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.

How does your team stay ahead of emerging technology trends while scaling fast?

We stay ahead of emerging technology trends by combining continuous learning with disciplined execution.

First, we make learning a habit. Our team regularly reviews leading tech blogs, research, podcasts and open-source projects to spot meaningful shifts early. We also attend major AI and engineering conferences to hear directly from builders and researchers, then bring back practical ideas to test internally. This helps us evaluate new technologies before they become mainstream.

Second, we stay focused. We have a clear strategy to build an AI-driven product that can scale for large businesses. That focus guides our investments. We prioritize strong engineering foundations, reliable infrastructure and modern AI development tools so new technologies can be tested and deployed quickly without creating instability or technical debt.

Finally, we balance build versus leverage. For rapidly evolving areas like large AI models, we integrate best-in-class external solutions. At the same time, we concentrate our internal efforts on the features that uniquely differentiate our platform. That balance allows us to move fast, scale responsibly, and continuously bring innovation into the product.

What recent product or feature are you most proud of — and what impact has it had?

The recent product we are most proud of is the launch of our AI Agents, the Marketing Agent and the Customer Agent, which power our vision for an autonomous B2C CRM. We designed the platform to be open, so customers can use Klaviyo-built agents or bring their own, whether that is Claude through a Model Context Protocol server or ChatGPT through the Klaviyo App — any agent, any model, with full customer context from one platform. Our agents are grounded in insights from hundreds of thousands of businesses and trillions of data points, enabling specific, revenue-driving decisions rather than generic output.

The impact is clear and measurable. More than half of campaigns created with Marketing Agent are now AI-generated, often performing as well as or better than manually built campaigns, while taking a fraction of the time to launch. Teams can run more high-quality campaigns without adding headcount. Customer Agent is driving similar results in service. Resolution rates have increased by 20 percentage points, and the volume of issues resolved each month has grown by more than 50 percent. Businesses are also seeing meaningful lifts in sales and average order value from AI-driven recommendations.

How do you create a culture where innovation and experimentation are encouraged daily?

At Klaviyo, we treat culture as a product. We’ve explicitly built it on rapid experimentation, fast learning and working autonomous-first. This encourages innovation to happen on a daily basis. 

Our teams are anchored in clear outcomes, “Know the score,” and encouraged to run fast iterations that move those metrics. We optimize for short cycles and learning though “Move fast, no shortcuts.” In practice, that looks like lightweight experiment briefs, which include hypothesis, success metric and guardrails, shipping in days or weeks, and treating “What did we learn?” as the primary success criterion, even when the result isn’t what we hoped for. 

We also emphasize high agency and ownership. “Drivers wanted” means the people closest to the problem are expected to act, not wait. If you see something broken, you own fixing it or pulling in the right people. With a focus on working autonomous-first, we encourage teams to experiment with different AI tools to develop our own processes and internal tools. 

Lastly, we stay hungry, stay humble, and operate as if we are 1 percent done. This mindset enables us to learn from our mistakes, experiment with new things every day, and adapt very quickly.