2026: Google shuts down Doppl and moves virtual try-on into Search and Shopping. The fashion tech industry calls it a graduation. And somewhere in the coverage, a much bigger announcement gets almost completely missed.

The fitting room didn’t just go mainstream today. It changed owners.

Before Selfridges, you described what you wanted to a clerk and bought it sight unseen. Before virtual try-on, you looked at a flat photo and hoped for the best. Both moments were the industry finally admitting it owed the customer something. The difference is that in 1909, the fitting room belonged to the brand. What Google announced today is a different arrangement entirely.

Here’s what’s actually happening and what the celebration is missing.

Google shut down Doppl — its standalone virtual try-on app — and folded the technology directly into Google Search and Google Shopping. As of today, any product listing with try-on capability shows it natively, one tap, no separate download. And yesterday, Google announced that Google Photos is getting an AI wardrobe feature this summer: it will scan your existing photo library, identify every piece of clothing you’ve ever been photographed wearing, and build you a digital closet — complete with mix-and-match outfit tools and virtual try-on of your own clothes (we’ve heard this story before).

Many outlets today ran the Clueless angle. “Finally, Cher’s virtual wardrobe is real!” It’s a good reference, it’s true, and I get it. But here’s the thing about Cher’s closet that everyone is glossing over: it was Cher’s. She owned it. She controlled it. What Google is building is a closet that lives on Google’s servers, feeds Google’s shopping graph, and routes your customer’s next purchase through Google’s infrastructure. That’s not Clueless. That’s something else entirely.

I use fashion tech and cover it, which means I get to watch the same announcements land differently on two groups of people. The tech side is calling this an infrastructure upgrade. The fashion side is mostly not paying attention yet. Both are missing the actual story. Let me break it down.

What is actually happening here?

Google ran an experiment called Doppl. It worked. Well enough that they didn’t need a separate app anymore. So they absorbed it into the product people already use every day to search and shop. That’s it. That’s the whole move. Google does this constantly: test something standalone, prove the concept, collapse it into Search. It happened with Google Maps, Google Shopping, Google Flights. Virtual try-on is now on that list. The technology graduating isn’t the news. The distribution shift is.

Who does this hurt that the coverage is ignoring?

Two groups. First: independent virtual try-on startups. Companies like AIUTA, Genlook, and Catches have raised real money to solve a real problem. Their pitch has always been “we integrate into your brand’s stack.” But Google’s move doesn’t plug into your stack — it replaces it at the discovery layer for most shoppers. The luxury end is still defensible (Catches is building physics-accurate fabric rendering for brands like Amiri, which is genuinely different from what Google offers). But the mass-market try-on opportunity just got a lot harder.

Second group: indie and mid-market brands that aren't plugged into the Google Shopping ecosystem. If your product listing doesn't support try-on, you are now visually competing against listings that do because your product data isn't structured the way Google's infrastructure needs it to be. That gap is real, it's opening right now, and the trade press is not writing about it from the small brand perspective.

Who benefits, and why are they framing it as consumer empowerment?

Google. Obviously. But the Google Photos wardrobe feature is the part of this week’s news being most underestimated. It’s not try-on for shopping — it’s styling intelligence built from what you already own. Years of clothing choices sitting in your photo library, documented incidentally in birthday photos and vacation selfies. Google hasn’t said how that data will be used. But you don’t build a wardrobe graph at Google’s scale purely as a convenience feature. The pattern is consistent: free tool, useful data, monetization follows.

What does this mean for indie brands versus the big ones?

Enterprise brands already inside the Google Shopping ecosystem get conversion lift immediately. Proof point: Macy’s launched Ask Macy’s in March — an AI shopping assistant built on Google Gemini. In testing, customers who used it spent 4.75 times more per visit than those who didn’t. (Macy’s noted those users arrived with higher purchase intent to begin with — so take the multiplier with appropriate skepticism. The directional signal still stands.)

For indie brands the challenge is structural. Google Shopping integration requires clean product data, accurate imagery, and in many cases an active relationship with Google’s commerce infrastructure. Most small brands haven’t prioritized this because they’ve been thinking about it as a marketing channel. It’s not a marketing channel anymore. It’s the fitting room.

What should actually happen now?

If you’re inside a brand: Google Shopping integration is no longer an ecomm team decision. It’s a company infrastructure decision. Your product data quality, your catalog hygiene, your imagery — these are now the first thing your customer interacts with, before they ever reach your website. The creative director, the tech team, and the ecomm lead need to be in the same room talking about this. Most aren’t.

If you’re an indie brand that can’t win at the Google layer right now: your play is the relationship Google structurally cannot own. The post-purchase moment. Fit memory. The customer who comes back because you knew what she needed before she did. Let Google send you the first sale, genuinely, use their infrastructure for discovery. But build something in the relationship that makes the second sale yours regardless of which platform she found you on. The brands that survive this shift won’t be the ones who out-Googled Google. They’ll be the ones who built something a wardrobe algorithm can’t replicate.

A new job title emerges…

One more thing, and this is the part I like to track and don’t skip over: every shift like this one creates new jobs before the industry has names for them.

What Google announced this week means that every serious fashion brand now needs someone who owns how their products appear, render, and get recommended inside AI-native discovery surfaces. Not your SEO manager. Not your paid media team. The person who understands that your product data is your storefront in a world where the AI is making the first impression.

An “AI Visibility Strategist” role seems to be on the horizon — the person who manages brand presence in AI search, agentic checkout, and virtual try-on results.

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The fitting room has always changed hands. From the clerk’s judgment to Selfridge’s curtain. From the physical store to the website to the app. Now to Google.

Each handoff looked like a threat to the brands that had just figured out the last one. Each one turned out to be a new surface to build on.

