦ Cross-Platform Integration: Integrated web and desktop versions to provide a seamless user experience.

Outcomes

♦ Enhanced Budget Management: Developed a reliable web application that significantly improves budget management efficiency and accuracy.
♦ Customisable Reports: Enabled users to customise budget analysis and generate detailed reports tailored to their needs.
♦ User-Friendly Experience: Designed an intuitive interface that enhances the overall user experience.
♦ Valuable Insights: Provided valuable insights through comprehensive data analysis, aiding in better decision-making.
♦ Cross-Platform Consistency: Ensured a seamless and consistent experience across web and desktop versions.

This project highlights our expertise in developing robust financial management solutions that enhance efficiency, accuracy, and user experience. It also shows our dedication to providing high-quality, user-centered applications.”

Project:

Refactoring Legacy Algorithms for Parkinson’s Detection

Overview

In this project, we modernised a suite of legacy algorithms originally developed in TCL, Julia, C/C++, and Java, transitioning them to the latest version of Python. These algorithms are essential for detecting various Parkinson’s disease parameters in patients using data collected from wrist-mounted data-logger wristwatches. The refactoring process significantly enhanced code maintainability, performance, and integration with modern data analysis tools, ensuring that the algorithms remain effective and efficient in their application.

Challenges Addressed

♦ Language Transition: Converting algorithms from multiple languages (TCL, Julia, C/C++, Java) to Python while preserving functionality and accuracy.
♦ Performance Optimisation: Improving the performance of the algorithms to handle large datasets efficiently.
♦ Maintainability: Refactoring the code enhances readability, reduces complexity, and facilitates more accessible future updates.
♦ Integration: Ensuring seamless integration with modern data analysis tools to leverage advanced analytical capabilities.

Outcomes

♦ Enhanced Maintainability: The transition to Python resulted in more maintainable and readable code, making it easier for developers to understand and modify.
♦ Improved Performance: Optimisation techniques applied during refactoring led to faster execution times and more effective handling of larger datasets.
♦ Better Integration: The refactored algorithms integrate seamlessly with modern data analysis tools, enabling more sophisticated analysis and visualisation.
♦ Increased Accuracy: The updated algorithms detect Parkinson’s disease parameters accurately, contributing to better patient monitoring and research outcomes.
♦ Future-Proofing: By adopting Python, a widely used and supported language, the algorithms are now future-proofed, ensuring long-term usability and support.

This project demonstrates our capability to modernise complex systems, improve their performance, and ensure their continued relevance in a rapidly evolving technological landscape.

Project:

Research and Development of Movement Disorder Algorithms Using Machine Learning

Overview

This project focused on the research and development of innovative algorithms to detect and analyse movement disorders in patients. By enhancing existing algorithms and integrating various machine learning techniques, we aimed to improve accuracy and predictive capabilities significantly. The project’s primary objective was to advance our understanding of movement disorders and provide more effective monitoring and diagnostic tools.

Challenges Addressed

♦ Algorithm Enhancement: Refining existing algorithms to increase their accuracy in detecting movement disorders.
♦ Machine Learning Integration: Implementing machine learning techniques to boost predictive capabilities and provide detailed analysis.
♦ Data Complexity: Managing complex and diverse data from multiple sources to ensure comprehensive and reliable results.
♦ Research and Development: Conducting thorough research to identify the most effective methods for analysing movement disorders.

Outcomes

♦ Increased Accuracy: The improved algorithms showed higher accuracy in detecting movement disorders, enhancing patient monitoring.
♦ Advanced Predictive Capabilities: The integration of machine learning provided advanced predictive features, enabling earlier detection and better patient outcomes.
♦ Deeper Understanding: The project contributed to a deeper understanding of movement disorders, informing future research and treatment approaches.
♦ Effective Monitoring Tools: Developing advanced diagnostic and monitoring tools has helped healthcare providers manage and treat movement disorders more efficiently.
♦ Research Contributions: The techniques and findings from this project have added valuable insights to the field of movement disorder analysis and the application of machine learning in healthcare.

This project highlights our dedication to utilising advanced technologies and research to enhance healthcare outcomes and deepen the understanding of complex medical conditions.

Project:

Statistical Analysis and Visualization of Preprocessed Time Series Data

Overview

This project was centred on comprehensive data analytics and preprocessed time series data visualisation. By employing advanced statistical operations, we aimed to uncover patterns and trends within the data, providing insightful visualisations that aid in understanding various health conditions. The work facilitated more informed clinical decisions and contributed to broader research on health data management.

