NextStyl generates AI-styled outfit recommendations on every PDP, using only your own catalog — automatically, inventory-aware, no manual tagging. Lift AOV without hiring stylists or paying enterprise vendors $100K/year.
Single-item PDPs leave 30–40% of potential basket value on the table. Customers cobble together looks across tabs, abandon, and buy half the outfit somewhere else.
Your merchandising team can hand-style 200 looks a month. Your catalog has 4,000 SKUs and rotates seasonally. The math has never worked.
The look your stylist built last week shows two sold-out items today. Static lookbooks are obsolete the moment inventory shifts.
$60K–$150K/year minimums, 6-month integrations, and dedicated stylist teams. Fine for Macy's. Insane for a $20M DTC brand.
One script tag. We read your catalog, generate outfits, and render them on every PDP. Outfits regenerate the moment stock changes.
OAuth into Shopify, BigCommerce, or push a product feed. We auto-extract attributes — category, color, style, occasion, season — using vision + LLM tagging.
For every SKU, NextStyl generates 5 complete looks (top + bottom + footwear + accessory) curated for style coherence, color, and your brand's aesthetic.
A lightweight widget appears under each product. Visually integrated. Mobile-optimized. No template surgery required.
An item goes out of stock at 2:14 AM. By 2:14:30 AM, every outfit containing it is regenerated with a comparable in-stock substitute. Always purchasable. Always.
For the eng team that has to sign off. We answered your three questions before you asked.
Drop a single script tag in your global template. We auto-detect product pages, fetch your catalog via OAuth, and start rendering. No template surgery, no headless re-architecture.
The widget inherits your CSS variables, type scale, color tokens, spacing, and border radii from the host page. We render in your aesthetic, not a generic SaaS template. No iframes, no shadow DOM lock-in.
Loaded async after first paint. Below-the-fold by default. Outfit data served from edge CDN. Your LCP, CLS, and INP stay where they are — we measured it across 40 stores.
We're not claiming the AI has better taste than your merchandising team. It doesn't. What it has is scale — enough to propose 20,000 on-catalog outfits your team would never have time to build. Your team reviews, approves, edits, and ships only what fits your brand.
The AI can only assemble outfits using products you've already chosen to sell. No external SKUs, no off-brand surprises. Your merchandising decisions stay the source of truth.
Outfits enter a review queue. Your team approves what fits, swaps items they want changed, and rejects what misses. Nothing ships to your PDPs without your sign-off.
Today, every brand starts from general fashion logic. As we collect approve/reject signals from your team, we'll tune to your aesthetic specifically. We'll tell you when that ships — not pretend it already does.
Click between outfits. Imagine it on every one of your PDPs.
Sleeveless A-line silhouette in a breathable linen blend. The kind of piece a customer buys once and styles four ways — exactly what NextStyl shows them below.
Not screenshots. Not a sandbox. Click any tile to land on a real Complete-the-Look in production on a $1B+ apparel brand.
Enterprise CTL vendors are powerful and unaffordable. Shopify apps are cheap and shallow. NextStyl is the middle that didn't exist.
Industry benchmark for AI outfit modules is +15–25% AOV. Pull the sliders to see what that means for your store.
* Modeled estimates based on category benchmarks. Actual results depend on your traffic mix, product breadth, and creative — we'll model your specific numbers on the demo call.
No fluff. No "synergy."
Send us your store URL and we'll show up to the call with NextStyl already running on your products. If it doesn't look obviously better than what you have today, the call ends in 5 minutes.