Client Problem:

The platform supported analytics, AI experimentation and production cloud workloads for multiple teams. While the technology stack was modern, leadership lacked a real-time view of platform health and reliability increasingly depended on individuals rather than engineered ownership.

Technical pain points:

As platform usage increased, operational weaknesses surfaced across data pipelines, AI workloads and cloud infrastructure. These issues were not isolated failures, but recurring patterns caused by missing standardisation, observability and guardrails.

• Data pipelines failed inconsistently, with no standard orchestration, retry behaviour or health scoring.

• AI and RAG components degraded over time due to missing evaluation, drift detection and refresh controls.

• Monitoring signals were fragmented across tools, with no unified incident or escalation path.

• Cloud costs increased unpredictably, with spend visibility and anomaly detection arriving too late to prevent waste.

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Operational impact:

The lack of a unified operating model turned technical issues into day-to-day operational friction. Over time, this affected platform reliability and response times across consuming teams.

• Production interruptions became frequent, requiring manual intervention to restore services.

• Incident resolution times increased due to unclear ownership and the absence of standard runbooks.

• BI, analytics and AI consumers experienced inconsistent availability and declining trust in platform outputs.

• Engineering teams spent more time firefighting operational issues than delivering new capabilities.

  
  

Root Cause Analysis:

Our experts conducted a thorough root cause analysis and noticed that it was not a tooling failure or a lack of effort. It was an operating model gap.

• No single reliability plane across Data + AI + Cloud

Teams monitored individual components rather than end-to-end outcomes. Pipelines, models and infrastructure each had partial visibility, but nothing connected incidents to SLAs, ownership or automated remediation.

• Governance wasn’t connected to operations

Lineage, metadata and access controls, where present, were treated as static compliance artefacts. They were not used as operational signals to reduce blast radius or accelerate root-cause analysis.

• Cost drift came from ungoverned choices, not “high usage”

Without enforceable policies and continuous FinOps loops, cloud spend became a lagging indicator. Alerts arrived too late.

• AI systems degraded silently

AI and RAG workloads lacked continuous evaluation and drift detection. Quality issues surfaced as trust problems rather than observable incidents.