Conversational Product Discovery
Turn ordinary shopper language into relevant, catalog-grounded product choices. WardrobeIt understands the need, asks focused questions, searches your approved catalog, and explains why each product matches.
Capture occasion, style, budget, size, fit, and urgency.
Ask only the missing question that improves relevance.
Use approved products, variants, pricing, and inventory.
Show why each result fits the shopper’s request.
Product Discovery in Action
Some shoppers know the product. Others only know the event, budget, style, problem, or result they want.
Shop by occasion
WardrobeIt combines the occasion, venue, style, size, color, and total budget before searching the merchant catalog.
I need an elegant but comfortable outfit for an outdoor engagement party.
Got it. I’ll focus on occasion-appropriate dresses that feel polished outdoors.
Size medium. Prefer soft neutrals. Keep it under $250.
I found dresses that match the occasion, venue, size, and budget from this store’s current catalog.
Shopper Language to Product Intent
WardrobeIt structures everyday language into product requirements that can be matched with merchant data.
“Something to wear over a dress on a cool evening.”
Outer layer · Wrap · Cardigan
“Formal work shoes for several hours of standing.”
Formal · Comfort priority
“Modern and elegant, but not decorative.”
Minimal · Understated
“Dress under $180, full look under $300.”
Item and basket limits
“Medium, with room around the waist.”
Variant · Silhouette
“Lightweight and breathable, not heavy satin.”
Preferred and excluded
“I need it before Friday.”
Approved delivery context
“I like it, but I am worried about fit and returns.”
Hesitation signals
Beyond Search and Filters
Traditional search expects shoppers to translate a complete need into catalog terminology. WardrobeIt lets them explain it naturally.
Traditional Product Search
Every requirement becomes another keyword, menu, filter, or manual comparison.
WardrobeIt Discovery
WardrobeIt combines the request, asks one focused question, and returns contextual product choices.
Intelligent Follow-up Questions
A guided experience should not feel like a questionnaire. WardrobeIt identifies the missing requirement with the highest relevance value.
One useful question
The answer immediately changes which products, materials, silhouettes, and levels of formality should be considered.
Improved discovery context
Catalog-Only and Inventory-Aware
Every result remains grounded in approved product information, current variants, pricing, and applicable availability data.
Results with Clear Reasoning
A recommendation becomes easier to trust when the assistant connects it directly to the shopper’s stated needs.
Sage Pleated Midi
$149
Linen Wrap Dress
$169
Soft Satin Midi
$129
Flexible Discovery Experiences
A product name is only one starting point. WardrobeIt supports occasion, style, budget, color, product need, similarity, availability, and complete-look journeys.
Mobile discovery
A guided shopping journey in the shopper’s hand.
Shop by occasion
Weddings, work, travel, gifting, exercise, or everyday use.
“Find an elegant outfit for an outdoor engagement party.”
Shop by style
Minimal, modern, relaxed, formal, modest, or statement.
“Show clean silhouettes with no bold prints.”
Shop by budget
Keep individual recommendations and complete looks within range.
“Build a complete look under $250.”
Find alternatives
Suggest the closest available size, color, silhouette, or price alternative.
“Find the closest option available in medium.”
Describe a product need
Understand the desired use or result, then map it to suitable product types.
“A lightweight layer for a cool evening.”
Discover similar products
Compare silhouette, material, price, color, and use case.
“Similar to this dress, but with long sleeves.”
Merchant Discovery Controls and Intelligence
The assistant follows merchant catalog boundaries and merchandising decisions. Discovery activity then becomes useful demand and content intelligence.
Outcomes and Product Discovery Proof
Measure conversational product discovery through engagement, recommendation quality, product-page progression, and demand signals — using clear event definitions, not unsupported performance claims.
Review time to first useful recommendation and how quickly shoppers reach a relevant product set.
Track product-card opens, detail visits, and follow-through on catalog-backed recommendations.
Understand which products and match reasons generate the strongest shopper interest.
See how shoppers move from conversation into relevant product pages within your store.
Review selection, variant choice, and cart progression after a supported discovery journey.
Surface zero-result requests, missing attributes, and recurring price or availability gaps.
Conversational product discovery helps shoppers find suitable products by describing what they need in ordinary language—occasion, style, budget, color, fit, or use case—instead of relying only on keywords and filters. WardrobeIt structures that request, searches the merchant’s approved catalog, and explains why recommended products match.
WardrobeIt captures the shopper request, clarifies only what improves the result, maps language to product attributes, searches approved catalog products and available variants, ranks relevant options, and returns recommendations with clear match reasoning. Merchants can also review demand signals from discovery activity.
Traditional site search usually depends on keywords, category browsing, and filters. Conversational discovery accepts natural shopper language, asks focused follow-up questions when needed, and returns catalog matches with explanations—so shoppers can start from an event, budget, style, or product need rather than an exact product name.
Yes. WardrobeIt is built Shopify-first for V1, including store connection, catalog and inventory sync, storefront discovery experiences, supported cart actions, and checkout redirect where the merchant’s configuration allows it.
No. WardrobeIt recommends products from the merchant’s approved catalog only. It does not return open-web results, competitor inventory, or unauthorized marketplace products.
When a preferred option is unavailable or no strong match exists, WardrobeIt can suggest merchant-approved alternatives—such as the closest available size, color, silhouette, or price option—or clearly state that no suitable product is available. Zero-result and gap signals can also help merchants improve catalog readiness.
WardrobeIt is designed to stay inside merchant-approved catalog boundaries, current product data, availability logic, and merchandising rules. It should not invent specifications, prices, inventory, or policies. When information is missing or uncertain, responses should be qualified rather than guessed.
Pricing depends on store size, catalog complexity, and implementation scope. Staging does not publish a public pricing page yet—qualified ecommerce teams can book a discovery demo or apply to the Merchant Success Program to review fit and commercial terms.
Improve Ecommerce Product Discovery
Turn natural shopper language into structured intent, relevant catalog choices, clear match reasoning, and a confident next buying action.
