Product Case Study
Cloud Migration with Scout-itAI
Cloud migration is supposed to bring a company agility, but in reality, it often throws up new problems - latency spikes, service failures, endless noisy alerts and the "it works in dev" mystery.
This financial services firm with a global footprint was migrating a critical customer portal and several backend services from on premises to AWS and a hybrid setup. The issue wasn't lack of monitoring - they had plenty. The problem was that every tool was telling a completely different story.
What they encountered during their migration:
Business leaders were asking: “Are we actually more reliable now?” - and demanding more than just a pretty dashboard to prove it.
They needed something that would give them reliable, measurable and predictable IT service performance - especially when they were changing things.
They deployed Scout-itAI, a cloud-native Reliability monitoring platform designed to turn telemetry into plain-language, business-relevant answers.
What Scout-itAI Delivered
Scout-itAI was deployed as an intelligence layer across the company’s existing observability stack, sitting above AWS, on-prem systems, and the network paths connecting them. It continuously ingested telemetry logs, metrics, traces, and alarms from tools like Splunk, Dynatrace, AppNeta, and DX NetOps/OI, then normalized those signals into the Reliability Path Index (RPI Index / RPI-Index). Instead of forcing teams to reconcile competing dashboards, Scout-itAI translated cross-domain telemetry into one RPI score (Reliability score) and explained what was driving movement in that score using its 13 reliability buckets.
During migration waves, Trender (KAMA) compared current behavior against a rolling 100-day baseline to surface early drift, Blender (Six Sigma analysis) correlated patterns across noisy alerts to isolate true reliability drivers, and Predictor (Monte Carlo forecasting) ran high-volume simulations to estimate the reliability impact of upcoming changes helping IT Ops and Network Ops plan cutovers with clearer risk visibility and fewer surprises.
With Scout-itAI, the migration program shifted from firefighting to measurable reliability improvement, giving the teams a reliable way to track IT service reliability across hybrid paths and making reliability status something that could be easily explained to executives in plain language.
Alerts fell because they were focusing on the reliability metrics that influence the RPI score, not just raw event volume, allowing for faster triage and consistent root cause analysis (RCA) during release windows.
Most importantly, they were able to run reliability forecasting ahead of their major cutovers, quantify the reliability impact analysis of the changes they were making and check afterwards whether those changes had actually had the desired outcome - supporting a culture of continuous improvement (reliability) rather than just trying to stabilise things temporarily.
One of the biggest challenges with cloud migrations is that every tool has a different idea of what reliability looks like. The breakthrough was using the RPI score as a single, shared reliability KPI - something that all teams could agree on. With reliability forecasting they were able to predict the impact of the changes they were making before they shipped.Thinking about implementing a standard way to measure and improve the reliability of your IT service during a migration project ?
Want a standardized way to measure and improve IT service reliability during migration?
Book a demo with Scout-itAI Learn the scoring model : Scout-itAI RPI Index.
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