The industry doesn’t go backward from here. It goes through. And what gets built on the other side of Google’s fitting room is a much bigger opportunity than the one everyone is celebrating today.

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But maybe you’re wondering, why now? This next one may answer exactly that:

Bill Gross spent years studying hundreds of startups and landed on one answer. Not the idea. Not the team. Not the VC money you’re pulling your hair out to fundraise for.

Timing.

It’s a clean thesis and in most industries it holds up. But fashion runs on a different clock, and I’ve been wondering if timing means something more complicated here than it does everywhere else. In most industries, consumer readiness is mostly behavioural: do enough people have smartphones, is the infrastructure there, has the habit formed? In fashion, readiness has an extra layer. You’re not just selling a function, you’re selling into an identity, a set of values about craft, self-expression, and what it means to be of the moment. That means a technology doesn’t just need people to use it, it needs the culture to decide it’s comfortable with what the technology is asking them to accept. That’s a much slower clock, and it doesn’t care about your funding timeline.

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Great idea! Give it a decade.

Being early to a party when no one else has arrived is its own kind of lonely, and in the mid-2010s, that was the reality for virtual try-on. Zeekit launched in 2013 solving for something genuinely broken about online fashion shopping and spent eight years building before Walmart acquired them in 2021. At the time, online sales made up about 15% of women’s clothing purchases, and people were still deeply skeptical about buying clothes without trying them on first. The problem was real. The technology was real. The cultural readiness simply hadn’t arrived yet.

What shifted it, and I hate to bring it up so I’ll whisper it — the pandemic — was less of a gradual warm-up and more of a hard shove. The behaviour that virtual try-on had been waiting on didn’t ease in, it got compressed into about twelve months, and suddenly the consumer appetite the technology had been building toward actually existed. Bill Gross makes a similar point about Airbnb: before the 2008 financial crash, letting strangers sleep in your home was a genuinely strange idea until circumstances made people reconsider what they were willing to try. Timing didn’t just matter, it was the whole game. And when the window finally opened for virtual try-on, Zeekit was already inside Walmart.

The trajectory since then makes it obvious that virtual try-on became a real priority for players you wouldn’t have expected it from, talking to you Nvidia. This week, Google is shutting down Doppl, its standalone virtual try-on experiment, not because it failed but because the technology earned its way into Google Search and Shopping directly. The standalone gets absorbed by the platform that caught the timing right.

Resale tells the same story with a different ending. ThredUp spent years offering free cleanout services just to get people to participate, subsidizing the habit before the habit existed. In a recent BoF piece on resale’s long-awaited financial flourish, that same service now comes with a fee, because when supply is abundant enough you no longer need to earn anyone’s participation. The timing arrived, the leverage shifted, and what started as a customer-first pitch became something else entirely. The unapologetic fees on top of fees revealed something the early days didn’t show: the right timing came, and the industry overshot it.

“I’m in” but are we?

Fashion and AI look like they’re on the same timeline right now, moving fast and in sync, and honestly the coverage makes it easy to believe that. But zoom out a little and the picture gets more interesting. According to Prévoir’s whitepaper referencing The Interline’s 2025 AI report, most of the industry is still sitting in stages one and two of a four-stage AI integration framework. The backend. The stuff that never has to ask a consumer to feel anything about it. Inevitable, sure, but still early.

BoF reported that a UBS analyst found 43 out of 45 companies in his coverage are already using AI, which honestly comes as no surprise. But the more telling detail is how they’re doing it: quietly, deliberately, and very much away from the consumer conversation. American Eagle uses AI for back-office efficiency while its Aerie brand has pledged never to use AI-generated models. Same parent company, two completely different relationships with the same technology depending on who has to actually see it. That’s the operational clock and the cultural clock doing very different things inside the same company.

The moment AI moves from operations into consumer-facing creativity it stops being an efficiency conversation and becomes a cultural one, and that’s where the timing problem shows back up. The industry hasn’t resolved what it’s actually asking consumers to accept, the craft, the authorship, the feeling that someone made a real decision about the thing you’re spending money on and building part of your identity around. The IAB found that 82% of advertising executives believe Gen Z feels positive about AI-generated ads, while only 45% of Gen Z actually do. Brand investments in virtual worlds like Roblox and Fortnite tell the same story one cycle earlier, going from $3 million in 2020 to $441 million in 2024 before dropping nearly 50% the following year when the cultural moment didn’t follow the capital. Which brings it back to Bill Gross. Timing isn’t one thing, it’s two: the operational readiness and the cultural one, and right now the startups being funded into the consumer-facing AI space are building inside the gap between them.

A broken clock is right twice a day

You already know how this goes. A brand drops a campaign, someone in the comments clocks it as AI, and the displeasure arrives right on schedule. The pile-on is practically its own content format now.

The conversations around it have been heated enough that it’s hard to imagine this being the right moment for generative AI in creative work. But the more interesting question isn’t whether now is the right time. It’s whether there will ever be a right time.

The pattern from the past two decades suggests there will be, eventually. Virtual try-on took roughly a decade to go from early startups to mainstream retail integration. Resale needed almost as long to move from subsidized habit-building to financial boom. If that arc holds, the AI creative tools being funded and stumbling right now might just be the Zeekit of this cycle: right idea, wrong moment, and the ones that survive long enough, or rebuild when the window finally opens, will be the ones that look like pioneers even if they’re too early right now.

Caroline Nitsch, creative director and co-founder of Lookbook AI, made a compelling case for this recently. In her experience sitting across from luxury houses contemplating e-commerce in the early days, the fear was that selling online would cheapen the dream and destroy everything they’d built. The line that stayed with me: “Every generation of this industry has looked at the next thing and seen the end of everything they loved. They have never been right.”

