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Kasisto, the market leader in AI for banking, today announced the launch of KAIops, an operational intelligence layer built on its KAIgentic platform. Designed specifically for financial institutions, KAIops brings agentic AI to the center of application and infrastructure operations—enabling self-healing systems, faster incident resolution, and reduced operational costs.
Kasisto introduces KAIops, an agentic AI-powered operational intelligence system for banks.
Built on the KAIgentic platform, it enables predictive, self-healing operations.
Helps financial institutions lower incident costs and accelerate RCA completion.
Automates telemetry monitoring, RCA drafting, and approved response execution.
Operates across three phases: detection, remediation, and prevention.
Available globally for banks and credit unions at kasisto.com/kaiops.
Banks collectively spend millions of dollars and thousands of hours each year investigating and resolving critical outages. Traditional root cause analysis (RCA) processes often span weeks, impacting customers and increasing operational costs.
KAIops transforms this model with its multi-agent framework, which continuously monitors system health, correlates telemetry, drafts RCA narratives, and executes authorized responses. By shifting from reactive to proactive operations, financial institutions can reduce downtime, accelerate recovery, and build customer trust.
“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 Kasisto’s agentic AI architecture, where intelligent agents collaborate autonomously across operational environments.
“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.”
The system’s architecture integrates preprocessing, autonomous orchestration, and validation to ensure every process—detection, action, and audit—is governed with accuracy and compliance.
KAIops operates through a three-phase intelligence model that enables continuous improvement across IT and operational ecosystems:
Phase 1: Detection and Correlation
AI agents ingest logs, metrics, traces, tickets, and changes to reduce time-to-acknowledge and time-to-resolve, while generating RCA narratives for review.
Phase 2: Autonomous Remediation
Agents execute approved runbooks—such as restarting services, clearing cache, scaling resources, or rolling back components—with role-based approvals and full audit trails.
Phase 3: Prevention and Risk Forecasting
Agents evaluate planned changes, model dependencies, forecast risks, and recommend mitigations before issues reach customers.
Across all phases, KAIops maintains strict governance with read-only permissions where necessary, reversible actions, and integrated change management controls—reducing incident costs, escalations, and customer impact minutes.
Kasisto is the market leader in agentic AI platforms purpose-built for the banking industry. Trusted by financial institutions worldwide, Kasisto delivers intelligent, compliant, and auditable AI experiences that transform how banks operate.
Its platform orchestrates autonomous AI agents that work securely within banking’s regulatory and operational frameworks. At its core is KaiGPT, a proprietary large language model tuned specifically for banking—enabling domain-specific accuracy, zero-risk reliability, and flexible deployment.
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