Our Projects: Speaking Volumes and Proving That Our Impact is Inherent
A global leader in video analytics and non-cooperative face recognition sought to develop highly accurate and defect-free surveillance products tailored for diverse sectors, ranging from oil and gas to Smart Cities. They required comprehensive product lifecycle management from an Offshore Development Centre (ODC), alongside scalable systems capable of operating across critical infrastructure such as airports, libraries, and banks.
Sphinx designed and developed a comprehensive suite of AI-driven security products, including facial recognition, license plate recognition, non-motion detection, video compression (H.264, HEVC, MPEG4), and mobile surveillance interfaces. Leveraging technologies such as Microsoft .NET, ASP.NET, C++, WPF, OpenCV, and FFMPEG, we integrated advanced video encoding/decoding tools and deployed proprietary algorithms to enhance visual analysis. The development also involved creating a smart pass system and implementing a vision stack tailored to a broad range of environments, including high-traffic urban zones.
The client realised a marked enhancement in operational security, supported by a scalable and intelligence-driven architecture. Our solutions also enabled on-demand business analytics and led to the development of a patented algorithm to redact and restore video segments. This allowed precise and secure image manipulation, increasing confidence in forensic and security applications.
A health and wellness provider needed a secure AI/ML-powered digital assistant capable of identifying early burnout symptoms and offering corrective support. The system required customisation by therapists, coaches, and nutritionists and needed to evolve daily through continuous learning.
We developed a responsive, role-based web platform using Angular 9, RASA (NLU and Core), Python, and Node.js. This system allowed the creation of intent libraries, contextual entities, and customisable conversation flows. Real-time dashboards and analytics were embedded to monitor interactions, while model training (manual and automated) was fully integrated with testing environments. The backend was secured using NGINX and powered by MariaDB for data storage and retrieval.
The result was a user-friendly and transparent platform that enabled a hybrid approach to mental wellness support. It combined digital assistance with human oversight, ensuring both scalability and personalisation. The assistant successfully delivered structured, traceable stress management outcomes across user cohorts, all delivered on time and within scope.
An international software development firm faced health-related concerns due to its touch-based attendance systems. They required a touchless, AI-enabled attendance solution that could authenticate multiple employees simultaneously without physical contact, especially critical during times of heightened health awareness.
We developed a facial recognition solution using Windows Server REST APIs and TensorFlow, integrating live video feeds through IP and web cameras. A FaceNet model was used for facial feature extraction, utilising triplet loss and optimisers like ADAM and ADAGRAD. The classifier employed was an SVM (Support Vector Machine), ensuring high accuracy for real-time authentication. The solution was implemented using C# .NET, Python, and CV2, supported by Visual Studio and TFS.
The client achieved seamless, real-time attendance tracking with zero physical interaction. Alerts were generated automatically, improving both safety and operational efficiency. The system also supported multi-user facial recognition in controlled environments, reinforcing workplace hygiene and data integrity.
An international real estate developer managing large public venues needed a thermal screening solution to detect elevated body temperatures among visitors and staff, helping to mitigate the risk of disease transmission across their properties.
We built a facial temperature recognition system using Thermal IP cameras, integrated with RESTful APIs on a Windows Server. The backend, developed in Python and C#, utilised TensorFlow for real-time thermal image processing and classification. The system was calibrated with black body references to ensure accuracy and deployed through WCF/Web API with web and mobile dashboards for monitoring.
The platform enabled contactless detection of fever symptoms for multiple individuals simultaneously, with instant alert generation and comprehensive MIS reporting. This not only improved public health safety within the client’s properties but also demonstrated effective automation and accountability through technology.
Legacy parking systems reliant on physical tickets or RFID cards were not only environmentally unfriendly and prone to misuse but also inefficient in terms of vehicle identification. The client needed an AI-powered solution to automate vehicle access and billing without manual intervention.
We deployed a system leveraging IP cameras and TensorFlow-based license plate recognition (LPR) to automate entry and exit processes. At the point of entry, vehicle images were captured and time-stamped. at exit, the system cross-referenced entry data and calculated parking fees. The solution was delivered using Python, C# .NET, WCF/Web API, and Onvif protocols, supported by third-party SDKs and integrated via Visual Studio IDE.
The client saw a dramatic reduction in human error and unauthorised access. Vehicle details, entry timestamps, and payment histories were logged and processed automatically, with blacklisted vehicles denied entry. The system ensured efficient traffic flow, eco-friendliness, and enhanced security, transforming the parking experience into a seamless, smart operation.
In response to COVID-19 protocols, organisations needed a way to ensure that employees entering their premises were wearing masks. The challenge was to detect multiple human faces simultaneously, recognise whether individuals were wearing masks, and trigger notifications if non-compliance was observed. The goal was not just detection but to use facial recognition as a touchless access and alert system in real time.
A robust application was developed using TensorFlow for image recognition, CV2 as the camera module, and Caffe models for facial detection. Built on a Windows Server environment, the backend exposed its features through REST APIs, while client applications for both web and mobile were configured to display access statuses and MIS reports. The system functioned seamlessly with IP camera feeds in controlled environments. The entire tech stack included C++, Python, C# .Net, Visual Studio 2019 with TFS, and WCF/Web API for system integration and communication.
The result was a highly effective system capable of detecting multiple faces simultaneously and issuing mask-related alerts in real time. It enabled organisations to monitor employee compliance without manual checks, thereby reducing transmission risks. Additionally, it introduced touchless facial recognition, enhancing both hygiene and operational efficiency in workplace access control.
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: +16506441375
: +443308087384
: +4915735986750
: +46271809884
: +16506441375
: +443308087384
: +4915735986750
: +46271809884