So will the timing ever be right for AI in fashion’s creative layer? Or is fashion’s identity so bound up in human authorship that the cultural infrastructure never fully opens for it? There was a time when cameras felt like an existential threat to painters, and then at some point we didn’t just accept photography, we built entire institutions around it, collected it, called it art. Maybe fashion gets there with AI too. Maybe the question isn’t if but what has to happen first, and how long that actually takes, and whether the companies building toward that moment have enough runway to wait it out.

There’s a reason the intro to Time by Pink Floyd is so disorienting. Every clock in the room chiming at once, not in sync but for one brief moment, they align. That’s what the right timing feels like in fashion tech. Not a single clock hitting the right hour. All of them, briefly, striking together.

Bill Gross spent six minutes making the case that timing beats everything, and honestly he’s not wrong. But fashion adds a layer that most industries don’t have to deal with. You’re not watching one clock, you’re watching several: the capital clock, the consumer behaviour clock, the cultural identity clock that decides whether the industry is actually ready to let something new in; and the trend cycle clock, the one that’s moving fastest and waiting for no one. Virtual try-on had to wait for all of them to align. Resale did too, and has recently overshot it. AI in the creative layer is still waiting.

The window only opens when all the clocks strike at the same time. And in fashion, that has historically taken much longer than the funding timeline assumes, if it happens at all.

She assumes it’s the blouse.

So she leans in, pushing inventory and content to match the demand she can see.

But the real signal runs deeper. It’s not just this one piece. White is trending. Satin is trending. The same glossy, fluid feel is showing up across dresses, skirts, and more.

She just can’t see it.

By the time her team pieces together fragmented dashboards and delayed reports, the moment is already slipping.

What looked like a single product win was actually a broader shift in taste, a hidden opportunity missed.

The Data Dilemma

Even when teams have the numbers, answering basic questions about what’s working can be surprisingly difficult. The sheer volume of data makes it hard to identify what actually matters and what to act on.

At the same time, the shopping landscape has become increasingly omnichannel, with shoppers browsing in-store and purchasing online. As behaviour fragments, signals become harder to interpret, especially as high- and low-ticket items are styled and purchased together.

For fashion brands operating at the pace of trends, this gap between data and decision quickly turns into missed opportunities. How much time could your team reclaim if actionable insights arrived exactly when decisions needed to be made?

This is where Prévoir steps in, helping brands act with confidence in moments that matter.

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Meet Prévoir: The Assortment Intelligence for Shopify Brands

In the past, a spike like Clara’s would have meant frantic spreadsheet checks and delayed decisions. With Prévoir integrated directly into her Shopify store, the signal is instantly visible and actionable, so opportunities don’t get missed.

Prévoir translates a brand’s product catalog into structured intelligence. Each item, colour, fabric, silhouette, is connected to live sales, margin, and inventory data, so instead of just seeing what sold, teams understand why it sold.

Patterns that were once buried start to surface more clearly, from which colours drive sell-through to where inventory risk is building, making it easier to act with clarity.

With that visibility, teams can adjust allocations earlier, rebalance stock with confidence, and carry those learnings into future designs.

Prévoir doesn’t replace the merchandiser, it supports them, removing the friction of piecing data together and creating more space for creativity and judgment.

Which of these insights would be most impactful for your team’s day-to-day merchandising decisions?

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A New Framework for Creative Decision-Making

Reading through Prévoir’s whitepaper, what stands out is how grounded it is in the realities of running a fashion brand. It doesn’t just describe a data problem, it captures the day-to-day tension between speed, intuition, and execution that teams operate within. Prévoir is built with this insider knowledge in mind: technology designed by people who know how fashion brands operate, not just abstract data scientists.

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To go deeper, I spoke with , Prévoir’s COO, on how these challenges show up in practice.

The Decision-Speed Problem

Even small delays can mean missed trends or mismanaged inventory. In fashion, how quickly insight becomes actionable matters just as much as the insight itself.

If you could get the most important insights instantly, what part of your workflow would you rethink first?

“I think a lot of the people we talk to are stuck in this kind of analysis paralysis — they have so much data, but no clear idea where to start or what actually matters.

What we’re trying to do with Prévoir is make that easier. Instead of dumping more dashboards on teams, we focus on surfacing the most important weekly insights, paired with recommended actions. Not just ‘here’s what happened,’ but ‘here’s what you could do about it.’

At the end of the day, the decision is still up to the human. We’re not trying to replace that.

Because honestly, data wrangling and cleaning messy datasets is time-consuming. If we can take more of that off people’s plates, they can spend more time on the creative side — building and delivering things their customers actually want.”

AI as a Reinvestment Tool

The opportunity isn’t just automation, it’s how teams reinvest that time into truly understanding their customer and delivering collections they’ll love.

With routine data work automated, what creative or strategic priorities would you tackle first?

“I’d start by going deeper on truly understanding their target customer — what’s actually driving their behavior right now.

• What trends are they leaning into, and why?

• Which product attributes are actually driving sales?

• What marketing campaigns are genuinely resonating?

• Where are the opportunities to cross-sell more effectively?

• Where do they spend time, and how can we show up there in a way that feels native?

If the operational data work is handled, it frees you up to ask better questions like these — and then actually act on them. That’s where the real leverage is.”

Merchandising as a Leverage Point

Fashion tech loves the customer-facing layer. But performance is driven by merchandising—what actually gets in front of the customer.

Which ‘hidden’ signals would you want to spot earlier?

“I think brands would want to spot the combinations earlier, not just single attributes, but how they work together.

Things like color + fabric + silhouette combinations that consistently outperform, or ones that correlate with higher returns.

