Find the best hardware and software setup to run your AI on the edge. <\/p>\n
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.<\/p>\n
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.<\/p>\n
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.
\nAt 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. <\/p>\n
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.<\/p>\n","protected":false},"excerpt":{"rendered":"
Hardware\/Software AI Deployment recommendation report<\/p>\n","protected":false},"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false},"product_brand":[],"product_cat":[135,149,148,122,129,123,127,130,128,146,147,134,125,153,126,121],"product_tag":[160],"class_list":["post-6179","product","type-product","status-publish","product_cat-ai-for-autonomous-decision-making","product_cat-ai-powered-learning-and-programming","product_cat-ai-powered-robot-control","product_cat-benchmarking-en","product_cat-computer-vision","product_cat-deep-learning","product_cat-edge-to-cloud","product_cat-factory-level-optimisation","product_cat-high-performance-computing","product_cat-human-robot-interaction","product_cat-intelligent-grasping-object-manipulation","product_cat-intelligent-material-handling-systems","product_cat-machine-learning","product_cat-natural-language-processing","product_cat-neural-network","product_cat-other-emerging-and-enabling-technologies","product_tag-germany","first","instock","shipping-taxable","product-type-simple"],"acf":[],"yoast_head":"\n