AI-powered optimization delivers 70% lower job costs and improves data platform stability

A major US-based investment bank sought to improve the efficiency of scheduled Databricks jobs while strengthening the stability of its enterprise data platform.

• Inefficient workload patterns: Jobs frequently processed full datasets when only incremental changes were required, resulting in higher compute usage
• Suboptimal resource allocation: Cluster configurations were not aligned with workload characteristics, leading to over-provisioning and performance bottlenecks
• Rising infrastructure costs: Monthly cloud expenditure was increasing without clear visibility into cost drivers or a structured optimization framework
• Limited performance engineering expertise: The client team had strong pipeline development capabilities but limited expertise in Spark optimization and data platform best practices

The Crisil IIQ team built an AI-powered optimization agent to systematically identify and address inefficiencies across the data platform.

• AI-driven workload analysis: The agent analyzed scripts, execution logs and workflow patterns to assess code structure, workload behavior and resource utilization, identifying opportunities for optimization
• GenAI-driven performance playbook: The solution generated targeted code improvement recommendations, resource allocation strategies and best practices, including specific refactoring recommendations and optimal cluster sizing
• Self-service optimization framework: A repeatable methodology enabled the client team to independently optimize jobs using existing technical capabilities, while establishing a scalable approach to improving platform efficiency

The framework delivered measurable improvements in cost efficiency and platform stability.