Challenges Addressed

♦ Data Preprocessing: Ensuring the time series data was clean, accurate, and ready for analysis.
♦ Statistical Analysis: Applying advanced statistical techniques to identify significant patterns and trends within the data.
♦ Visualisation: Creating clear and informative visualisations to communicate findings effectively.
♦ Clinical Applications: Translating data insights into actionable information for healthcare professionals to support clinical decision-making.

Outcomes

♦ Pattern Recognition: The project successfully identified vital patterns and trends within the time series data, enhancing understanding of health conditions.
♦ Insightful Visualisations: Developed visualisations provided clear and actionable insights, aiding healthcare professionals in their analysis and decision-making processes.
♦ Informed Clinical Decisions: The findings from the data analysis enabled more informed clinical decisions, improving patient care.
♦ Research Contributions: The project contributed valuable insights to the broader research community, advancing the field of health data management.
♦ Enhanced Data Utilisation: We improved the use of time series data in clinical settings, demonstrating the potential for advanced analytics in healthcare.

This project highlights our data analytics and visualisation expertise, showcasing our ability to transform complex data into meaningful insights that drive better healthcare outcomes and support research advancements.

Project:

Firmware Development for a Wrist-Worn Medical Device

Overview

This project involved developing embedded firmware for a wrist-worn medical device for health monitoring. The firmware controls the device’s functions, enabling accurate data collection and real-time monitoring of patient movements. My work ensured the device’s reliability, low power consumption, and precise data acquisition, which are essential for continuous monitoring and subsequent data analysis.

Challenges Addressed

♦ Firmware Development: Writing and optimising embedded firmware to control the device’s functions and ensure accurate data collection.
♦ Real-Time Monitoring: Implementing real-time monitoring capabilities to track patient movements continuously.
♦ Power Efficiency: Ensuring the device operates with low power consumption to maximise battery life.
♦ Data Accuracy: Achieving precise data acquisition to support reliable and effective patient monitoring.

Outcomes

♦ Reliable Performance: The developed firmware provided reliable performance, ensuring the device consistently operated as intended.
♦ Efficient Power Usage: The firmware optimisation led to low power consumption, extending the device’s battery life and enabling long-term monitoring.
♦ Accurate Data Collection: The device achieved precise data acquisition, which is crucial for subsequent data analysis and monitoring accuracy.
♦ Real-Time Monitoring: Enabled real-time monitoring of patient movements, providing valuable data for healthcare professionals.
♦ Enhanced Patient Care: The device’s improved reliability and accuracy improved patient care and monitoring.

This project showcases our capability to develop advanced embedded systems for medical devices, highlighting our focus on reliability, efficiency, and precision in health monitoring technology.

Project:

On wrist detection for wrist-worn medical devices using deep learning

Overview

This project leveraged deep learning algorithms to accurately detect and confirm the placement of medical devices on the wrist using accelerometer data. By analysing motion patterns and vibrations, the system differentiated between the correctly worn device on the wrist and other positions, ensuring reliable data collection and accurate patient vital signs monitoring. This technology enhanced the effectiveness and reliability of wrist-worn medical devices, improving healthcare outcomes and patient care.

Challenges Addressed

♦ Algorithm Development: Creating and optimising deep learning algorithms to accurately analyse accelerometer data.
♦ Placement Detection: Ensuring the system can reliably detect and confirm the correct placement of medical devices on the wrist.
♦ Data Analysis: Analysing complex motion patterns and vibrations to distinguish between correct and incorrect device placement.
♦ System Reliability: Enhancing the reliability of wrist-worn medical devices to ensure accurate monitoring and data collection.

Outcomes

♦ Accurate Placement Detection: The deep learning algorithms effectively detected and confirmed the proper placement of medical devices, ensuring precise monitoring.
♦ Enhanced Data Reliability: We improved the reliability of data collection by ensuring devices were correctly positioned on the wrist.
♦ Improved Patient Monitoring: Enabled more accurate monitoring of patients’ vital signs, contributing to better healthcare outcomes.
♦ Advanced Technology Integration: Successfully integrated advanced deep learning techniques into medical devices, showcasing innovative healthcare solutions.
♦ Enhanced Patient Care: The technology improved wrist-worn medical devices’ effectiveness and reliability, leading to better patient care and monitoring.

This project demonstrates our expertise in applying deep learning algorithms to solve complex healthcare challenges, highlighting our commitment to improving medical device technology and patient care.