For example, something like Romantic + Modest might quietly outperform within a category, while another combination might be over-indexing in returns, which is just as important to catch early.

With these kinds of insights, teams can act much faster — pushing the right products, buying into what’s working next season, or identifying where markdowns are needed sooner.

Prévoir surfaces these combinations by category and season, so you can see what’s actually driving performance at an attribute level. It also highlights the attributes behind your A, B, and C products, so you can lean into what’s working and adjust what’s not.”

Balancing Data with Instinct

Some AI projects fail and get framed as proof that data and creativity can’t coexist. Gap’s Product 3.0 is a clear example, items perfectly aligned with past preferences fell flat because they missed future demand. The real tension isn’t data vs. creativity, but how to use data to inform instinct without letting it limit what comes next.

Where in your product planning do you risk following historical data too closely, and what would you try differently?

“I think historical data is still a great starting point. If something has been working, there’s no reason to ignore it.

But where teams get stuck is relying on it too heavily, especially now. Consumer preferences are shifting weekly, not seasonally. Trends are peaking and fading faster, and there’s a lot more external pressure — from macroeconomic changes to cultural moments — influencing what people actually buy.

If you’re only planning based on last season’s data, you risk missing what’s happening right now.

What I’d try differently is using historical data as a foundation, but layering in real-time signals and being more willing to adapt mid-season. That means:

• Paying closer attention to emerging trends, not just proven ones

• Reacting faster to what’s gaining traction (or losing it)

• Leaving room in the assortment for experimentation and newness

• Staying more connected to what’s happening outside your own data

The teams that win are the ones who can balance what’s worked with what’s changing.”

If AI surfaces unexpected opportunities, how do you decide what to act on?

“I’d treat AI-driven opportunities as strong signals, but not automatic decisions.

AI is great at surfacing patterns you might miss, but they still need human judgment. The way to approach it is through small, controlled bets.

Sanity check the insight, test it in a low-risk way, watch how the metrics move, then double down or pull back.

It’s less about blindly trusting AI, and more about using it to expand your field of view — then learning quickly through experimentation.

With Prévoir, you can actually track how these recommendations perform, which makes that loop tighter. You’re not just guessing — you’re testing, measuring, and iterating.”

Why Fashion Teams need Prévoir Now More Than Ever

The pace of fashion has fundamentally changed. Trends rise and fade faster, consumer behaviour shifts more frequently, and the pressure to respond in real time is only increasing.

At the same time, distribution is evolving. With Shopify enabling product discovery through platforms like ChatGPT, structured product data is no longer just operational, it directly impacts visibility and growth.

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The brands that win won’t be the ones with the most data, but the ones that can act on it fastest, while still preserving creative judgment.

Prévoir sits in that balance, helping teams turn complexity into clarity without losing the human side of merchandising.

These reflections are just a starting point. Prévoir’s whitepaper goes deeper into how AI can support creative decision-making while reducing operational friction.

If you’re building or scaling a fashion brand, it’s well worth the read.

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And if you’re already experimenting with bringing more intelligence into your merchandising workflow, we’d love to hear how you’re approaching it.

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It’s the deliberate practice of picking a starting point and following your curiosity as questions arise, with no end goal in mind. And in a way, that’s dying.

Substack people probably feel this more than anyone, but research for the sake of research is actually a fun digital adventure. But we’ve shortened the gap between curiosity and answers through LLMs when the best exploration still lies in ambiguity.

That space helps us ask better questions. It sharpens our internal compass of what piques our curiosity, and what that says about what we care about.

Here’s where I started and where I ended up: from Margiela’s jabot to Hermann Hesse’s writing. But really, it became an accidental exploration of how softness, fragility, and time travel through fashion—across designers, genders, and generations.

With everything hyperlinked, so you can follow your own path.

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It starts with something small: a jabot

I started with Martin Margiela’s first show in 1989, where a recurring 18th-century menswear accessory kept showing up: the jabot.

Except it wasn’t first worn on the female models’ necks. It was wrapped around wrists like a boxer would, forming a kind of hand bra. Then it became a bandeau. Then it returned to the neck.

Like many garments in Margiela’s work, the jabot is worn “wrong” over and over again.

Historically, it’s a symbol of masculine formality. But here, it’s stripped of its original function and reassigned across the body.

And it’s not really about the garment. It’s about what happens when meaning is removed from where it’s supposed to sit.

I started to wonder: Was the jabot contained in his debut show, or was it a marker of Margiela’s work?

Turns out, like the tabi, it was re-editioned across multiple seasons. And in the late 90s, the boxes it came in included illustrated guides showing different ways to wear it.

Those illustrations were created by Sébastien Meunier.

So naturally I questioned, who is that?

Sébastien Meunier: inside the Belgian avant-garde ecosystem

Sébastien Meunier spent a decade as head of menswear at Maison Margiela before moving into the world of Ann Demeulemeester as creative director.

He now runs his own brand, exploring themes of gender fluidity, strength in women, fragility in men, and the body as something expressive rather than fixed.

As I got deeper into his work, it became clear why he was chosen to take over from Ann D.

So what was it about him that made him the right successor?

The same codes, reinterpreted

Meunier taking over didn’t feel like a departure but rather a continuation just expressed differently.

Both designers were interested in similar ideas:

• gender fluidity

• softness in menswear

• the body as expressive

But they arrived there differently.

Ann D’s work felt poetic, moody with some dark romanticism. Black, white, avant-garde tailoring—there’s a rebellious but tender spirit running through it.

Meunier softened that world. Fabrics became more fluid, silhouettes more romantic, the energy less austere and more intimate.

He didn’t change the language. He changed the tone.

Which starts to echo something from earlier: the idea of taking something historically structured, masculine, and fixed… and letting it loosen, shift, and mean something else entirely.

