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Retail AIMulti-language SaaSMulti-tenant Platform

Smart Stack Advisor: AI Supplement Advisor for Retail Stores

How Inventiple's engineering team — through partnership with Exiliensoft — delivered the AI scoring engine, multi-language LLM integration, multi-tenant SaaS architecture, and live inventory sync for an AI supplement advisor now live in retail stores across Thailand, the UK, and the US.

+68%
Avg. basket uplift
11
Languages supported
3
Countries live
60s
Customer journey
Client
Smart Stack Advisor Pte. Ltd.
Year delivered
2026
Delivery model
Staff augmentation via Exiliensoft
Live markets
Thailand, UK, US
Stage
Production · Pilot expanding
About the client

An AI supplement advisor that turns every retail store into an expert consultant

Smart Stack Advisor (Singapore-incorporated) was built around a specific problem in the sports nutrition retail industry: staff knowledge varies wildly between stores and shifts, language barriers limit who can be served, and customers walk out with generic advice that doesn't match their actual goals or budget. The founders wanted to give every retail customer the same level of expert guidance — regardless of which store, which language, or which staff member was on shift that day.

The vision: a customer walks into a store, scans a branded QR code on the counter, answers 9 questions about their goals, training, diet, experience, and budget — and receives a science-backed, tiered, personalized supplement stack in under 60 seconds. In their native language. With every product matched to that store's actual live inventory.

The Challenge

A retail problem disguised as an AI problem

Pure AI demos are easy. Embedding production AI into the physical-retail journey is not. The founders needed a system that worked for international tourists who didn't speak the local language, respected each store's actual inventory in real time, ran safely without making medical claims, and could be deployed across hundreds of independent retail locations without per-store engineering work.

Existing off-the-shelf AI tools failed at one or more of these — they either weren't multi-language, weren't multi-tenant, or had no path to real inventory data. The founders needed a custom system built right.

The Solution

A multi-tenant SaaS with LLM integration architected for retail scale

Through partnership with Exiliensoft, Inventiple's senior engineering team delivered the core technical architecture: a multi-tenant SaaS where every retail store gets its own branded experience (logo, colors, product catalogue, QR codes) without any per-store deployment work.

On top of that foundation, we built the AI product scoring engine evaluating 25+ products per session with strict safety guardrails, the multi-language LLM integration supporting 11 languages (auto-detected from the customer's device), and the live inventory sync connecting to Shopify, WooCommerce, and custom retailer systems via API.

The Result

Production SaaS live in 3 countries with verified retail outcomes

Smart Stack Advisor is live across retail locations in Thailand, the UK, and the United States. Store owners publicly report a 68% average basket size uplift when customers arrive with their personalized Smart Stack plan versus customers shopping unguided.

The 11-language capability has been particularly impactful in tourist-heavy markets like Phuket, where international visitors who couldn't previously communicate with English- speaking staff now receive complete personalized advice in their own language — and convert.

Live at smartstackadvisor.ai.

+68%basket uplift
Reported by store owners on customers using Smart Stack plans
11languages
Auto-detected per customer · global retail ready
25+products scored
Per session with safety guardrails enforced
0personal data
Zero PII collected · session-temp architecture
What we built

Eight integrated platform capabilities

Through our partnership with Exiliensoft, Inventiple's engineers delivered these core technical modules — the foundation the rest of the product is built on.

01

AI product scoring engine

Custom LLM-backed engine that scores 25+ products per session against the customer's goals, training style, dietary needs, experience level, and budget. Strict guardrails enforce supplement safety boundaries — no medical claims, no upsells beyond what was asked for.

02

11-language LLM integration

Multi-language support across English, Thai, Russian, Chinese (Simplified), French, Spanish, German, Japanese, Korean, Arabic, and Portuguese. Auto-detected from the customer's device language with seamless fallback handling.

03

Multi-tenant SaaS architecture

Every retail store gets its own fully branded experience — custom logo, color palette, active product configuration — managed from a single admin panel with no per-store deployment. Hundreds of stores can be onboarded without engineering work.

04

Live inventory sync

API integrations with Shopify, WooCommerce, and custom retailer systems. The AI only recommends products that are actually in stock at that specific store — eliminating the 'we don't have that' moments that kill conversions.

05

QR code generation & tracking

Branded QR codes generated per store, per placement (counter, shelf, mirror, POS, receipts). Each placement tracked so store owners know which positions drive the most sessions and revenue.

06

Tiered recommendation system

Every customer receives Essential, Performance, and Elite tier options matched to their stated budget. Naturally showcases the full premium range while respecting affordability — driving average order value without hard selling.

07

Privacy-first session architecture

Zero personal data collected. No names, emails, or PII. Sessions are temporary and tied only to store-specific QR codes. Customers engage honestly without privacy concerns, which materially improves recommendation accuracy.

08

Real-time retail analytics

Non-personal session-level analytics surface session counts, popular goal categories, recommendation patterns, and product demand signals. Store owners see what their customers want before staff even has a conversation.

Technology

Production-grade stack for global retail

Picked for reliability in physical-retail environments where uptime and offline-resilience matter — and for the flexibility to expand the LLM layer as the AI landscape moves.

Next.js
Frontend
TypeScript
Frontend
LLM APIs
AI
Multi-tenant DB
Backend
Shopify API
Integration
WooCommerce
Integration
i18n (11 langs)
Localization
QR generation
Frontend
PDF generation
Backend
Real-time analytics
Backend
REST APIs
Architecture
Privacy-first arch
Security
Outcomes

A retail AI that store owners actually defend in public

Most AI features in retail get launched, hit a usage spike for two weeks, then quietly fade. Smart Stack Advisor didn't. The reason isn't the AI capability — it's that the product solved a specific retail problem (staff knowledge variance + language barriers + tourist conversion) with technology architected from day one for the retail environment.

Verified retail outcomes. Store owners across three countries publicly attribute a 68% average basket size uplift to customers using Smart Stack plans. That number doesn't come from a vendor pitch — it comes from named store owners in public testimonials on the product's marketing site.

Multi-language opened new revenue. In Phuket and other tourist-heavy retail markets, Russian and Chinese-speaking tourists who couldn't previously transact (because store staff didn't speak their language) now receive complete personalized advice in their own language and buy. This wasn't a "nice to have" — it materially expanded the addressable market for every retail location.

Multi-tenant architecture made scale cheap. Adding a new retail location to the platform doesn't require engineering work — store owners self-serve via the admin panel with branding, catalogue, and QR placement. The architecture decision in week one is what made hundreds-of-stores roll-out economically viable.

Privacy-first improved accuracy. The decision to collect zero PII wasn't just compliance posture — it removed customer hesitation about disclosing real goals and budgets. Customers gave more honest answers, which directly improved recommendation accuracy. A side-effect we didn't predict, but a real one.

Want to build production AI for retail or multi-tenant SaaS?

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