Project:

Data Visualization and Analytics

Overview

This project focused on data visualisation and analysis for time series data. The scope included creating aesthetically pleasing and informative visualisations and conducting thorough data preprocessing. The goal was to transform complex datasets into clear, actionable insights, enabling stakeholders to quickly understand and make informed decisions. The blend of technical expertise and creative visualisation ensured the data was informative and engaging, enhancing its impact and usability.

Challenges Addressed

♦ Data Preprocessing: Thoroughly prepare and clean time series data to ensure accuracy and reliability.
♦ Visual Design: Creating visually appealing and informative visualisations that effectively communicate complex data insights.
♦ Complex Data Transformation: Simplifying complex datasets into clear and actionable insights.
♦ Stakeholder Engagement: Ensuring the visualisations were engaging and easily understandable for all stakeholders.

Outcomes

♦ Clear Insights: Successfully transformed complex time series data into clear, actionable insights, facilitating better decision-making.
♦ Engaging Visualisations: Developed aesthetically pleasing visualisations that effectively communicated data insights to stakeholders.
♦ Enhanced Usability: Ensured the data visualisations were informative and user-friendly, improving stakeholder engagement and understanding.
♦ Improved Decision-Making: Enabled stakeholders to make informed decisions quickly and confidently based on precise and reliable data insights.
♦ Technical and Creative Fusion: Demonstrated a blend of technical expertise and creative visualisation, enhancing the overall impact and usability of the data.

This project highlights our ability to combine technical proficiency with creative visualisation skills to transform complex data into valuable and accessible insights, driving better decision-making and stakeholder engagement.

Project:

Gunshot detection and source localisation

Overview

This project, funded by the Punjab Safe City Authority, focused on real-time gunshot detection and precise source localisation. By analysing audio signals and leveraging advanced deep learning and signal processing techniques, the system rapidly identified gunshot sounds and pinpointed the exact origin of the event. This technology enabled prompt response measures in high-risk environments, enhancing public safety and potentially saving lives.

Challenges Addressed

♦ Real-Time Detection: Developing a system capable of detecting gunshot sounds in real time.
♦ Source Localisation: Implementing techniques to determine the origin of gunshot sounds accurately.
♦ Signal Processing: Analysing complex audio signals to distinguish gunshots from other sounds.
♦ Rapid Response: Ensuring the system’s speed and accuracy to enable prompt response measures in high-risk environments.

Outcomes

♦ Accurate Detection: Successfully developed a system that accurately detects gunshot sounds in real-time, enhancing situational awareness.
♦ Precise Localisation: The system effectively pinpointed the origin of gunshot events, providing critical information for emergency responders.
♦ Enhanced Public Safety: Enabled rapid response measures, improving public safety and potentially saving lives in high-risk environments.
♦ Advanced Technology Integration: Leveraged deep learning and signal processing techniques to create a cutting-edge solution for gunshot detection.
♦ Life-Saving Impact: The technology’s ability to quickly identify and locate gunshots contributed to faster and more effective emergency responses, demonstrating its life-saving potential.

This project showcases our expertise in applying advanced technological solutions to enhance public safety, highlighting our commitment to developing innovative systems that make a real-world impact.

Project:

Fault detection in railway tracks using deep learning

Overview

The project, conducted in collaboration with Pakistan Railways, aimed to utilise deep learning techniques to detect faults in railway tracks using accelerometer data. The system identified anomalies indicative of potential flaws, such as cracks or deformations, by analysing the vibrations and motion patterns captured by accelerometers installed on railway wagons. This approach ensured safer and more reliable railway operations.

Challenges Addressed

♦ Fault Detection: Developing a system to detect faults in railway tracks accurately using accelerometer data.
♦ Deep Learning Integration: Implementing advanced deep learning techniques to analyse complex vibration and motion patterns.
♦ Data Analysis: Processing large volumes of accelerometer data to identify anomalies.
♦ Real-Time Monitoring: Ensuring the system could provide timely and accurate fault detection for immediate action.

Outcomes

♦ Enhanced Safety: Successfully developed a system that detects potential faults in railway tracks, improving the safety of railway operations.
♦ Accurate Anomaly Detection: The deep learning algorithms accurately identified anomalies such as cracks and deformations, allowing for timely maintenance.
♦ Improved Reliability: The project contributed to more reliable railway operations by enabling proactive identification and resolution of track issues.
♦ Advanced Technological Application: Demonstrated the effective use of deep learning techniques in infrastructure monitoring.
♦ Collaborative Success: The collaboration with Pakistan Railways ensured the system was tailored to real-world conditions, maximising its effectiveness.

This project highlights our capability to leverage advanced technologies like deep learning to improve infrastructure safety and reliability, showcasing our commitment to innovative solutions in the transportation sector.