For modern SaaS teams, the old model of “build → launch → support” is no longer enough. What’s needed now is an architecture that brings feedback and intelligence into the product loop—faster and smarter. DevRev’s engineering blog offers a fascinating look at how they’re doing just that.
Their work on semantic search infrastructure highlights the role of vector embeddings and LLMs in making support and product discovery deeply contextual. Combined with the algorithms behind their AI-native vector database, they’ve built a foundation where machine-readable feedback becomes product action.
Equally interesting is how their no-legacy architecture enables this kind of innovation—freeing them to design systems that treat APIs, feedback, and support tickets as first-class components of the product lifecycle.
This holistic approach—bridging support intelligence, dev agility, and user visibility—reminds us that software isn’t just what you ship, it’s what you continuously learn from. DevRev’s transparent writeups are worth bookmarking for any product-minded engineering leader.