What drew me in was that both approaches point to a different way of expressing masculinity. Not through strength, but through vulnerability.

Which led me to dig deeper into Ann D’s idea of male fragility.

Male fragility, Patti Smith, and something softer

At the center of Ann D’s universe is Patti Smith, her long-time muse and close friend. What’s often noted is that Smith’s androgyny wasn’t styled. It was lived.

Through her, Demeulemeester expressed a version of masculinity that wasn’t rigid, but open, poetic, and unfinished.

In one of her menswear shows, inspired by Hermann Hesse, older men walked the runway with some well into their 80s. Not despite their age, but because of it. She once said, “The older you get, the wiser the clothes would be.”

Hesse’s writing centers on self-discovery, time as non-linear, and the idea that all moments are interconnected.

Which, in a way, brings everything full circle.

What happens when you start removing structure? Not just from clothing, but from gender, from time, from identity itself?

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What curiosity actually builds

There may not be a straight line from a ruffled garment to philosophy, to aging, to identity. It’s not exactly efficient. And if you tried to turn that into an AI prompt, it would probably come out feeling sterile.

But this is how fashion thinking actually works.

Before anything becomes a collection, a trend, or a decision, it exists in this phase of wandering, connecting, reframing.

In a System Magazine interview with John Galliano, a student once asked how he keeps evolving, it came down to something simple: surround yourself with the right people, and get out into the world.

Because that kind of research; the lived, unstructured kind, is what surfaces real insight.

And it inevitably brings me back to The Alchemist. Where nothing is direct, and that’s the point. You follow what pulls you, and meaning only reveals itself after.

The ambiguity is the point.

In this noisy landscape, it’s crucial to spotlight who is genuinely moving the needle. Especially as emerging fashion brands navigate high risk and low attention. Fabra stands out. Backed by early-stage investors who see it as a foundational piece in the emerging creative stack, the platform is making 3D design not only more accessible but also genuinely collaborative and production-ready. Its freemium model ensures anyone can start designing, unlike CLO 3D, which locks users behind a paid membership after a 14-day trial. While Fabra’s first vertical is fashion, they plan to expand across physical products soon.

Yet even with this strong execution, a subtle strategic question comes up: how can a tool signal professional credibility, support creative freedom, and remain approachable for new users? That tension, and how Fabra navigates it, is the heart of this case study.

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Emerging Brands Today: The High-Stakes Landscape

FW26 reporting from Vogue Business and BoF highlights the precarious environment for emerging brands:

• Economic pressures: Rising costs, uneven cash flow, and shrinking wholesale channels make brands cautious about adopting new tools.

• Attention scarcity: Social media spikes, e-commerce volatility, and selective retail partnerships mean visibility doesn’t guarantee adoption; onboarding and narrative clarity are essential.

• Need for focus and discipline: Successful brands slim down collections, emphasize wearable and modular designs, and double down on what works; minimizing risk while maximizing impact.

For these brands, a tool that is legible and effective is non-negotiable. A quick Reddit scrape confirms this: users seek low-risk, industry-grade tools that are inexpensive, require no downloads, and browser-based so they can work across devices. CLO 3D often comes up, but while it delivers professional-grade tools and depth, it can be overwhelming or too complex for new designers just getting started.

Interestingly, posts seeking user feedback on their tech pack creation tools generated meaningful engagement, signaling that community validation matters for adoption. More on this later.

Fabra’s Product & Messaging

Fabra’s core messaging pillars; easy to use, collaborative, production-ready, come to life in three main functions:

1. 3D Design Editor: Browser-based, collaborative, and (in my experience) reminiscent of Figma or Adobe XD. Multiple users can iterate simultaneously, lowering the learning curve.

2. Asset & Material Libraries: Prebuilt base designs, repeat prints, trending color palettes, and Pantone options enable rapid prototyping. Experimental or avant-garde combinations are somewhat constrained, though importing custom designs is on the roadmap. The time Fabra has invested perfecting base products ensures a reliable starting point for creativity.

3. Automated Tech-Pack Creator: Designs can be exported as manufacturer-ready specifications at speed, bridging concept and production.

My hands-on insights reveal how these choices signal positioning:

• Ease of use: No login barriers; demo video emphasizes speed and simplicity.

• Collaborative workflow: Cloud-based iteration reduces friction for teams and stakeholders.

• Production-ready outputs: Tech packs and material libraries reinforce professional credibility.

• Visual identity: Overall UX and social graphics differentiate Fabra from competitors, reinforcing accessibility and creative empowerment.

• Subtle limitations: Flexibility for avant-garde designs and delayed help chat highlight the ongoing balance between accessibility and credibility.

Diving deep into Fabra’s messaging touch points highlighted how much they get right, especially in its story-led social media and educational content. Their demos; from playful reality-TV-style showcases to posts on materials and cultural trends, reinforce trust and show that the product can reliably support creative- and design-driven decisions. Fabra’s choices in product and content consistently signal its positioning.

The Strategic Tension

Fabra’s audience spans professional designers and brands while also attracting prosumers who are just starting out. This duality creates a subtle tension:

• Lean too heavily on accessibility → risk alienating professionals expecting reliability and depth

• Emphasize technical rigor → risk overwhelming newcomers

• Limit creative flexibility → risk discouraging power users, weakening long-term adoption

Even Canva, a tool celebrated for democratizing graphic design, is often criticized by professionals for oversimplifying workflows. Fabra faces a similar balancing act, needing to appeal to both audiences without compromising credibility.

How Fabra Navigates the Tension

• Freemium access: Lowers adoption barriers, allowing emerging brands to test the platform with no financial risk; the key insight from my Reddit research. In a landscape where starting a brand already carries high risk, this incentive is particularly compelling.

