Synthesizer - AI Creative Platform for Luxury

SYNTHESIZER - Strategy | Product Design | Pitch Design

Blending Real and Generative Design in Luxury Fashion

The Opportunity

Luxury fashion brands are evolving into entertainment-driven content creators, with LVMH alone spending €9.5 billion on advertising in 2022. Yet, by 2024, only 28% of brands had experimented with AI in creative processes, revealing a gap in knowledge, tools, and adoption.Synthesizer aimed to bridge this gap, but fundamental questions remained:

How do creatives actually want to use AI?

Should AI support ideation, production, or marketing?

Is AI adoption blocked by technical limitations or cultural resistance?

Key Challenges

The initial product vision lacked clarity on target users, differentiation from existing tools, and its role in creative workflows. My role was to refine Synthesizer’s strategy, product positioning, and UX approach, ensuring that it aligned with real industry needs.

These unknowns had to be addressed to define Synthesizer’s role in the luxury fashion ecosystem.

Ambiguous Business Fit – Unclear whether the platform should serve designers, marketers, or production teams.

Overlap with Existing Tools – Needed a unique value proposition beyond standard AI solutions.

Undefined Core Problems – Uncertainty on whether AI should focus on creative exploration, production efficiency, or automation.

Cross-Functional Silos – Design, marketing, and production teams often worked separately, limiting collaboration.

Balancing Creativity & Efficiency – Should AI enhance ideation or automate workflows, and how does this shift across users?

What We Set Out to Create

A luxury-specific AI platform designed to:

Speed up design prototyping and production – AI-enhanced ideation for rapid concept visualisation.

Reduce creative costs while maintaining craftsmanship – Automated tools that align with brand identity.

Unify designers, marketers, and production teams – Enabling co-creation, iteration, and on-demand content generation.

Bridge AI and brand DNA – Training AI models with archival assets to ensure authenticity and storytelling precision.

The goal was to create a seamless AI-powered ecosystem that supports luxury brands in designing, iterating, and marketing at speed—without compromising creative integrity.

Step 1: Market & User Research

To define Synthesizer’s role, we conducted a deep dive into the industry’s pain points.

Audited existing AI tools – Identified gaps in customisation and luxury brand applicability.

Competitor & Market Analysis – Assessed how AI was (or wasn’t) used in luxury fashion.

User Profiles & Pain Points – Mapped Key Personas:
🔹 Creative Directors – Needed fast ideation tools that balance creativity with commercial goals.
🔹 Fashion Designers – Required automation for repetitive tasks and rapid prototyping.
🔹 Merchandisers – Sought AI-driven tools to update best-sellers and predict trends.
🔹 Marketing Teams – Needed scalable, personalised content solutions aligned with brand identity.

Sitemap & User Flow Mapping – Structured the experience to seamlessly integrate AI without disrupting workflows.

Step 2: Ideation & Feature Prioritisation

With research insights, we ran feature prioritisation workshops to define a scalable roadmap:

Short-Term (MVP) – Customisable AI models, archive digitisation, and marketing content generation.

Mid-Term – Market insights integration and real-time collaboration tools.

Long-Term – Symbolic AI-driven feedback loops for performance-based design refinement.

Brand DNA Achieve – Training AI on Digitalised Brand Archives

One of the biggest differentiators was ensuring AI outputs reflected a brand’s DNA. To achieve this, we:

Digitised fashion archives – Using past designs, historical campaigns, and craftsmanship guidelines to train AI.

Created AI-powered retrieval systems – Allowing brands to generate content inspired by their heritage.

Integrated symbolic AI – Enabling AI to evolve based on market feedback and performance analytics.

This ensured that AI wasn’t just generating designs, but creating within a brand’s distinct visual and storytelling framework.

Step 3: Prototype Development

The first prototype showcased mood board-driven creation, allowing Creative Directors and marketing teams to rapidly visualise and iterate designs.

Generative Product Designs – AI-enhanced mood boards that transformed creative intuition into visual concepts.e AI models, archive digitisation, and marketing content generation.

Custom AI Model Training – Collaborated with engineers to train AI on archival brand assets, historical campaigns, and craftsmanship guidelines. insights integration and real-time collaboration tools.

By refining AI’s role from a generic generator to a luxury-specific co-creator, we positioned Synthesizer as a strategic design partner, not just another AI tool.

What We Delivered

One of the biggest differentiators was ensuring AI outputs reflected a brand’s DNA. To achieve this, we:

Strategic Market Positioning – Differentiated Synthesizer from existing generative AI tools.

AI Generated User Personas & Journeys – Mapped key audience needs and workflows with AI tools and real insights.

Prototype Development – Delivered AI-powered product visualisation & content generation workflows.

Brand DNA Achieve POC – AI Model Training – Developed a digitalised archive training system ensuring AI-generated outputs reflected luxury aesthetics.

Investor-Ready Pitch Deck – Defined the platform’s offering, target audience, and competitive advantage for beta client acquisition.

What This Project Taught Us

🔹 AI Strategy & Expansion – Scaling hybrid AI for analytics and recommendations requires a significantly increased budget, data inputs, and testing time for validation.

🔹 User Feedback for Iteration – Limited resources made structured user insights challenging. Quick validation methods like Typeform surveys or 2-week feedback MVP sprints with a pilot clients could have accelerated prototyping and improved iterations.

🔹 Balancing AI Innovation with User-Centric Design – Combining AI-driven innovation with design thinking ensured that data-driven solutions remained user-focused. Collaborative decision-making and iterative refinement were key to developing a 0-1 product that balanced creativity and business impact.

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