Many companies struggle to harness the full potential of AI and data engineering, often overwhelmed by the complexity of adoption and the challenges of integrating legacy systems with modern cloud-native solutions. Our AI and Data Engineering practice is designed to simplify this journey, enabling businesses to securely leverage AI models like sentiment analysis and churn prediction while building scalable, compliant data architectures. By integrating AI into business processes and delivering data solutions tailored to specific organizational needs, we help companies unlock actionable insights, drive automation, improve operational efficiency and to put it bluntly, achieve the impossible.

The Case for Intelligent Agents in Financial Services

Legacy decision-making processes in financial services—fraud checks, loan underwriting, customer onboarding—rely on a patchwork of manual workflows, overnight batch jobs, and disconnected systems. This leads to slow turnaround times, inconsistent compliance enforcement, and poor customer experiences.

Agentic AI architectures solve these challenges by combining real-time event streams with AI agents capable of retrieving knowledge, reasoning with context, and taking actions. By layering agents on top of event-driven pipelines, organizations gain systems that not only deliver insights in real time, but also trigger next-best actions—approvals, escalations, notifications—automatically.

Our Agentic AI practice builds on Confluent’s event streaming backbone to capture and curate events, then routes them into AWS where Bedrock Agents enrich, reason, and act. This approach unifies operational data with AI-driven workflows, creating intelligent, compliant, and explainable decisioning systems that adapt to changing policies and market conditions.