• Intuitive interface: Familiar paradigms (Figma/Adobe XD style) make the product immediately usable.

• Collaborative cloud workflows: Multiple stakeholders can iterate in real time, reinforcing professional credibility.

• Output as the hero: Speedy tech-pack creation, and material libraries showcase production-ready results, aligning the product with real-world needs of small brands.

• Narrative and social storytelling: Demo videos, social content, and “reality show” character-building reinforce Fabra’s positioning and highlight how the platform balances the needs of different types of users.

Want to know how emerging brands can trust a platform without sacrificing creative freedom? The full section reveals practical strategies inspired by Fabra’s ecosystem.

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That is not what happened.

The framework in question:

Instead, I ended up overwhelmed, second-guessing my own framework, and staring at piles of clothes covering my bedroom floor. Humbling, considering this was the very system I thought I had so cleverly crafted and confidently put out on the internet.

But after the initial spiral, I realized the process had not failed so much as exposed where my logic was too neat for real life. And that ended up being more interesting than success would have been. Because if getting dressed is this messy, emotional, and non-linear for someone actively trying to systemize it, that says a lot about how fashion tech should think about user stories, guidance, and what support actually looks like in practice.

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The Closet Audit Matrix Didn’t Work the Way I Thought It Would

When I started separating everything into piles, I realized I was still judging each piece in isolation instead of as part of my wardrobe system. I had defined “exciting” and “functional,” but I was still evaluating clothes by their individual qualities, not by how they actually worked with everything else I own.

The questions in my head were simple: Can I wear this comfortably in my daily life? Can it elevate a basic? If a piece checked both boxes, it should have been a win. So tell me why I was still cringing at parts of my “exciting and functional” pile, which was supposed to be the ideal one.

That was the issue. Even when a piece met the criteria on paper, it did not make me feel how I expected. The matrix helped me sort, but it did not fully help me decide.

I also realized I get weirdly emotional about breaking up with clothes. I can rationalize keeping almost anything because I can still see its potential. I really believe most pieces are not bad on their own, a lot of it comes down to styling. And I do not think I am the only one like that.

For builders, that is the bigger takeaway. Users may say they want help organizing or decluttering, but what they are often dealing with is attachment, identity, and possibility. If the experience only solves for logic, it misses the real problem.

The Stages Don’t Have to Happen in Order

I naturally did not follow the stages in the neat order I prescribed, mostly because of real life. I happened to be at the mall running errands, so I ended up doing Stage 3 first: testing assumptions in real life. I went on my no-buy mall trip before organizing my closet because, honestly, I did not need a perfectly organized wardrobe to clock that the turquoise Pleats Please–esque top was something I would want to try.

That part actually worked. I am a huge advocate for sitting on a purchase before making it, which is probably why Locker is the one fashion tech app I use consistently.

Where I do think organizing first matters is in places where a decision has to happen fast, like vintage pop-ups, thrift stores, or markets. Those environments require you to know what you already own and have the imagination to envision it styled in your wardrobe.

In my case, the bigger problem was not discovering new things. It was figuring out how to work with the abundance of clothes I already had. Organizing my closet helped because it gave me visibility into my options. But that first audit was not the breakthrough. It gave me visualization, not clarity. The clarity comes later, after I work with what I have, develop stronger style signals, and then return to my closet with a better sense of what actually belongs in the bigger picture.

That part felt messy, but useful. For builders, the point is that users do not move through style in a clean funnel. The messaging has to support revisiting, rethinking, and changing course. The best experiences will not just organize the process, but guide people through the ambiguity in a way that feels more consultative than corrective. And more broadly, fashion tech cannot assume one ideal path through wardrobe discovery. Either the experience is flexible enough to support different user journeys, or it is intentionally narrow and excellent at serving a very specific one.

The Real Problem Was How I Was Visualizing My Closet

For me, visualization was honestly half the battle. When I started separating things into piles, I realized I always begin with the pieces I grab every day because those are the easiest to judge. But by the time I get to the neglected corners of my closet, I am overstimulated, sweating, and deep into decision fatigue. Suddenly I cannot tell if a pair of pants from 10 years ago still belongs.

That is also why I default to a capsule wardrobe without even meaning to. Visibility is a huge part of how I get dressed. Before I reorganized my closet, I would literally have to pull out all of my pants just to find one pair (yikes), so of course I was wearing the same three on repeat every week.

I am still figuring this out, but I think organizing your closet in a way that matches your natural flow matters more than people admit. If you want to maximize your wardrobe, you need to be able to actually see it.

For builders, the takeaway here is that wardrobe friction is not just a discovery problem. It is often an access problem. Not everything in a closet deserves equal visibility. If the items that support everyday dressing are competing for space with pajamas, intimates, or sentimental pieces, the user loses clarity before they even start. The opportunity is not just to document the wardrobe, but to help choreograph access to it.

My dream closet is obviously a giant walk-in the size of its own bedroom, but since that is not my reality, this is where wardrobe apps can actually help bridge the gap. That is what led me to try Alta.

A Very Brief Alta Review

A few quick thoughts on Alta: closet upload is still the bottleneck, even though this is one of the more sophisticated versions of it I have seen so far. Receipt-based upload was basically useless for me because I do not keep most of those emails, and my camera roll is not a reliable archive either since I only photograph outfits on occasion or when I really like them. Searching from the database helped a bit, but because so much of my wardrobe is thrifted, I still ended up manually uploading most things.

I did appreciate that you can batch upload and clean things up after, but I have still only uploaded maybe 10% of my closet. I also was not really connecting with the outfits it styled for me, which only reinforced my current belief that, at least for now, human taste still beats algorithmic styling. The avatar also was not accurately translating how the clothes actually look and fit on me.

