Presear engineers edge AI solutions — model compression, quantisation, on-device inference, and hardware-optimised deployment — for IoT, industrial, and embedded systems.
Technical Depth
From model compression to federated edge training — we deliver AI that runs on constrained hardware without sacrificing accuracy.
Reduce model size and accelerate inference by converting 32-bit floating-point weights to INT8 or FP16 representations — with minimal accuracy loss. We apply post-training quantisation and quantisation-aware training for maximum compression, enabling models that were impossible to run on edge hardware to execute in real time on microcontrollers and edge SoCs.
Automatically discover compact neural architectures that meet your hardware's latency and memory constraints — rather than manually shrinking large models. We use hardware-aware NAS to search architecture spaces constrained by target device specs, producing models that are natively efficient rather than retrospectively compressed, achieving better accuracy-efficiency tradeoffs.
Transfer the intelligence of a large teacher model into a compact student model — preserving most of the teacher's predictive power in a fraction of the parameters. We implement task-specific and feature-level distillation strategies, often combining distillation with quantisation and pruning for compound compression ratios that achieve 10x size reduction with under 2% accuracy degradation.
Convert and optimise models for deployment on resource-constrained devices using TFLite for Android, iOS, and microcontrollers, or ONNX Runtime for cross-platform deployment including Windows, Linux, and embedded Linux. We handle operator compatibility, delegate selection (GPU, DSP, NPU), and benchmark-driven optimisation to extract maximum performance from every device.
Unlock the full potential of your target hardware by using vendor-specific acceleration — TensorRT for NVIDIA Jetson, OpenVINO for Intel hardware, SNPE for Snapdragon DSPs, CoreML for Apple Silicon, and ARM CMSIS-NN for Cortex-M microcontrollers. Generic deployment leaves 40–70% of hardware performance on the table; hardware-specific optimisation closes that gap entirely.
Train and continuously improve models across distributed edge devices without centralising raw data — each device contributes gradient updates rather than personal data. We implement FL protocols compatible with resource-constrained hardware, handling asynchronous updates, partial participation, and communication-efficient aggregation to keep models improving even when devices are intermittently connected.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
Every edge AI project starts with hardware. We assess your deployment environment — power budget, memory constraints, compute availability, operating temperature, connectivity — and select the optimal hardware platform. This decision gates all subsequent architecture and compression choices, so getting it right early saves months of rework.
We design or select neural architectures with hardware constraints as first-class design parameters — not afterthoughts. Using hardware-aware NAS or established efficient architectures (MobileNet, EfficientDet, YOLO-Nano), we build models that are compact by construction. Task-specific architecture choices, input resolution, and anchor configurations are all tuned to the target device's capability envelope.
Once the base model meets accuracy targets, we apply a systematic compression stack — knowledge distillation from a larger teacher, structured and unstructured pruning to remove redundant parameters, followed by quantisation to INT8 or FP16. Each step is validated against accuracy thresholds, and the pipeline is iterated until size, latency, and accuracy targets are simultaneously satisfied.
Deploying to edge is not just copying a model file — it requires runtime setup, hardware delegate configuration, thermal stress testing, and real-world evaluation under actual operating conditions. We flash target devices, validate latency and memory footprint on hardware, test across environmental extremes, and run adversarial input sets that represent the worst-case conditions the device will encounter in deployment.
Edge devices in the field need secure, reliable model updates without physical access. We build OTA update pipelines that deliver signed, versioned model packages to device fleets — with rollback capability, delta update support to minimise bandwidth, A/B deployment for safe rollouts, and telemetry collection to monitor inference quality post-update across thousands of deployed units.
Real-World Impact
Production edge AI deployments across industries — delivering real-time intelligence directly on device.
Core Challenge
Manufacturing lines need defect detection at conveyor speed — 30+ frames per second with sub-10ms decision latency — in environments with no reliable internet connectivity. Cloud-based vision AI introduces unacceptable latency and network dependency that halts production lines during connectivity outages.
Who Benefits
Automotive, electronics, and FMCG manufacturers that run high-speed production lines and need on-device computer vision for surface defect classification, foreign object detection, and dimensional inspection — operating independently of cloud connectivity.
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Transmitting full video streams to cloud for AI analysis consumes prohibitive bandwidth and introduces privacy risks. Security systems need on-camera AI that processes video locally, sending only metadata and alerts — drastically reducing bandwidth while keeping sensitive footage within the physical security perimeter.
Who Benefits
Airports, campuses, retail chains, and critical infrastructure operators that deploy high-density camera networks and need privacy-preserving, bandwidth-efficient AI surveillance with real-time threat detection that operates reliably even during network disruptions.
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Agricultural IoT sensors deployed across fields operate in areas with no mobile connectivity, on battery power, and must continuously classify plant health, soil conditions, and pest presence from sensor readings — requiring ultra-low-power AI that runs on microcontrollers for months without intervention.
Who Benefits
Precision farming operators, agritech companies, and agricultural research institutions that instrument fields with sensor nodes and need on-node inference for crop disease detection, irrigation triggering, and yield prediction — without cloud dependency or battery drain concerns.
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Wearable devices monitoring heart rate, SpO2, and activity continuously must run AI inference within a 10–50mW power budget with millisecond latency — without transmitting raw biosignal data to cloud, due to both battery constraints and patient privacy regulations governing continuous health data streams.
Who Benefits
Medical device manufacturers, health-tech wearable companies, and remote patient monitoring platforms that need on-device AI for arrhythmia detection, fall detection, sleep staging, and continuous vital sign anomaly alerting — compliant with healthcare data privacy requirements.
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Best-in-class runtimes, hardware platforms, and development toolchains — covering the full spectrum from microcontrollers to edge servers.
Frequently Asked
Answers to the questions hardware engineers, product managers, and CTOs ask before starting an edge AI engagement with Presear Softwares.
Ask Our Edge AI TeamPartner with Presear Softwares to compress, optimise, and deploy AI models that run reliably on any hardware — with or without internet.