Shaupa retailer pitch

Turn messy shopping intent into confident product decisions.

AI product discovery for Australian retailers.

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Shaupa proof asset showing a shopper outfit recommendation board
Existing Shaupa proof asset. Retailer pilots can use the same flow with agreed catalogue scope, tone and measurement rules.

Shopper behaviour

Shoppers do not search like databases.

Intent is messy

People describe occasions, rooms, fit problems, budgets, styles, materials, and uncertainty.

Filters miss context

Keyword search is useful for exact terms, but weak when shoppers need guidance across trade-offs.

Generic bots lack catalogue depth

Retailers need answers grounded in real products, attributes, availability, and brand rules.

Insight disappears

Teams often cannot see what shoppers were trying to find when search sessions go nowhere.

Why now

AI assistants are moving from novelty to retail infrastructure.

Australian retailers are beginning to expect conversational discovery that feels practical, measurable, and safe to trial. Competitor proof shows demand exists when the product is packaged around demos, implementation paths, pricing, and customer stories.

Retailer-safe proofDemos, pilots, controls, feed setup, and clear buyer language.
On-site expectationConversational search is becoming part of how shoppers expect to browse.
Urgency without panicThe window is open for focused Australian category demos.

What Shaupa is

An AI shopping assistant for conversational product discovery.

Shaupa helps shoppers describe what they need, refine through conversation, and reach relevant products faster.

ConsumerShoppers search naturally across product ideas.
BrandedRetailers can launch a hosted assistant around their catalogue.
EmbeddedThe assistant can sit beside existing site search.
APIRetailer UI can call Shaupa discovery behind the scenes.

Pilot flow

Three steps from natural language to product action.

1

Shopper asks naturally

“Find black shoes for wide feet that I can wear all day.”

2

Shaupa understands intent

Constraints, category, fit, style, budget, material, and context are mapped to catalogue data.

3

Products and guidance appear

Relevant products, follow-up questions, and retailer product-page links help shoppers keep moving.

I need a comfortable sofa for a small apartment under A$2,000. I need a gift for my mum who likes minimalist jewellery under A$150.

Deployment options

Start light, then deepen the integration.

01

Referral partner

Relevant products appear in Shaupa discovery journeys and send shoppers to the retailer’s own product pages.

02

Hosted branded page

A shaupa.com/yourbrand style experience for focused category and campaign pilots.

03

Embedded assistant/search

A guided discovery layer on the retailer site, placed beside existing search or category pages.

04

API/white-label

The retailer controls the interface while Shaupa powers intent understanding and ranked product discovery.

Retailer outcomes

A clearer path from shopper language to catalogue learning.

Shaupa focuses on operational outcomes retailers can observe during a pilot: better product discovery, fewer dead-end searches, higher-quality product clicks, more shopper confidence, and faster learning from real customer language.

Category and collection signals Top requested styles and budgets Common follow-up questions Products clicked or saved Weak-result query themes Brand and product priority controls

Sample insight layer

See the demand that search filters never captured.

Intent analytics can help teams understand what people ask, where discovery is weak, which products receive attention, and what merchandising opportunities are emerging.

Pilot insight readoutSample data only
Top request themeWide-fit black shoes

Specific comfort language points to missing fit attributes.

Weak-result queuePetite linen sets

Repeated demand, low confidence due to inconsistent sizing copy.

Merchandising signalMinimal gold gifts

Gift language crosses product type and needs curated style tags.

Product attention132 sample sessions

Clicks and saves concentrate around complete-decision contexts.

View the sample report format

Why Shaupa

Sharper focus, faster proof, and a more natural shopper experience.

Demand is proven

Existing retail AI assistants show buyers are open to this category when the demo is concrete.

Consumer-grade UX

Shaupa can make discovery feel like a helpful shopping conversation, not a generic support flow.

Australia-first focus

Local categories, language, budgets, retailers, and pilot relationships can become an advantage.

Category-specific demos

Fashion, footwear, furniture, gifts, jewellery, and accessories are strong early proof areas.

Pilot offer

A low-risk design-partner pilot.

Use a retailer catalogue, feed, API, or CSV. Build a branded demo. Run a focused pilot alongside existing search. Measure shopper questions, product clicks, weak-result queries, and qualitative discovery quality.

Pilots and monthly SaaS plans available. Pricing depends on deployment model, catalogue complexity and usage. Early design-partner pilots available for selected Australian retailers.

  1. Week 1Catalogue setup and discovery scope
  2. Week 2Branded demo and query tuning
  3. Weeks 3-4Focused pilot and insight review
  4. NextChoose SaaS, referral, or hybrid model

Demo queries

Queries built for real shopping language.

Find black shoes for wide feet that I can wear all day. Build a winter outfit for dinner in Melbourne under A$300. I need a comfortable sofa for a small apartment under A$2,000. Find a housewarming gift for someone who likes warm minimalist decor. Show me gold earrings that feel simple but still special. Find a work bag that fits a laptop and looks polished. Help me choose a rug for a sage and walnut living room. Find sandals for travel that are dressy enough for dinner.

Next step

Show shoppers the products they were trying to describe.

Request a branded demo and see how Shaupa can turn your catalogue into a conversational discovery experience.

Request a branded demo View sample insight report Email [email protected]