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Luxury AI Fashion Platform2026

Onliest REX

Designing REX — a luxury AI stylist and product-design engine for Onliest that turns vague fashion instincts into personalized, purchase-ready saree & blouse recommendations.

Product Designer
Role
Web App
Platform
Saree & Blouse
Focus
2026
Year
Conversational AIAgentic ArchitecturePersonalizationFashion TechDesign SystemVoice & Avatar UXRAG / GenAIBRD → Product
Tools

Figma · Notion · Claude AI · Jira

Collaborators

Product Owner (me) · CTO · AI/ML Lead · GenAI/RAG Lead · Fashion Curator · Security Lead

Deliverables

Business Requirements Document · Conversational & Wizard UX · Design System · Admin Governance Model

Onliest REX interface
Overview

A Personal AI Stylist, Not a Search Bar

REX was built to make discovering and designing the perfect saree feel effortless, personal, and inspiring — for a platform where trust and craftsmanship matter as much as technology.

Onliest REX is the AI recommendation, personalization, and product-design engine for Onliest.ai — a luxury fashion platform. Rather than behaving like a generic e-commerce recommender, REX is built to act as a personal AI stylist: understanding a customer's body, occasion, taste, and budget well enough to design a saree or blouse with them, not just for them.

My Role

Owned the Business Requirements Document end to end and translated it into the conversational, wizard and try-on experiences shown here.

The Challenge

Turn a multi-agent AI system into something that feels like working with a stylist, not filling out a form.

The Approach

A low-friction conversation paired with a structured wizard, backed by a scored engine a customer can trust and an Admin team can control.

Problem Statement

Fashion Discovery Was Guesswork

Luxury customers deserve a stylist's judgment, not a filter panel — but building an AI stylist that's trustworthy, explainable, and commercially sustainable is a much harder problem.

Premium fashion customers often need guidance beyond simple filtering. They may not know what design suits their body, skin tone, occasion, or budget — and existing platforms respond with endless catalogs instead of understanding. Onliest needed an AI system that could ask the right questions, reason over trust-sensitive data, and still feel like luxury, not automation.

Decision Paralysis

Browsing thousands of sarees with no reliable way to know what suits your body, skin tone, occasion or cultural context. Customers default to endless scrolling.

One-Size-Fits-All Recommenders

Generic e-commerce logic treats a saree, a blouse and jewellery the same way, ignoring that fit, drape and occasion rules differ per product line.

Inspiration Without Translation

Customers save Pinterest boards and wedding photos, but have no way to turn “I like this” into a manufacturable design without a human stylist.

Trust & Hallucination Risk

An AI stylist that invents availability, price or delivery destroys trust instantly — and bridal timing is non-negotiable.

Solution

An Agentic Engine Behind a Human Experience

Every conversational or Wizard interaction is backed by a governed, multi-agent architecture — designed so customers feel a stylist's intuition while the business retains full control.

REX addresses this with a tiered, agentic architecture: a conversational and guided-wizard front end backed by independent per-product-line scoring, tiered LLM routing, and hallucination guardrails — all configurable from a single Admin layer instead of buried in code.

💬

Conversational + Guided Wizard

Free chat, voice or an uploaded image, or a structured 14-step wizard — two entry points into the same engine.

🧩

Product-Line Independent Scoring

Each category carries its own weights, fit rules and penalties, so a blouse's neckline scoring never distorts a saree's drape.

🕸️

Hierarchical Agent Orchestration

An orchestrator routes work to specialist agents through MCP-style tools. No agent reaches the customer directly.

🎚️

Tiered, Cost-Aware LLM Routing

Guests get rule-based results, unpaid users route to Llama, paid members to OpenAI — configurable by Admin, not hardcoded.

🛡️

Hallucination Guardrails

Every product, price, inventory and delivery claim is validated against the catalogue before display.

Key Features

Four Ways In, One Recommendation Engine

Whether a customer converses, steps through a Wizard, uploads inspiration, or simply browses, every path resolves to the same scored, explainable recommendation.

Conversational Mode

Interactive Stylist Chat

A live conversation with REX — via text or voice — where customers describe an occasion ('a saree for a birthday party in Miami around 8pm') and REX responds with a curated design plus a live Match Score.

Structured Mode

Guided Design Wizard

A 14-step guided flow (fabric → border → pallu → blouse → fasteners) for customers who'd rather build their look step-by-step, with progress tracked and a live preview updating alongside every choice.

Image Understanding

Upload Your Inspiration

Customers upload up to five inspiration images — Pinterest boards, wedding photos, celebrity looks — and REX extracts color, motif, fabric, and embroidery signals to recommend a similar, Onliest-suitable (not copied) design.

Fit Confidence

Live Virtual Try-On

Recommendations render on a mannequin scaled to the customer's real proportions, with the Match Score panel breaking down Confidence, Color Harmony, Occasion Match, and Preference Match in one glance.

Discovery Mode

Explore Curated Collections

For customers who want to browse rather than converse, curated designer collections are ranked by REX against the same fit, palette, and occasion profile — with full filtering by material, occasion, price, and color.

Refinement

Design Studio Handoff

Any design — from chat, Wizard, or Explore — can be sent to Design Studio, where borders, drape, palette, and motifs remain fully editable until it's no longer REX's suggestion, but the customer's own design.

Process Artifacts

The working drawings

How a request actually moves through the system, and the problems the BRD was written against.

