Edge AI is gaining significant momentum across Latin America, thanks to the efforts of Rakuten Cloud partners such as Simply Tech, who are championing the technology in the region.
To get an update on the latest edge AI trends, our own Padmarajan (Raj) Narayanan, global head of presales and solutions (enterprise), sat down with Gabriel Prodera, CEO of Simply Tech, and Sam Freitas, head of business development at Simply Tech.
Having this conversation at Google Cloud Next, a conference focused on the latest large language models, one might think LLMs would be the main topic. “Most AI discussions focus on models—what model to use, benchmark results, parameter counts, and so on,” Raj said.
Prodera and Freitas, however, had a different perspective: they believe that robust AI infrastructure is the true key to success. As Prodera explains, “At the end of the day, if the infrastructure fails, the business fails. The model can always be improved later, but the infrastructure is the foundation everything depends on.”
This infrastructure is often the primary bottleneck to AI success in an organization. It must be implemented correctly from the start, as it is both difficult and costly to replace later.
All AI use cases are not equal when it comes to bottom-line impact. Freitas organizes AI deployment success into three tiers:
One important—but often overlooked—key to edge AI success is persistent storage.
Storage is critical because AI model weights, configurations, and application state must persist locally and if these assets must be reloaded multiple times, downtime can extend from milliseconds to minutes.
Second, RAG applications depend on local vector databases and indexes. Without reliable local storage, these systems cannot operate efficiently.
Third, highly regulated industries such as healthcare and financial services require secure data retention, auditing, and compliance capabilities. Infrastructure must support these requirements from the beginning.
Finally, model lifecycle management becomes more difficult when deploying updates across hundreds or thousands of edge locations. That’s when infrastructure reliability becomes essential.
Organizations should start small and focus on solving a single business problem with a single deployment, rather than waiting for a perfect long-term strategy.
Freitas’s advice: “Stop planning and start applying. One deployment, one camera, and 30 days of operational experience will teach you more than one or two years of planning.
Start small, learn quickly, and scale based on results.”
When discussing practical deployment strategies, both speakers advised organizations to focus on use cases that produce immediate business value.
For retailers, computer vision systems can help reduce losses through improved monitoring and loss prevention. Manufacturers can deploy AI-driven monitoring systems on production floors to improve efficiency and identify issues before they become costly failures. Financial institutions can use edge AI for image processing, OCR, and compliance-sensitive workflows while keeping data local.
In closing, Prodera summarized the discussion with a simple principle: "Your AI models are only as good as the infrastructure they run on."
Freitas agreed, noting that as edge AI hardware becomes more powerful, it will enable more sophisticated deployments. He further predicted that AI systems will eventually become self-managing, capable of determining when the infrastructure needs updates, upgrades, or optimization. It will be able to solve many of the problems it identifies.
Raj offered the final word, capturing the essence of the conversation: “Infrastructure at the edge is what separates AI demonstrations from production AI deployments.”
For those interested in learning more about this insightful conversation on AI at the edge, infrastructure architectures, deployment models, and operational best practices, please watch the full video here.
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