Final Thoughts

The stage I still have not really practiced is writing out my style signals. I noticed a lot while organizing my closet, but honestly, I did not write a single thing down. That is probably the one part of this process I actually need to commit to, because insight is not that useful if it never gets captured.

I also still have to do the harder part: work with what I already own. Styling is where I struggle most. I find it hard to translate individual pieces into outfits that feel intentional, so when I do land on a good one, I end up relying on it for dear life. Having a few predetermined outfit formulas would genuinely change my mornings instead of trying to be creative at 6 a.m. before work.

Since I do not really trust AI styling, the social side of wardrobe apps still interests me more. The problem is that most of them do not feel social-first enough to make collaboration easy. That gap still feels underrated to me, especially for users trying to make better use of the pieces they already own.

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Trying my own process reminded me that getting dressed is not a clean decision-making exercise. If the person who designed the system still loops, resists, and reroutes it, that is not just a personal quirk. It is product insight. The gap between the system I imagined and the one I could actually follow says a lot about where user stories need more nuance, where experiences need more flexibility, and where guidance needs to feel more consultative than prescriptive.

The interesting part is that the best fashion tech may still shape what we buy, even if it never becomes an app on our phone.

In my last post, I wrote about the very real 1–3 app ceiling that limits how many fashion tech apps people actually adopt. I argued that retention should not be measured through daily engagement, but through whether a product shows up at the moments that matter in someone’s style routine.

But there is another response to this constraint. Two startups I’ve been watching closely illustrate it well. We’ll hear from them later in this post.

Even genuinely useful products may never earn a permanent slot in someone’s routine. The value has to move instead. It migrates to where the shopper already is: retailer sites, checkout flows, returns portals, email, loyalty programs, resale marketplaces, and creator storefronts.

This is why B2B2C is emerging as a distribution response to real consumer limits.

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The Signal That Changed the Model

Consumer fashion tech is built for engagement. Enterprise fashion tech is built for workflow and ROI. More companies now sit somewhere in between: the buyer is the brand, but the value is felt by the shopper.

This shift became clearer to me when Tote’s founders, Kathleen and Gordon, announced they were taking Tote off the app store. Tote’s pivot is slightly different from a classic B2B2C model. Rather than serving shoppers directly, the company now sells tools to brands that ultimately shape the consumer experience behind the scenes.

The mission had stayed the same but the distribution shifted. When I asked what triggered the pivot, she pointed to a very specific signal from brands:

“While building our consumer app our business model was always centered around working with brands… we started hearing feedback from brands that they wanted to access and implement our proprietary tools and data infrastructure in their own platforms.”

The signal didn’t come from consumer downloads. It came from brands asking to deploy Tote’s capabilities inside their own environments. In other words, the value wasn’t the app. It was the intelligence behind it.

Once You Notice These Patterns, You See Them Everywhere

Brands Don’t Want Another App Either

Tote’s pivot points to a broader shift: some brands would rather implement capabilities directly inside their own platforms than rely on a separate consumer app.

When a capability lives inside the brand’s environment, it can influence the moments where purchase decisions happen.

When tools move inward like this, the product has to be legible as infrastructure rather than simply another app. The consumer may still feel the benefit, but the technology itself operates inside the brand’s system.

The Story Has Two Audiences

This model can be difficult to communicate because it serves two audiences at once: the shopper experiencing the benefit and the brand paying for the outcome. Many startups end up speaking clearly to one side or vaguely to both.

Stiled approaches the problem by starting with consumer behaviour. Founder Will explained, “We went B2C2B because we believe the real fix starts with the consumer.” When shoppers understand how something will actually fit on them, their behaviour changes. “That behavior change is what reduces the rate of returns.”

But the company doesn’t frame the solution as purely consumer-facing. Retailers still play a role in improving sizing standards and reducing waste across the system. As Will puts it, “Tackling both sides in tandem is what actually moves the needle faster and gives many data points to pinpoint issues.” He admits the challenge is mostly narrative: “It’s not the most obvious business model to explain, but once you tell the story from both sides it clicks.”

Outcomes Are Easy to Promise. Mechanisms Are Harder.

Many fashion tech products lead with outcomes like higher conversion or fewer returns. Those claims only become convincing when the mechanism behind them is clear.

Stiled’s explanation makes that chain visible. The behaviour change leads to fewer sizing mistakes, which then reduces return rates and generates more accurate fit data for brands. The strongest B2B2C companies explain how the outcome actually happens instead of relying on promises alone.

The Product Is Still There. The App Isn’t.

This shift is also changing how some companies define themselves. Instead of positioning the product as something shoppers use directly, they position it as a capability brands can deploy across different parts of their systems.

Tote’s repositioning reflects this. As Kathleen explained to me, the company no longer sees itself as a single consumer platform. “Instead of being one user-facing platform, we’re now a ‘headless’ set of tools that can be leveraged by brands of all sizes.” In practice, that means Tote operates more like an intelligence layer that brands can integrate wherever it makes the most sense within their own platforms.

Where The Model Gets Tricky

Embedded solutions solve one clear problem: they improve the shopper’s experience without asking the shopper to adopt another platform. But that same advantage introduces a new tension: the more embedded the product becomes, the easier it is for the technology itself to disappear.

That invisibility creates two risks.

Risk 1: Commoditization

When a company moves toward a headless or embedded model, it gains distribution but risks looking like just another vendor tool. If the role of the product in the shopping system is not clearly defined, the capability can quickly blend into the broader stack of integrations brands already use.

