AI, Data & Intelligence
Edge AI
Deploying optimized machine learning and computer vision models directly onto low-power embedded hardware and IoT devices.
Capability overview
What edge ai involves
Edge AI processes artificial intelligence workloads locally on embedded hardware devices, microcontrollers and edge gateways without relying on cloud connections. We engineer low-latency, bandwidth-efficient Edge AI solutions.
We optimize models using TensorRT, ONNX Runtime, TensorFlow Lite and OpenVINO, quantizing neural networks down to INT8 and INT4 precision.
Our Edge AI deployments enable real-time local decision-making, operate reliably offline and ensure maximum data privacy for industrial devices.
During the edge ai engagement, our specialists work closely with your technical leads to establish tailored operational workflows, automated validation controls and clear deliverables for model quantization & pruning and embedded hardware optimization. From initial hardware & latency constraints audit through to model compression & compilation, we embed continuous telemetry monitoring, structured documentation and risk mitigation rules tailored specifically for your organization's edge ai goals and offline local inferencing pipelines requirements.
What is included
What the engagement covers
Model Quantization & Pruning
Compressing neural network sizes via weight pruning and INT8 quantization without sacrificing accuracy.
Embedded Hardware Optimization
Optimizing model execution for NVIDIA Jetson, NXP, STMicroelectronics and Raspberry Pi hardware.
Offline Local Inferencing Pipelines
Building edge execution engines that operate continuously without cloud network connectivity.
Over-The-Air (OTA) Model Updating
Configuring secure OTA deployment pipelines to update edge model weights across distributed device fleets.
How we work
How we deliver edge ai
Hardware & Latency Constraints Audit
Auditing target device RAM, compute specs, power draw limits and inferencing latency requirements.
Model Compression & Compilation
Compiling PyTorch models into ONNX, TensorRT or TFLite flatbuffers optimized for target hardware accelerators.
On-Device Benchmarking & Testing
Measuring inferencing speed, RAM utilization, thermal output and power consumption directly on physical hardware.
Edge Application Integration
Integrating edge AI inferencing libraries into C++ or Python embedded device application software.
OTA Deployment Setup
Deploying model management pipelines via Docker Edge, AWS IoT Greengrass or Azure IoT Edge.
Related capabilities
Related capabilities in Research & Emerging Intelligence
IoT Analytics
Building real-time telemetry processing pipelines, time-series anomaly detection and predictive maintenance for IoT networks.
Intelligent IoT
Fusing Artificial Intelligence with Internet of Things (AIoT) networks to create autonomous, self-optimizing physical environments.
Digital Twins
Creating virtual digital twin replicas of physical assets, factories and supply chains for real-time simulation and optimization.
Advanced Decision Systems
Building prescriptive analytics engines, multi-criteria decision models and automated executive support systems for complex environments.
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Where this applies
Healthcare & Life Sciences
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Manufacturing & Industrial
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Banking, Financial Services & Insurance
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Questions & answers
Questions about Edge AI
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionEdge AI provides near-zero inferencing latency, eliminates cloud bandwidth costs, operates without internet connectivity and keeps sensitive data on-device.
Edge AI development is quoted as a fixed-fee milestone project based on target hardware platform complexity and model optimization goals.
Using INT8 quantization and structured pruning, we routinely compress 500MB cloud models down to under 15MB for embedded microcontroller execution.
Next step
Discuss edge ai with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.