Custom or off-the-shelf? It’s rarely a simple either/or. Our latest blog breaks down the real question behind hardware architecture decisions: which approach gets a product to market on time, meets the requirements, and stays supportable through the life of the program.
Data center build-out is not keeping pace with AI demand. Power constraints, permitting delays, and grid limitations are real. Edge AI is not a workaround; it is a practical architectural decision for companies that cannot afford to wait. ADLES explores what that means for your deployments.
There is a big gap in how the market talks about AI right now. Most of the conversation is still centered around training: bigger models, more compute, more data, better benchmarks. That all matters. But for defense, industrial, and other rugged edge deployments, that is only part of the story.
Cloud AI is great… until you deploy it somewhere that isn’t a demo. Out in the field, you rarely get perfect connectivity, clean data paths,
Artificial Intelligence isn’t living in the cloud anymore. From autonomous vehicles to defense and industrial automation, more AI workloads are happening right where the data
— And Why COTS Alone Won’t Get You There In rugged embedded computing, failure isn’t just inconvenient—it’s a non-starter. These systems are the quiet workhorses
Designing such systems requires more than just selecting robust components—it demands a holistic approach that integrates hardware, power architecture, thermal management, mechanical structure, and system integration into a cohesive and resilient solution.
When designing enclosures for rugged embedded computers, the choice between milled and extruded aluminum designs significantly impacts performance, durability, cost, and functionality. Understanding these differences is key to selecting the right solution.
