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The client is a large enterprise managing high volume customer interactions across multiple channels and geographies.
When we first engaged with this client, the sentiment from their engineering leadership was straightforward:
“Our teams are strong. Our platforms are strong. Yet, we keep losing time in places that should never take this long.”
This wasn’t an exaggeration. Their developers and service teams were putting in the effort, but the cumulative friction across daily activities—pull requests, test prep, deployment checks, and triage cycles—was beginning to chip away at release confidence. Nothing was broken, yet momentum was slowing. And for an organization operating at their scale, even a slight slowdown is rarely acceptable.
The client struggled with resource allocation inefficiencies across distributed data centers. Workloads were queuing while assets remained under-utilized, and manual orchestration couldn't handle the operational complexity at scale. Additionally, lengthy root cause analysis from run logs, fragmented product knowledge, and complex incident management spanning across thousands of nodes were creating operational bottlenecks.
Impact Realized by the Client
Twelve weeks after the initial pilot, the client’s internal reporting teams shared their assessment. They observed a clear shift, and each metric had been validated on their side.
- Code review cycles improved by 35%
- Test coverage increased by 40%
- Deployment success rates rose by 25%
- ITSM analysts spent less time on repetitive triage
- RCA cycles shortened because the system highlighted patterns that previously required extensive manual investigation
- Release planning became more predictable
Where the Real Issues Actually Lived
After spending time with their engineering directors, a pattern emerged. Not a dramatic one, but a quiet, persistent one. Review cycles dragged longer than intended because responsibilities were spread thin. Test teams kept up, but with effort levels that were clearly unsustainable. Their CI/CD estate was large enough that even minor configuration mismatches could snowball into multi-team delays.
On the ITSM side, analysts were dealing with a regular stream of incidents. Many of those were routine but still required strong attention. More complex incidents required engineers to reconstruct context across logs and configuration histories—a time sink they had grown accustomed to.
The takeaway wasn’t a lack of discipline. They had plenty of that. What they didn’t have was time.
Birlasoft’s Working Model
We approached the engagement with a simple rule: Do not disturb what is already working well. Instead, amplify what is slowing teams down.
The first step was observation, not solutioning. We sat with their developers, watched how service analysts handled incoming queues, asked release managers where they typically lost hours, and we listened. There was no need to reinvent their ecosystem; it was already well-architected. What they needed was relief in the right places.
Three insights shaped the path forward.
- Their friction lived inside repetitive activities, not exceptional ones.
- Their tools were capable, the overhead around them was not.
- Narrow interventions would produce more value than a sweeping agenda.
With this clarity, we introduced agentic AI capabilities as assistive elements inside their workflow, not disruptive ones.
Choosing What to Fix First
Even before we talked about the ‘solution,’ we prioritized areas where teams felt the pressure most.
- Code reviews slowed when reviewers were stretched thin. AI-assisted context notes improved turnaround times.
- Test teams spent more time preparing than executing. Automated test generation changed the ratio.
- Deployment readiness varied by business unit. Early configuration insights reduced avoidable failures.
- ITSM analysts spent hours on repetitive classification. AI-based triage helped them focus on the actual problems.
- RCA benefited from pattern-spotting across logs and configurations—something AI does well and humans do only when they have the time.
None of this was pushed at scale immediately. Every capability was tested in controlled environment with real workloads.
How the Solution Was Built
Technically, architecture resembled a set of cooperating services rather than one large (single monolithic) engine.
The agentic AI components each had a tight, specific role: review support, test generation, deployment readiness assessment, triage assistance, and pattern identification for RCA.
A model operations layer governed access, auditability, and version control. This became important because the client had strict internal guidelines around model behavior and data governance.
Finally, the components are integrated with GitHub Enterprise, Jenkins pipelines, ServiceNow and the client's observability ecosystem. The client’s teams did not need to change their workflows to use these capabilities. That was intentional.