Klaviyo Innovation & Technology Culture

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

We take a three-pronged approach to staying at the forefront of innovation: internal knowledge-sharing, external industry engagement and intentional research. Teams regularly share insights from sources like Hacker News, LinkedIn and academic papers in Slack and meetings. Our Boston and Silicon Valley teams stay connected to academia and industry leaders — including OpenAI, Anthropic and Meta — to exchange ideas and track trends. When exploring new domains, we organize focused reading groups, tap into recent academic research and empower interns and new grads to lead learning sessions — ensuring our teams remain informed, agile and ahead of the curve.

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

We’re innovating in product recommendations for email marketing and customer service by moving beyond static, history-based models. Our approach integrates conversational context, allowing agents to handle open-ended prompts like “a gift for my mom” in real time — bridging the gap between search and recommendation. While we use proven technologies like deep neural networks, the real innovation lies in how we apply AI to structure messy customer data. By cleaning and interpreting this data first, we turn Klaviyo’s data scale and depth into a competitive edge for precision machine learning applications — solving challenges that traditional search engines aren’t built to handle.

How does your culture support experimentation and learning?

We foster an engineering mindset grounded in curiosity, experimentation and continuous learning. Hackathons, quick prototypes and open knowledge-sharing help us explore ideas efficiently and collaboratively. On the tactical side, we’ve built deep observability into our stack to monitor and refine model performance and we own our own Statsig experimentation capability — enabling rigorous A/B testing to validate impact. This combination of culture and infrastructure empowers our teams to move fast, test boldly and deliver real value.

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At Klaviyo, I’m part of the K-Service group, where we’re building a new suite of products to transform the customer experience for e-commerce brands — before, during and after the sale. Think of it like creating an Amazon-style experience for Shopify businesses. Under this umbrella, we’ve developed tools like Customer Agent, an AI chatbot for pre-sales and support, Help Desk for human agents, powered by Klaviyo’s rich data, and Customer Hub, which brings personalization, merchandising and support together on the storefront. Our goal is to help e-commerce brands, whether emerging or scaling, deliver more intelligent, data-driven service that doesn’t just resolve issues but drives revenue and builds stronger customer relationships.

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?

I use AI every day both as an engineer and as a cross-functional partner to nearly 50 people across product and engineering. Because I move between teams often, context switching is intense. AI helps me stay on top of changes by summarizing code and system design updates, so I can quickly re-engage wherever I’m needed. Within Customer Agent, our AI-powered solution, we also use AI to accelerate how we learn and explore new domains. Whether it’s prototyping or clarifying a complex feature, AI helps us surface unknowns and quickly build expertise in areas that once required significant time and effort, enabling us to design the best possible AI UX for our customers.

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Customer Agent is a complex product, not just a single feature. Without AI, building it would be significantly slower, especially for engineers like me who don’t come from a machine learning background. AI surfaces approaches we wouldn’t know to look for and fills in critical knowledge gaps. It helps me uncover “unknown unknowns,” so I can upskill in real time and immediately apply those learnings to the work. On a practical level, we also use AI to rapidly prototype new features, test ideas and iterate quickly, allowing us to deliver value to customers faster. AI hasn’t replaced my role as a software engineer, but it has dramatically expanded what I can accomplish and how efficiently I can do it.

Klaviyo’s competitive advantage has always been its robust capabilities around data. One of the key reasons why Klaviyo stands out is its ability to harness and utilize data to drive results effectively.