The challenge
Turning curious teams into AI builders
A major U.S. apparel retailer was under growing pressure to figure out how AI could meaningfully improve customer experience across its digital storefront, but it didn’t have the internal capacity to move quickly on it. Ideating, prototyping, and validating AI concepts at the pace the market was setting would have meant pulling commerce and engineering teams off the work that was already keeping the business running, and even then, the team would have been learning the AI side from scratch. What the retailer needed wasn’t a vendor to ship a single feature, but a way to test multiple AI directions in parallel, separate the ones that earned production from the ones that didn’t fit the brand, and do all of it without disrupting the core business.
The solution
Approach: Parallel prototyping with a brand-fit evaluation loop
We stood up an AI Innovations Lab as a joint venture, pairing retail-domain research with applied AI experimentation so concepts could be evaluated for both technical feasibility and brand fit before anything reached customers.
The lab built a portfolio of prototypes against the commerce funnel rather than betting on any single use case. AI-generated review summaries distilled large volumes of customer feedback into concise, structured takeaways on fit, comfort, and quality, with particular attention to size-to-fit signals that influence return rates. A conversational AI assistant let shoppers describe a need in their own words (“a birthday gift for my mom,” “something for a beach wedding”) and surfaced relevant products through a multi-agent system that translated customer sentiment into style attributes, brainstormed directions, and re-ranked the catalog accordingly. A product comparison capability used AI to read product photos and descriptions and explain differences in plain language, including both measurable attributes like sleeve length and contextual ones like formality or warmth. A size recommendation model clustered shoppers by purchase, return, and fit patterns to predict the most likely fitting size at the point of purchase. A similar-products capability used vector representations of product descriptions and images to surface alternatives that preserved customer intent while expanding discovery.
Each prototype went through the same evaluation loop: technical validation, then a brand-and-strategy review, then a release decision. A prototype could perform well technically and still fail to fit the way the brand wanted to talk to its customers, and the lab was designed to surface that distinction early rather than late.
The outcome
Turning curious teams into AI builders
Review summarization shipped to production and is live across product detail pages, with an admin console giving the merchandising team direct control over how summaries surface to customers, particularly around fit guidance. The conversational AI assistant was A/B tested in production and held back from release: it performed well on the technical metrics, but the retailer’s brand strategy favors curated inspiration over algorithmic relevance, and the lab’s evaluation process surfaced that mismatch before a public launch could create one. Several other capabilities, including product comparison, size recommendation, and similar-products discovery, are at varying stages of validation and prioritization.
The deeper outcome is the engagement model itself. The lab gave the retailer a way to test AI broadly, validate quickly, and ship only what fits, without committing to a feature before knowing whether it belonged. That’s a repeatable pattern for any consumer brand trying to move fast on AI without putting brand integrity at risk.
About client
Apparel retailer
A major U.S.-based apparel retailer seeking to accelerate AI-led improvements to its digital customer experience without disrupting core commerce operations.
