Context.
A large online gaming and betting operator ran 18 business critical Java services across more than 3,000 VMs. The platform processed over 180 million casino spins per day and handled traffic peaks of up to 600,000 requests per second. In a regulated real-money environment, observability had become part of the revenue-protection and operational-resilience model.
Monitoring had evolved over a decade: SCOM, VMware and Nutanix for infrastructure; AppDynamics and Prometheus for application performance; Logstash and Elasticsearch for logs; and several visualisation platforms. Each tool remained useful, but incidents still had to be assembled across disconnected timelines. The organisation had extensive telemetry without a single path from business impact to technical cause.
Problem Statement.
Outcome/Impact.
Solution.
We framed the programme as controlled modernisation, not a tool replacement. The existing stack stayed active, each service was validated independently, and rollback remained available throughout. This protected business continuity while allowing value to be proven progressively.
Used AI to reduce uncertainty around the critical dependency: AI-assisted analysis mapped the TCP library's Netty flow, serialisation boundaries, metadata, thread hand-offs and error paths. Engineers used that evidence to select safe propagation points, verify compatibility and benchmark the change on Java 8 before rollout.
Turned one intervention into an estate-wide capability: OpenTelemetry client and server spans were added to the shared library with W3C trace context. One controlled framework change unlocked end-to-end tracing across all 18 services without redesigning each application.
Moved business detection closer to transaction time: Four bounded streaming metric families for spins, bets, wins and revenue replaced poll-based detection. Detailed logs remained available for audit and investigation, while latency drift, retry growth, queue pressure and spin-rate deceleration provided earlier warning of business impact.
Created fleet-wide control and portability: The OpenTelemetry Java Agent and local Collector were deployed across 3,000+ VMs. Batching, retry and export were kept outside the application process, and an open instrumentation standard reduced future switching cost.
Scaled the migration with AI assistance: AI mapped 67 monitor definitions across repositories to services, methods and candidate instrumentation points, then drafted configurations and documentation. Repetitive planning moved from days to hours; engineers retained accountability for semantics, performance and production approval.
Result.
The operator moved from fragmented, reactive monitoring to an observability capability aligned with business continuity. The value extended beyond better telemetry: earlier protection of peak-hour revenue, faster operational decisions, lower platform waste and a safer path to modernise a legacy estate. The five-year-stable library was not rewritten; it was understood, instrumented and converted into a platform-wide control point. AI accelerated discovery, while disciplined engineering kept the change safe.
