Services
Practical support for deploying edge AI and low-latency models on constrained devices
Edge AI Architecture Design
Strategic planning and architectural guidance for deploying machine learning models at the edge. We help you evaluate approaches, identify infrastructure requirements, and design systems that balance performance with your resource constraints. This typically involves 2-3 structured sessions covering model optimization techniques, hardware-software tradeoffs, inference pipeline design, and data flow architecture.
Model Deployment and Optimization
Technical implementation support for taking trained models from research into production on edge devices. We cover model quantization and compression, runtime environment selection (TensorFlow Lite, ONNX Runtime), inference acceleration, deployment pipelines, and performance profiling. Scope depends on your current state—whether you're starting from a research checkpoint or refining an existing deployment.
Latency and Performance Consulting
Hands-on diagnosis and optimization of inference latency, throughput, and resource usage. We profile your current pipeline, identify bottlenecks (model size, quantization artifacts, memory bandwidth, inference runtime), and recommend targeted improvements. Useful for teams hitting latency targets or trying to reduce hardware requirements while maintaining accuracy.
Edge vs. Cloud Architecture Review
Clarifying the tradeoffs between on-device processing, cloud inference, and hybrid approaches. We help you understand cost, latency, privacy, and connectivity implications for your specific use case—IoT sensors, mobile apps, real-time video processing, or autonomous systems. This session outputs a decision framework you can use to guide technical choices.
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Contact us to discuss your project, ask questions, or request a consultation. We'll work with you to understand your constraints and recommend the right approach.
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Explore our guides and technical articles on edge computing and on-device AI deployment
Quantization Techniques for Mobile Models
Deep dive into model quantization and compression strategies for constrained devices.
Hardware Selection for Ottawa Startups
Choosing the right processors, accelerators, and edge devices for your workload.
TensorFlow Lite vs ONNX Runtime
Comparing inference runtimes: performance, compatibility, and production considerations.
Building Real-Time Inference Pipelines
Practical patterns for low-latency model serving on edge infrastructure.