Applied AI/ML Research
QuietGrowth Tech data scientists and data engineers conduct applied research in Artificial Intelligence and Machine Learning (AI/ML) and translate it into working product capabilities. The case studies and project examples below highlight selected AI/ML solutions we have helped develop for client products.
Prediction and recommendation engines
We develop prediction engines using supervised machine learning algorithms that can predict outcomes and deliver context-specific recommendations in real time based on the insights identified from the collected historical information. These models can be integrated into product workflows to provide reliable, data-informed guidance.
We applied this capability for: A fintech product and a healthtech product.
Evidence-based statistical modelling
We develop and validate statistical models using real-world datasets to build novel techniques for health and fitness applications. Drawing on the study of new insights discovered by the medical community and using data science methodology, we formulate testable hypotheses and evaluate them on relevant datasets to determine whether they can support reliable and practical product features.
We applied this capability for: A healthtech product.
Computer vision and real-time image analysis
We build computer vision and systems for analysing real-time videos and large sets of images. These systems use region-based segmentation, pixel-level analysis, pattern matching, motion correction, thermal analysis and statistical modelling techniques to extract meaningful features from image data.
We applied this capability for: A healthtech product.
Biosignal processing systems
We build biosignal processing systems that clean, synchronise and analyse signal data captured by one or more biosensors, to extract meaningful features. We have used sensing modalities such as optical sensing, bioimpedance, radar, electrocardiography (ECG) and piezoelectric sensors for biosignal capture. We then use advanced signal-processing techniques and ML models to derive health-related metrics from these signal features. We then work to ensure these health-related metrics support applications in risk stratification, prediction and monitoring across cardiometabolic, vascular and respiratory health.
We applied this capability for: Healthtech products for two different clients.
Volumetric video and 3D telepresence
We build real-time volumetric video systems for holographic and immersive remote meetings. These systems combine depth-camera data, computer vision and machine learning to segment foreground subjects, refine depth maps and reconstruct participants' 3D representations for transmission and rendering in immersive environments. We work to achieve low enough latency for natural conversation, not just playback.
We applied this capability for: A communications technology product.
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