Without advanced tools, IT teams are forced to diagnose the root cause of any issues on their own, slowing development pipelines and hurting efficiency. And with human error in the mix, it’s very possible that developers too close to their own code still miss problems.
What if developers and engineers could predict issues well before symptoms started to show in the final product? AI-powered algorithms can learn how systems behave when healthy, and then perform tasks including observing signals, detecting subtle “heartbeat” patterns, and finding correlations between workload and processor-level time signatures to spot problems in the code. Not only would this enable IT teams to fully understand their systems’ behavior and proactively jump ahead of any issues, this AI-driven approach could address risks before they escalate into outages.
This adaptability is indispensable as the mainframe talent pool continues to shrink, as an AI system frees invaluable human team members from waste cycles checking and triple-checking their own work, letting them use their ingenuity and creativity on key tasks.
Because the IBM i platform is so stable and high‑performing, many users assume it will keep running flawlessly forever. And while IBM i is one of the most reliable systems on the market, organizations overlook the uncomfortable truth that even the most stable system becomes vulnerable when its configuration stops keeping pace with the user’s business.
There is no universal, always‑correct configuration, and what works today may already be misaligned tomorrow. Businesses evolve, workloads shift, applications change, integrations grow, hardware gets refreshed, and system parameters move. IBM i isn’t static, which means no configuration can afford to be static either, and human team members can’t waste time hunting down every issue that emerges in a rapidly changing business.
i-Rays was built to solve the problem no human team, no matter how skilled, should spend time trying to solve manually: understanding, validating, and optimizing the real configuration of any IBM i system.
Combining deep expert knowledge with advanced AI and machine‑learning algorithms, i-Rays can analyze patterns, detect anomalies, and understand configuration dependencies at a scale no person could maintain.
Here’s how i-Rays helps any IBM i system adapt seamlessly:
Replacing guesswork with data‑driven insights: i-Rays analyzes hundreds of parameters and relationships, revealing risks, inefficiencies, and misalignments even experienced administrators can’t track. Its AI/ML engine learns from system behavior, increasing accuracy over time.
Adapting to changes in any business: Instead of a one‑time configuration, i-Rays continuously monitors how workloads, applications, and system settings evolve—keeping configurations aligned with reality, not with how things looked months ago.
Protecting companies from the shrinking talent pool: As IBM i expertise becomes harder to find, i-Rays becomes an on-demand specialist, capturing and automating the knowledge that used to live only in the heads of a few experts.
Preventing the butterfly effect before it happens: i-Rays identifies risky changes, conflicting parameters, and hidden dependencies before they become outages or performance issues.
Offering expert visibility and guidance. It eliminates hours of manual analysis and delivers ready‑to‑apply system commands and configuration fixes, so your team can focus on strategy — not firefighting.
As workloads shift and expertise becomes scarce, i-Rays keeps IBM i infrastructure stable, efficient, and aligned with any business. With i-Rays, Administrators get the AI‑powered insight and automation they need to operate at a whole new level.
