Fashion-Native AI
for e-com profit
Personalization and Outfit AI.
What leading fashion brands achieved with dresslife.
All figures from statistically significant A/B tests against a control group.
Generic recommenders weren't built for fashion.
Product lifecycle
Standard
Sells for years
Fashion
Under 90-day cycles
Product variants
Standard
Comes as-is
Fashion
Dozens of SKUs per product
Body dependency
Standard
No dependency
Fashion
Depends on the body — cut, and even color
Generic predictions are less accurate in fashion, because of its short cycles, variant explosion, and body dependency.
How dresslife drives uplift that generic engines can't.
Fashion-specific data
First-party fashion data — product images, size and stock coverage, real clickstream — not a generic catalog feed.
Fashion-specific AI
Specialized models for product, style, intent, and fit — combined into one recommendation, built for fashion's short cycles and variants.
Profit, not clicks
Optimizes for profit, not engagement — factoring price, discount, and availability into every recommendation.
Fashion-specific features
Outfit AI, availability handling, and merchandising overrides — capabilities a generic engine doesn't have at that level.
A 5% revenue uplift from dresslife drives +25% profit.
Profit without dresslife
€100M revenue · 10% baseline margin
Revenue uplift
a conservative, average uplift from dresslife's personalization
Margin impact
~50% gross margin — no extra ad spend, fixed costs already covered
Profit with dresslife
+25% profit — from just a 5% revenue lift.
Every 1% revenue increase ≈ +5% profit.
Illustrative at €100M revenue · 10% baseline margin · ~50% gross margin on incremental revenue · before subscription fee.
dresslife elevates profit and user experience across the full user journey.
Recommended for you
Real-time recommendations of what each shopper is most likely to buy next — at the best price, discount, and availability.
Outfit AI
AI-styled, AI-rendered shoppable looks from a single product image.
Similar items
Closest viable alternatives when a product is unavailable or commercially suboptimal.
Cart recommendations
The most relevant additions in the cart, at the moment of purchase intent.
Cross-category
View a t-shirt, get a matching jacket — moving shoppers across categories (footwear → apparel, jersey → lifestyle & accessories).
Category re-sort
Real-time PLP re-sort per shopper, up to the last click — factoring behavior, price, discount, and stock.
Ad-landing personalization
Personalizes the content and products a shopper sees right after they click an ad.
More journey levers
Same personalization layer, more touchpoints.
Live fast. Continuously optimized. Autonomous, under your control.
Live fast
Integrated in Shopify in under a day, under five days on other platforms — alongside your stack or replacing a generic engine. No rip-and-replace.
Continuously optimized
We deploy the features and optimize the AIs, end-to-end, for profit — like a CRO team you don't have to staff.
Autonomous, under your control
Can run fully autonomously. But you steer the calls that matter: prioritize products as signals the AI weighs, or as hard overrides.
Go live in under a day — first measurable uplift within 4 weeks.
Start
Retailer integration
Under a day on Shopify, under five days on other platforms.
1–4 weeks
Collect data & tune AI
dresslife collects product and user-event data and calibrates the recommendation AIs on your data.
5–12 weeks
Real uplift & A/B test
Uplift is live from week 4. The A/B test against your existing setup then confirms it as statistically significant.
Continuous growth
Maximizing uplift 5%+
We expand to new features and keep optimizing the AIs — conversion, margin, and profit improve over time.
Real uplift in 4 weeks — compounding growth from there.
See how it works — and what it could do for you.
