Babylist Innovation & Technology Culture

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

We build commerce at Babylist: search, pricing, checkout and handling out-of-stock items for baby registries across web and mobile. When someone’s carefully chosen stroller disappears, it’s not just lost revenue; it’s someone panicking about whether they’re ready for their baby. We focus on transparent pricing, honest stock status and good alternatives when things go wrong. We handle the technical complexity of e-commerce but for purchases people have put real thought into.

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?

Our OutOfStock project tackled what happens when registry items become unavailable across platforms. I did the technical shaping: architecture, service design and rollout strategy for web, iOS and Android. I used AI to draft the technical specification document. I gave it context about our existing systems — how our product data is structured, our API patterns, services we’d integrate with — and worked with it to design the solution. It helped me think through things like how the API for product alternatives should work, what logic to use for recommending similar items and how to structure the work across three platform teams.

AI also helped with risk analysis. It caught edge cases I might have missed, like what happens when there are no good product alternatives and potential performance bottlenecks with stock status checks. It generated code examples, API response formats and implementation phases that I could refine. The whole team works this way now. We treat AI as shared infrastructure for how we coordinate and share context across projects.

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?

Without AI, writing that technical spec would have taken way longer. Probably a week instead of a day or two for all the API designs, code examples and work breakdowns across teams. The constant switching between thinking about architecture and writing detailed documentation is exhausting. AI let me stay focused on the design decisions while it drafted the specifications.

The broader change is that our entire engineering team collaborates differently now: better documentation, more consistent patterns and faster onboarding. AI speeds up the planning and coordination work, which frees up time for the actual engineering problems: keeping stock status data accurate and performant and building a recommendations system that surfaces useful alternatives.