AI Edge Deployment Evaluation

Find the best hardware and software setup to run your AI on the edge. This service benchmarks your AI pipeline across a broad range of industrial edge-AI hardware and software stacks. We measure inference latency, model accuracy, energy consumption, and overall feasibility, and then we deliver a clear, data-backed recommendation for the optimal deployment configuration. This service is valuable manufacturers running machine vision or quality inspection, when they need real-time inference directly on the shop floor without any cloud dependency. AI providers benefit when they want to prove that their models run efficiently on power-constrained industrial devices, such as those operating under 75 watts. Product teams also benefit when they are deciding between hardware options like a microcontroller, an embedded GPU, or a dedicated AI accelerator, and when they need objective and comparable performance data to guide that decision. Instead of buying and testing multiple devices yourself, we run your pipeline on our full lab of industrial edge hardware and hand you a decision-ready comparison. This approach saves procurement cost, reduces engineering time, and lowers the risk of choosing the wrong hardware. At the end of the engagement, you receive a Hardware and Software AI Deployment Recommendation Report. It contains comparative benchmark tables covering latency, accuracy, and power across all tested hardware, along with a recommended optimal configuration for the targets you have stated. The report also identifies the relevant trade-offs and optimization options and includes a feasibility assessment for production deployment. You reach a decision faster when selecting edge hardware, because the results are quantified, with typical outputs including measured latency in milliseconds, model accuracy as a percentage, and energy per inference in joules or watts, your procurement decisions can be fully data-driven.