How A Leading E-Commerce Company Accelerated High-Performing Ad Creation Using AI

A prominent fashion ecommerce company using AI-driven recommendation engines was looking for a better way to harness the insights of its domain experts stylists, copywriters, and creative leads to enhance the quality of its AI-generated ads.

Among the earliest data science teams to integrate Databrewery into their stack, this team set out to innovate around personalized ad generation. Given that creative assets directly impact user engagement and revenue, they partnered closely with the marketing and creative teams. Like many in ecommerce, the company had access to millions of raw, unstructured product images, yet lacked an efficient way to transform these visuals into high-converting ad campaigns. Producing editorial-quality outfit combinations led to strong results, but the manual effort required made it unsustainable at scale. The goal was to automate asset generation both copy and visuals without compromising brand quality or performance.

To meet this challenge, the team employed natural language processing (NLP) to generate compelling headlines and computer vision to build relevant outfit pairings. Brewforce was embedded into their workflow as the key annotation and review system. It allowed human reviewersVstylists, editors, and marketers to assess, refine, and approve each asset, ensuring that every output aligned with brand expectations and user appeal.

The review process went beyond basic QA. Within Brewforce, domain experts could answer critical questions like “Does this outfit pairing feel cohesive?” or “Would this headline resonate with our audience?”, making it a dynamic environment for structured feedback. Instead of scattered cloud files and hard-to-track feedback loops, the entire team now collaborated through a central platform designed for speed and clarity. For copywriters, it meant no more juggling between spreadsheets and manual notes, Brewforce became their go-to workspace.

The shift was transformational. After a structured before-and-after test, the data team found that what used to take two weeks creating and validating a full set of personalized ad assets could now be done in just a few days. The automation of data handling, streamlined expert reviews, and simplified coordination across teams dramatically increased both productivity and output quality.

Today, the company runs close to a thousand active users across dozens of marketing and AI projects. With a single, centralized system for AI experimentation, content validation, and deployment, they’re not only pushing out better campaigns, they're doing it faster, with less friction, and with greater alignment between tech and creative functions.