Enova Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks?

We stay ahead of emerging technologies through a thoughtful mix of research, experimentation and smart use of industry-leading tools. A dedicated group helps guide our focus, keeping close watch on new developments by reading the latest research, reviewing industry papers and attending conferences. This gives us a clear view of where the field is heading, such as the move toward more agentic AI and helps us understand how these shifts could shape our future work. Alongside this, we maintain an active experimentation pipeline where we explore new capabilities and push our systems beyond basic search. For example, we test advanced methods like retrieval-augmented generation and different ways of structuring data so our AI can better understand the relationships within our code, leading to more accurate and helpful assistance. To move quickly from ideas to real solutions, we build on top of strong vendor platforms such as Google Gemini and AWS Bedrock, giving us the ability to prototype and launch new tools faster without reinventing the wheel.

Can you share a recent example of an innovative project or tech adoption?

A great example of a recent project is our AI-powered code reviewer, ArchBot. We built it ourselves using Go on AWS Bedrock and it’s integrated directly into our GitHub environment to handle many of the routine tasks in the code review process. The benefits are clear: it keeps our code clean and consistent, speeds up development by giving senior developers more time back and can be customized for different team workflows. ArchBot is a prime example of how we use AI for “human assistance” — creating practical tools that help us today while also building our understanding of AI for the future. It’s a real, in-production tool that our engineering teams rely on every day.

How does your culture support experimentation and learning?

Our culture is built on encouraging new ideas and learning and we support this in several key ways. We have a clear path for taking ideas from small experiments to full products: We identify a specific problem, build a small test — like a tool for financial document processing — and if it works, we roll it out for everyone to use. At the same time, we strike a balance between quick wins and bigger bets. Tools like ArchBot deliver real value, build trust and give us the freedom to explore more cutting-edge research. Above all, our approach is people-focused. We develop AI tools to augment our teams, helping them work smarter on tasks like diagnosing complex issues and modeling financial scenarios, always with a focus on real-world value.