AI Shopping Assistant AIショッピングアシスタント
Turn your own EC site into an EC site "that serves customers."
A conversational shopping assistant powered by generative AI. It picks up on customers' vague requests through natural conversation and recommends the best products along with the reasoning behind each suggestion. It prevents drop-off common with keyword-search EC, and conversation logs can be used as Voice of Customer (VoC) data for management decisions.
AI
Shopping AssistantOnline now
I'm looking for a wedding gift for a friend.
Congratulations! Could you tell me your budget and your friend's taste?
Around ¥10,000. She likes Scandinavian style.
In that case, how about this? It's simple and popular for everyday use.
Nordic Pair Mug Set¥9,800 (tax included)
Add to Cart
Challenges
小売DXにあたってのお客様課題と背景
Why Conversational Commerce, Now
With generative AI now widespread, customers have grown used to an experience where they can "consult in their own words and get suggestions.
" Meanwhile, most EC sites are still centered on keyword search. This experience gap is exactly where the difference is being made right now.
Search → Conversation
Use of conversational AI is expanding rapidly. Buying by "asking" rather than "searching" is becoming the new standard.
Comparison Shopping
More consumers than ever gather information and compare options online before buying.
Mobile-First
Most EC usage happens on mobile, where the difficulty of "finding things" on a small screen drives drop-off.
Four Challenges Facing Search-Based EC
Customer Touchpoint
A One-Size-Fits-All Search Experience
Keyword-dependent search can't capture vague needs like "something that just feels right."
Lost Opportunity
Rising Cart Abandonment
Even after viewing product details, customers aren't confident it's right for them, hesitate right before purchase, and leave.
Experience Gap
No Service Staff
There's no "consult while you shop" experience like in a physical store, which lowers site engagement.
Customer Understanding
Search Terms Only Reveal the "Means"
A query like "knit, 30s, gift" doesn't reveal the motivation.
The context of "for whom, and why" only emerges in conversation.
Our Solution
アジアクエストのソリューション
The Solution: AI Shopping Concierge
We station a "customer-service expert" who knows your products inside and out, right on your EC site.
An AI trained on your product data receives customers' vague questions and requests through natural conversation,
and recommends the best products along with the reasoning — delivering personalized service to each customer, 24/7.
Traditional EC
Type words into a search box
Dig through mountains of results alone
Hesitate, drop off, abandon cart
After Implementing AI Concierge
Say, "I want something like this"
AI probes further and proposes the best fit
Purchase with confidence
Prevents drop-off, turns hesitation into purchase
Mainly tag-based setup — start in as little as 4 weeks
Uses your existing EC cart and infrastructure as-is
Capabilities
アジアクエストの強み
What the AI Concierge Can Do
Six core capabilities delivered as a one-stop solution, from data integration to service, purchasing, and analysis.
Six core capabilities delivered as a one-stop solution, from data integration to service, purchasing, and analysis.
Data Sync
Automatic Product Data Sync
Automatically feeds product master data, inventory, and pricing into the AI, ensuring accurate recommendations based on always-current information.
Conversation
Natural-Language Service
Understands context to answer questions like "What's a good gift for X?" or "Is there something cheaper?"
Add to Cart
Seamless Path to Purchase
Adds items directly to the EC cart from the chat screen, keeping the search-to-purchase flow uninterrupted.
Checkout
In-Chat Checkout (Extension)
With the extension, complete checkout without ever leaving the chat, thoroughly reducing drop-off points.
Personalize
Personalization via Membership Data
Integrates with existing CRM/membership data to deliver 1-to-1 suggestions based on purchase history and preferences.
Insights
VoC Analysis from Conversation Logs
Accumulates and analyzes raw customer requests as text — feeding directly into product development, purchasing, and promotion decisions.
User Experience
顧客体験価値
A New Purchase Process Through Conversation
The natural flow of "consult → hear needs → propose → add to cart," all within the screen.
STEP
Consult
Enter a vague need, like "I'm looking for a wedding gift for a friend."
STEP
Hearing Needs
AI probes further — "What's your budget?" "What does your friend like?" — to zero in on the best suggestion.
STEP
Proposal
Multiple matching products are picked from your own data, each with the reasoning behind the recommendation.
STEP
Add to Cart
Add a liked product to the cart — and move to checkout — with one tap, right from the chat.
AI
Shopping AssistantOnline now
Welcome! What are you looking for today?
I'm looking for a winter moisturizing cream for sensitive skin.
Understood. From our low-irritation, high-moisture options, I'd recommend this.
Sensitive Moist Cream¥3,200 (tax included)
Add to Cart
Expected Impact 期待される効果
Expected Impact of Implementation
"Resolving hesitation" directly impacts sales, satisfaction, and long-term customer relationships.
Higher Conversion
Being able to consult builds the confidence to buy, increasing conversion.