Fig. 01Request flow — agent orchestrationGuardrail can return a result rather than release it
  1. 01EntryChat, wizard, image or browse
  2. 02OrchestratorRoutes to specialist agents
  3. 03DiscoveryAsks only what is missing
  4. 04ScoringMatch, confidence, penalties
  5. 05GuardrailValidated against real inventory
  6. 06DisplayDesign + explained score
Fig. 02Affinity map — problem synthesis3 clusters behind the brief

Search cannot hold intent

  • A vague instinct has no keyword to type
  • Generic search returns a catalogue, not a recommendation
  • Customers abandon rather than refine

Unexplained results are not trusted

  • A ranked list with no reason reads as arbitrary
  • High-value purchases need a stated rationale
  • Confidence has to be visible, not implied

Invented answers destroy trust

  • Availability, price and delivery must be real
  • Bridal timing is non-negotiable
  • Every claim needs a source to check against
Fig. 03Stylist chat wireframe — low fidelityConversation and recommendation always co-visible
Interactive stylist chat
Mode switch — chat / wizard
Conversation + transcript
Generated design
Match score + why
Input
Refine in Design Studio
Underlying Architecture

Orchestrated Agents, One Trusted Voice

No single agent talks to the customer directly. Every response is assembled by an Orchestrator and validated before it ever reaches the chat, Wizard, or Try-On panel.

UserOrchestratorSpecialist AgentsGuardrailsDesign Studio
Orchestrator Agent
Controls the overall flow and merges specialist agent outputs before anything reaches the customer.
Preference Discovery Agent
Asks only the minimal, adaptive questions needed when data is missing or confidence is low.
Image Understanding Agent
Extracts color, motif, fabric, and embroidery signals from uploaded inspiration images.
Scoring Agent
Calculates the Match Score, penalties, and confidence shown in the Live Virtual Try-On panel.
Hallucination Guardrail Agent
Validates every claim against catalog, inventory, pricing, and delivery data before display.
Design Generation Agent
Produces the design candidates that populate directly into Design Studio for refinement.
High Fidelity Screens

Inside the REX Experience

Five core surfaces of the REX experience — from open conversation to structured design to discovery.

Interactive Stylist Chat

Interactive Stylist Chat

Conversational Mode
Design Rationale

Voice input, live transcript, and a stylist avatar sit alongside the generated design and a real-time Match Score — so the conversation and the recommendation are never more than a glance apart.

Guided Design Wizard

Guided Design Wizard

Structured Mode · Step 14 of 14
Design Rationale

A persistent step tracker and progress bar give customers who prefer control a transparent, undo-able path — while the same Match Score panel keeps both modes consistent.

Upload Your Inspiration

Upload Your Inspiration

Image Understanding
Design Rationale

Drag-and-drop upload with format guidance (JPG/PNG/SVG/TIFF, 25MB max) sits next to REX's own conversational suggestion — reinforcing that upload is one of several ways in, not the only one.

Explore Curated Collections

Explore Curated Collections

Discovery Mode
Design Rationale

Filterable, ranked collections give browsing customers the same scored personalization as the chat and Wizard flows, closing the loop between passive discovery and active design.

REX Landing & Onboarding

REX Landing & Onboarding

Entry Point
Design Rationale

Four clear entry paths — Design Saree, Design Blouse, Just Exploring, Upload Inspiration — replace a single generic search bar, reducing the 'blank page' problem of a first-time AI stylist session.

Scope & Success Metrics

Designed to Be Governed, Not Guessed

As a BRD-driven, pre-launch product, success here is defined by the constraints and metrics built into the design — the guardrails that make the AI stylist trustworthy at scale.

5 → 25
Recommendation batch scaling
Initial batch of 5 designs, 4 more per 'More' click, scaling up to an Admin-configurable maximum by loyalty tier.
8
Scoring parameters per design
Overall Match, Confidence, Color Harmony, Occasion Match, and Preference Match are all surfaced individually, not collapsed into one opaque number.
2
Tiered LLM routing paths
Llama for unpaid registered users, OpenAI for paid and Platinum members — with a future Onliest-owned domain-specific LLM planned for Phase 2.
10
Languages in initial scope
English, Hindi, Telugu, Tamil, Kannada, Malayalam, Marathi, Gujarati, Bengali, and Odiya — configurable through Admin as REX expands.
0
Hardcoded recommendation rules
Every weight, penalty, threshold, and prompt lives in the Admin configuration layer — not the codebase — to keep the business in control of its own AI.
CEO-only
Algorithm access boundary
Proprietary scoring formulas and weights are visible only to the CEO or a CEO-delegated Product Controller, enforced through field-level encryption and RBAC.

Course correction

What I got wrong

First take
I designed the conversational flow first and treated the guided wizard as a fallback for people who did not get on with chat.
What changed it
Walking the flows with the fashion curator made it obvious that a blank conversation is its own blank page. For a high-value purchase, plenty of customers want to be led — the wizard was not a fallback, it was a first-class path.
What I did
Gave both modes the same Match Score panel and the same underlying engine, so neither is the degraded option, and put four explicit entry paths on the landing instead of one prompt.

CostThe scoring panel had to be redesigned to work in two very different layouts rather than one.

Reflection

What This Project Taught Me

Designing an AI stylist for a luxury brand meant every UX decision doubled as a trust decision.

Designing for trust mattered more than designing for cleverness — customers forgive a clarifying question far more readily than a confidently wrong recommendation.

Giving every recommendation a visible, broken-down Match Score turned an opaque AI decision into something a customer (and the business) could actually reason about.

Two entry points — free conversation and a structured Wizard — served very different customer mindsets without forking the underlying recommendation logic.

Writing the BRD alongside the UX meant every screen was traceable back to a business rule, which made trade-offs (like tiered LLM routing) a design decision, not just an engineering one.

Want to work together?

Let's design AI experiences people can actually trust.