CAIS Innovation & Technology Culture

What types of products or services does your engineering team build? What problem are you solving for customers?

Our users often need to sift through a long menu of alternative investment products to extract information such as performance data, sector allocation and redemption terms. Relying on our sales or customer service teams to help locate the information often slows this process down. 

My team is focused on removing these friction points by building an AI-powered chat interface that integrates across our platform. It enables users to discover, compare and explore alternative investments available through the platform by simply asking natural-language questions.

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

For our product chat interface, we recently created the front-end app from scratch and AI proved useful in several ways.

One challenge was learning new technologies, such as Google’s Agent Development Kit (ADK). Using GPT-5 to ideate, explore architectural choices and understand how different components fit together was invaluable. Instead of parsing documentation and relying on trial and error, I could work through ideas interactively with the model. That back-and-forth sometimes surfaced approaches I might not have considered on my own, ultimately leading to a stronger implementation and, arguably, a more enjoyable development process, much like pair programming. 

Because we were working under tight deadlines, maintaining velocity was crucial. Claude Code, an agentic coding tool, helped us move beyond boilerplate quickly and deploy a functional prototype to our infrastructure for immediate testing. It also made it easier to adapt to large architectural changes, common when working with emerging technologies, allowing us to iterate and test different strategies much faster.

What would that project have looked like if you didn’t have AI as a tool to use? 

Without AI, setting up the app’s boilerplate, configuring deployments and building a proof-of-concept skeleton would have taken several extra days. Or, it would have required more developer support for areas I’m less familiar with, like our infrastructure repositories. Agentic tools helped me navigate those repositories efficiently and even ran multiple instances across domains, accelerating progress. 

Beyond the setup stage, AI remained valuable. It freed time to refine the product by reducing time spent on business logic; enabled rapid iteration through frequent architectural changes common in greenfield projects; and supported fast, parallel exploration of new features and UX ideas, especially with limited product and design resources. 

Overall, AI has shifted my focus away from mechanical implementation to creative problem solving. By compressing build time, it gives me more room to think critically about how to deliver better user experiences.