Kasisto, the market leader for AI in banking, announced KAIops, an operational intelligence layer on the KAIgentic platform built for financial institutions. KAIops brings agentic AI to the center of application and infrastructure operations to materially lower incident costs, accelerate resolution, and protect customer trust.
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KAIops empowers banks with agentic AI Ops to cut outages, accelerate resolution, and deliver zero-incident, self-healing operations.
Banks spend significant budgets and thousands of hours each year investigating critical outages and writing root cause analyses. KAIops changes the model. Its multi agent framework monitors system health, correlates telemetry, drafts RCA narratives, and executes approved responses, so teams move from hunting to fixing and from fixing to preventing.
“Every major bank spends weeks on root cause analysis after a P1 outage, time lost, customers impacted, and hundreds of millions of dollars spent annually trying to understand what went wrong,” said Lance Berks, CEO of Kasisto. “KAIops changes that. Built on our KAIgentic platform, it shifts operations from reactive to predictive, from investigation to prevention. This is the future of banking operations: reduced incidents, eliminate guesswork, and achieve measurable cost savings at scale.”
“KAIops represents the next evolution of our agentic architecture, a system where AI agents collaborate, learn, and act independently across operational environments,” said Joshua Schechter, Chief Product and Innovation Officer at Kasisto. “By combining preprocessing intelligence, autonomous orchestration, and post-processing validation, KAIops creates a self-healing operational fabric for banking systems. It is the foundation for a future where every process is intelligent, auditable, and adaptive.”
KAIops delivers a three-phase operating model available . In Phase 1, agents ingest logs, metrics, traces, tickets, and changes to cut time to acknowledge and resolve while drafting RCAs ready for review. In Phase 2, agents execute approved runbooks and remediations such as restarting services, clearing cache, scaling resources, and rolling back components with role-based approvals and full audit trails. In Phase 3, agents evaluate planned changes, model dependencies, forecast risk, and recommend mitigations to prevent incidents before customers feel them.
Across all phases, controls include read only access where appropriate, step approvals, reversible actions, and change governance integration, delivering lower incident cost, faster RCA completion, fewer escalations, and fewer customer impact minutes.
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