Higher Average Order Value
Related suggestions and upsells increase the amount per purchase.
Longer Time on Site
Being able to consult increases engagement with the site.
Higher Satisfaction/CX
Buying with confidence drives repeat purchases and brand loyalty.
Maximized LTV
Contributes to long-term customer lifetime value, not just short-term sales.
Reduced Lost Opportunity
Converts moments of hesitation — which often lead to drop-off — into purchases through conversation.
Why it matters：By resolving the hesitation that keyword search could never capture, conversation pays off across sales, satisfaction, and long-term relationships alike.
Data as an Asset
データ資産
Understanding "Why They Want to Buy," Not Just "What They Bought"
Click history and purchase data only tell you the "result." Conversation naturally reveals the motivation and context behind a purchase — "for whom" and "in what situation" it will be used.
This first-party data drives your next moves in products, purchasing, and promotion.
Examples of Context That Only Emerges in Conversation
I want to give my mother something that makes laundry easier for her 60th birthday.
I have sensitive skin and dislike fragrance — what winter moisturizer would work?
I work from home more now, so I'm looking for sneakers I can wear as loungewear too.
Is there a shampoo my kid and I can use together?
What This Context Data Enables
Product Development/Assortment
Identify products customers "searched for but couldn't find," feeding into development and purchasing — revealing blind spots that click data can't show.
Marketing Initiatives
Understanding "why they want it" lets you directly discover copy and messaging angles that resonate.
Inventory/Demand Forecasting
Catches purchase "intent" early, reducing both lost sales opportunities and excess inventory.
Use Cases
小売・流通業における活用シーン
Use Cases in Retail & Distribution
Sensory preferences, specs, and personal concerns — the kind of consultation that's hard to search for is exactly where AI excels.
Apparel
Styling Suggestions Tailored to Taste and Body Type
What to wear on a date tomorrow
A coat that suits someone 150cm tall
What accessories go with this outfit?
Electronics
Answers Complex Spec Comparisons and Expert Questions
An air purifier for a 10-tatami room
What's the difference between Model A and B?
What do you recommend for a ¥50,000 budget?
Beauty
Consulting Tailored to Skin Type and Concerns
Winter moisturizing cream for sensitive skin
A lipstick that suits cool undertones
I'm concerned about acne scars
Roadmap 導入ステップ
Implementation Steps: A Small Start in as Little as 4 Weeks
Agile hypothesis validation → production → expansion, with clear deliverables at each phase.
PHASE
0
Week 1
Assessment
- Review product data and existing EC environment
- Align on goals and KPIs
- Decide target categories and scope
Deliverables: requirements summary / scope definition
PHASE
1
Week 2-4
PoC (Hypothesis Validation)
- Sync product data and run initial AI training
- Design and tune conversation scenarios
- Demo and measure impact via limited release
Deliverables: working demo / impact report
PHASE
2
Week 5-10
Production Build & Launch
- Integrate via API with the EC cart
- Apply design/branding
- Production release and operational design
Deliverables: a live, production-ready assistant
PHASE
3
Ongoing
Expansion & Improvement
- Expand with payment/membership data integration and more
- Turn VoC analysis into regular reporting
- Continuously improve conversation accuracy
Deliverables: ongoing growth operations
Future
未来像
Beyond the Chatbot
From an EC site that "serves customers" to an autonomous "AI agent commerce." Capabilities expand as data and integrations grow
— and you can advance to the next stage smoothly, in line with your company's DX roadmap.
NOW
Conversational Recommendations
Picks up on vague needs and suggests the best products through conversation.
NEXT
Voice & Multimodal
Supports consultation by voice or image — even "find clothes like this photo."
OMO
Integration with Stores & Inventory
Extends to in-store pickup and inventory checks, seamlessly connecting online and in-store.
AGENT
Autonomous AI Agent
Autonomously handles reordering, reservations, and optimal suggestions — evolving into a 1-to-1 "dedicated concierge."
Why AsiaQuest アジアクエストが選ばれる理由
A Partner for Co-Creating "Quick Wins"
Speed and flexibility. We support you with agile PoC engagement all the way through to your own self-sufficiency.
400+
Engineers
End-to-end coverage from apps to cloud to data infrastructure — participating in development with real technical strength.
500+
AWS Certifications
Covers 100+ technologies, with strength in AI and cloud modernization.
PoC
Agile-First
Rapid hypothesis validation in weeks to months. Creating value early with a small start.
Hands-On
Close to the Ground
Works closely alongside your team, embedding know-how in your organization. Supports internalization and self-sufficiency.
Ready to Turn Your EC Into One That Serves Customers?
We'll propose an approach to the AI shopping assistant tailored to your products and EC environment.
Inquiries at the concept stage are also welcome.
Inquiries at the concept stage are also welcome.