Driving 40% Faster Resolution With AI Across One Of Asia’s Largest Digital Businesses
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Driving 40% Faster Resolution with AI Across One of Asia’s Largest Digital Businesses

A leading consumer technology platform in Southeast Asia, enabling millions of daily interactions across customers, partners, and service providers in multiple markets. Its services support critical everyday activities such as mobility, commerce and digital payments, making platform availability a direct driver of customer trust, partner income, brand equity and revenue.

The Problem Statement

As the business scaled across markets and services, handling disruptions became harder to manage consistently. Outcomes depended too much on who was available at the time, creating variability in response speed and quality. The organization needed a more predictable way to protect customer experience and business continuity while reducing risk during high-impact, time-critical situations. 

Challenges

Fragmented Observability Context

Operational insights were distributed across multiple tools, increasing time required to build incident context.

Tacit Knowledge Dependency

Critical diagnostic knowledge resided with senior engineers, limiting scalable and repeatable incident investigations.

Delayed Root Cause Visibility

Significant time was spent correlating signals before meaningful remediation actions could begin.

Inconsistent Incident Handling

Response quality varied based on on-call experience, reducing standardization across similar incident types.

High Cognitive Load on SREs

Manual analysis and constant context switching diverted focus from proactive reliability engineering.

Solutions

AI-Driven Incident Orchestration

Introduced an intelligent reasoning layer to analyze alerts and coordinate automated investigative actions.

Parallel Context Aggregation

Simultaneously collected metrics, system state, historical incidents and documented procedures in real time.

Integrated Knowledge Retrieval

Unified operational data, past resolutions and SOPs into a single, accessible decision surface.

Standardized Response Execution

Ensured every incident followed consistent diagnostic logic aligned with documented best practices.

Privacy-Preserving AI Architecture

Leveraged local LLM to maintain data security while minimizing external dependency costs.

Outcomes

Mean Time to Resolution reduced by 40% through automated context gathering and intelligent reasoning.

Engineers received structured, actionable insights, minimizing manual analysis and context switching during incidents.

Incidents were handled uniformly using documented best practices, regardless of on-call engineer experience.

Resolved incidents continuously enriched historical patterns, strengthening future investigations and recommendations.

Local LLM processing ensured data privacy while significantly reducing external tooling and API expenses.

Faster & Secure Software Delivery With BuildPiper!!

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