Over the past year, I’ve noticed a shift in how AI products are being designed: instead of building broad “do-everything” generators, more teams are focusing on tightly scoped, vertical-specific workflows.
A recent example I came across in the real estate space uses AI to turn semi-structured listing data—descriptions, photos, and property attributes—into short-form marketing videos. What’s interesting from an engineering perspective isn’t the video output itself, but the underlying approach: domain-aware data extraction, reusable branding templates, and a constrained generation pipeline that reduces user input while keeping results consistent.
This kind of design highlights an important trade-off in AI tooling. By narrowing the problem space and encoding industry assumptions upfront, vertical AI tools can often deliver a smoother UX and more predictable output than generic models.
Curious to see how far this verticalization trend will go, especially in other industries with structured content and repetitive workflows.
Top comments (0)