We design AI models optimized for low-power, real-time embedded systems, enabling smarter decision-making without cloud dependency. Our expertise ensures AI-enhanced functionality even in power-constrained and safety-critical environments.
We provide tinyML development, on-device inference optimization, AI model compression (quantization, pruning), hardware-accelerated AI (TPUs, FPGAs, MCUs), and real-time anomaly detection—bringing intelligence directly to the edge.
From adaptive control algorithms to self-optimizing systems, we develop advanced control strategies that enhance precision, efficiency, and responsiveness in real-world applications.
We provide model predictive control (MPC), PID tuning, sensor fusion, real-time optimization, and fault-tolerant control systems—ensuring stability and reliability in dynamic environments.
We extract meaning from complex sensor inputs to improve decision-making and optimize embedded AI models for medical, industrial, and IoT applications.
We provide real-time sensor fusion, noise reduction algorithms, anomaly detection, pattern recognition, and AI-enhanced signal processing—ensuring accurate, high-speed data interpretation.
We implement AI-powered predictive maintenance and fault detection systems that identify potential issues before they impact operations—reducing downtime and improving safety.
We provide failure mode prediction, early warning system design, Bayesian & neural-based fault detection, vibration/spectral analysis, and real-time health monitoring—keeping systems running smoothly.
We develop lightweight computer vision solutions for embedded applications, from medical imaging to quality control in manufacturing.
We provide image processing, feature extraction, low-power vision AI (e.g., OpenMV, TensorFlow Lite), object detection, and edge-based optical inspection—making real-time visual intelligence possible.
We apply AI to optimize power consumption in embedded systems, ensuring efficient energy use and extending device lifespan.
We provide AI-driven power management, predictive battery optimization, adaptive energy harvesting strategies, and real-time load balancing—maximizing performance while minimizing energy waste.
We design AI-driven adaptive algorithms that enable systems to dynamically adjust to changing conditions—improving responsiveness, efficiency, and automation in embedded environments.
We provide reinforcement learning for control systems, adaptive filtering, AI-enhanced feedback loops, self-learning optimization, and real-time decision-making models—ensuring systems continuously improve without human intervention.
Company Name: FutureCare UK Ltd.
Digital Health Start-up
The Target
The Outcome
The Work
