Capital One Software, the enterprise B2B software business of Capital One, announced intelligent optimization features for Capital One Slingshot, designed to improve performance and quickly detect and resolve data infrastructure issues. By utilizing context across a user’s environment, Slingshot will identify opportunities to improve workload performance in Snowflake that go beyond basic SQL syntax and storage costs.

These features reflect a fundamental shift in how enterprises can approach data efficiency: not just tuning individual resources in isolation, but understanding and optimizing the entire system, including code, pipelines, infrastructure and the teams running them.

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“Enterprise data infrastructure is a complex web of inter-dependencies that requires a context-first approach for optimization at scale,” said Jeff Chou, VP, Slingshot Product Management, Capital One Software. “Slingshot’s intelligent optimization capabilities can help businesses understand what their queries are actually doing, what their tables are built for, and where their teams are unknowingly duplicating work. That’s how we help enterprises get efficient at the system level.”

Upcoming Slingshot features include:

• Context-Aware AI Query Optimization: Enterprise Snowflake environments generate staggering query volumes that no team can manually evaluate at scale. Slingshot will automatically identify top queries by cost, runtime, and frequency in Snowflake environments. It will generate AI-powered optimization recommendations that surface clear, actionable steps and project cost and runtime improvements for both Snowflake admins and data engineers.

• Context-Aware AI Table Optimization: Query inefficiency often doesn’t stem from the query itself, but from poorly configured tables. Slingshot’s Table Optimization capability will analyze the top 50 tables by query impact and surface multi-dimensional infrastructure fixes. Slingshot will also validate that proposed table changes will not negatively impact the top queries already running against that table, before surfacing the recommendation.

• Duplicate Pipeline Detection: Large enterprises have full pipelines that are unknowingly redundant. Slingshot’s AI-powered duplicate pipeline detection will identify these redundancies by looking at common patterns of data usage to find potential overlap. Slingshot uses AI to compare many potentially-similar workloads to evaluate functional equivalence.

• Data Explorer: This interactive, drill-down analytics interface will allow data teams to investigate root causes. Users can interactively slice Snowflake costs across various dimensions (accounts, users, query hashes, Slingshot tags, service types) with synchronized filtering. Drill-downs offer rich object detail pages for individual warehouses, databases, and queries, collapsing the gap between cost visibility and actionable change. Data rich before-and-after impact analysis pages provide historical context for any changes made to a Warehouse.