Three fashion players are teaming up to make digital styling feel like a one-on-one session with a boutique expert. Vivrelle, the luxury rental membership platform, has partnered with Revolve and FWRD to unveil Ella, a conversational AI stylist that curates head-to-toe looks by searching inventory across all three catalogs—then routes everything through a single checkout on Vivrelle.
How Ella builds outfits across three catalogs
Ella functions like a personal stylist you can message. Shoppers can describe the occasion, vibe, budget, sizing constraints, and even preferred silhouettes—think “sunset reception on the beach,” “work-to-dinner capsule,” or “festival weekend staples.” The assistant pulls pieces from Revolve’s trend-driven selection, FWRD’s designer roster, and Vivrelle’s rental inventory (with optional pre-owned where available), then assembles a cohesive look with accessories to match.
Because the tool spans three distinct assortments, it can balance mix-and-match goals: rent a statement bag from Vivrelle, buy a new-season dress on FWRD, and add a budget-friendly layer from Revolve. Fit and availability are accounted for in real time, and the purchase or rental lands in a single cart, streamlining a step that usually forces shoppers to juggle tabs, logins, and shipping windows.
Ella improves as customers engage. Preference signals—colors you save, fabrics you skip, heel height tolerance, even how often you repeat categories—help refine subsequent suggestions. The aim is to compress the messy “scroll and compare” phase into a few natural-language prompts and a short list of confident picks.
Why retailers are chasing AI-driven personalization
Fashion has chased the holy grail of personalized styling for decades. What’s changed is the ability to merge large language models with granular catalog data and customer context. According to McKinsey’s Next in Personalization research, effective personalization can unlock a 10% to 15% revenue lift on average, with leaders seeing significantly higher gains. The same report found that most consumers now expect tailored experiences and feel frustration when brands miss the mark.
There’s also a returns angle. Apparel e-commerce has long struggled with high return rates, often well into the teens, with fit and styling disappointment among the drivers, as noted by the National Retail Federation and other industry trackers. A stylist that guides shoppers toward better-fitting, occasion-appropriate picks can reduce bracketing and post-purchase remorse, and in turn decrease reverse logistics costs that erode margins.
The cross-retailer approach is particularly notable. Consolidating rental, resale or pre-owned, and retail into one experience gives customers flexibility—splurge on a forever piece, borrow a trend, and fill gaps with everyday staples—without the friction of separate carts. That omnichannel blend also creates richer data loops, which feed smarter recommendations over time.
Beyond “complete the look” add‑ons
Vivrelle, Revolve, and FWRD previously collaborated on a checkout companion that auto-suggested finishing touches based on what was already in the basket. Ella moves the conversation to the very start of the journey. Instead of reacting to a single item, it asks about occasions, budgets, and personal style, then composes a full wardrobe moment—top, bottom, shoes, bag, jewelry—tailored to the prompt and the user’s historical preferences.
In practice, that shift matters. Most recommendation engines optimize for incremental add-ons; a proactive stylist optimizes for intent satisfaction. Retail leaders increasingly track intent resolution metrics—how often the shopper feels “done” within one session—because they correlate with repeat purchases and loyalty.
Under the hood: data, fit logic, and privacy
Ella’s effectiveness hinges on clean product data and fit signals. Attributes like fabric stretch, rise, heel height, garment length, and brand-specific sizing run are essential for accurate pairing. Expect the system to blend first-party data (wishlists, past orders, rental frequency) with contextual cues (location, season) to narrow results. Clear controls to edit or reset preferences will be key to trust, alongside compliance with privacy frameworks such as GDPR and CCPA and transparent handling of rental-versus-retail data.
Retailers also tend to add guardrails—filtering out sold-out sizes, avoiding duplicate category clashes, and respecting budget caps. The best implementations expose quick toggles (e.g., “swap heels for flats,” “make it office-ready”) so customers can steer recommendations without rewriting their prompt.
Competitive context and what to watch
Fashion is testing AI assistants across the board—Zalando piloted a conversational stylist for outfit discovery, Stitch Fix has long leaned on algorithmic styling, and marketplaces are weaving AI into search and fit assistants. The differentiator here is the tri-brand model and unified checkout that fuses rental, pre-owned, and new into one journey.
Key metrics to monitor will include conversion rate on Ella-led sessions, average order value when rental and retail are combined, repeat usage, and changes in return rates for AI-styled orders. Seasonal use cases—weddings, travel, festivals, back-to-office—should reveal where the assistant most effectively reduces decision friction. If those signals trend positive, expect more multi-retailer alliances to follow, with styling assistants becoming the default point of entry rather than a novelty tucked into the cart.