Risk 2: Explanation difficulty

Since B2B2C companies are always explaining themselves to two audiences at once, when those two stories are not clearly connected, the product can start to sound generic. The value becomes difficult to articulate because the consumer identity and the business identity are presented separately rather than as one system.

This is why so many early-stage fashion tech companies sound interchangeable on their homepage. They lead with integrations, outcomes, or broad category terms before naming the specific moment they actually improve.

Positioning becomes practical here. Not abstract brand strategy, but the ability to clearly say what changes for the shopper, what changes for the brand, and why it matters. The companies that win will not just build useful tools. They will be able to explain their role in the system clearly enough that shoppers feel the benefit and brand teams can justify the buy.

That translation layer has become increasingly interesting to me the more time I spend looking at early-stage fashion tech.

The B2B2C Clarity Test

Will from Stiled sums up the messaging challenge perfectly:

“It’s not the most obvious business model to explain, but once you tell the story from both sides it clicks.”

The companies that make this model legible tend to answer three simple questions.

1. What shopper moment do you improve?
Understanding fit. Discovering relevant products. Gaining confidence before checkout.

2. What brand metric improves because of that moment?
Return rates. Conversion. Merchandising accuracy. Customer satisfaction.

3. What mechanism connects the two?
Better visualization. Behavioural data. Contextual recommendations.

When you map those pieces together, the value becomes easier to explain.

Tote
AI learns shopper context → brands integrate the intelligence into their experience → discovery and conversion improve.

Stiled
Shopper sees the truth about fit → behaviour shifts → returns drop → brands gain fit data and lower return rates.

The more time I spend looking at early-stage fashion tech, the more it becomes clear that building the tool is only half the challenge. Being able to clearly articulate where that tool fits in the system is what makes the model click.

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Tote and Stiled both point to the same reality from different directions. Tote learned that brands do not necessarily want another platform. They want proprietary tools and data infrastructure they can implement inside their own environments.

Stiled learned that a B2B2C model is not obvious until you tell the story from both sides, what the shopper gets and what the brand gets. That is the lesson I keep coming back to: in a world where consumers will not adopt endless new fashion apps, distribution shifts toward integrations. But distribution alone does not create differentiation.

In practice, that means naming the moment they improve, the metric they move, and the mechanism connecting the two.

This is where consumer and enterprise tech intersect. If you want to understand the difference between the two, this is your next read:

The future of fashion tech looks promising right now so understandably, there’s an explosion of new tools. Digital wardrobes. AI stylists. Closet analytics. Resale aggregators. Several of them solve the same problems in slightly different ways. From the outside, this looks like innovation. From the inside, it can start to feel redundant.

There’s a visible gap between experimentation and habit. Between interest and ritual. Most of us are willing to try multiple tools, but very few make it into our daily or even weekly routines. That gap feels less like a marketing failure and more like a signal about how people actually relate to their clothes.

It doesn’t help that there’s growing skepticism around optimizing everything (including your style).

That skepticism feels connected to a bigger cultural shift we’re seeing in 2026: friction-maxxing.

If fashion tech has spent the last few years trying to make fashion frictionless, what happens when culture starts reclaiming friction?

Have you heard? We’re friction-maxxing

This article from The Cut looks into how tech has trained us to treat friction as a bug. There is an app to solve (virtually) every inconvenience. Waiting is inefficient, thinking is tiring, decision-making is inconvenient. We’re being nudged toward smoother and smoother ways of moving through the day.

Friction-maxxing flips that script. It reframes friction as a feature. Not because convenience is evil but because not all inconvenience is actually a problem. Some of it is just… being human.

Even the discourse around “the cost of community is inconvenience” — we’re collectively realizing that not everything should be optimized. Sometimes taking the harder way out of things is the reward.

So what does this have to do with fashion?

Fashion Tech isn’t Spotify

For a small percentage of people, systemizing their style is genuinely appealing. They love data, tracking what they wear, and refining their closet like it’s a living document.

But for most people, clothing is not a hobby. It is a recurring decision tied to mood, context, and self-presentation. It is relatively low in frequency but high in emotional weight. You get dressed once or twice a day. You do not interact with your wardrobe passively in the background.

Which is the reason Fashion Tech isn’t like Spotify.

Music can be consumed ambiently. It integrates seamlessly into daily life without demanding identity-level decision-making each time you press play. Fashion does not tolerate frictionless delivery in the same way, because getting dressed is an act of self-definition.

The 1-3 App Ceiling for Fashion Tech

The behaviour of users engaging with 1-3 fashion apps at a time doesn’t indicate a failure of product design. It may reflect a structural limit because style is not a daily optimization project for the majority of people.

In that context, the 1–3 app ceiling makes sense. People are willing to use tools that help with logistics, discovery, or occasional reflection. Fewer are willing to integrate a system that attempts to mediate their identity on a constant basis.

So what do we do about this tension, this rising friction, and the reality that for most people, these apps are something they experiment with, not something ingrained into their everyday?

The answer may lie in digital rituals instead of daily engagement.

2026, The Year We Go Analog…Sort of

We say we want to go analog but it’s really selective convenience that we want.

The most effective fashion tech products already understand this. They provide infrastructure for taste without fully optimizing it. But many still try to build daily engagement loops, gamify styling, automate decisions.

If style behaves more like ritual than workflow, then designing fashion tech around daily engagement may be misaligned.

Rituals are recurring but contextual. They are tied to moments in life rather than arbitrary streaks. Many people have pre-existing style rituals: reorganizing their closet at the start of a season, planning outfits before a trip, reassessing purchases after a major life change, browsing intentionally on payday, styling for an event.

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From Daily Engagement to Digital Rituals

For Founders…

When a product aligns itself with those moments, it becomes supportive rather than intrusive. Instead of prompting users every morning with automated outfit suggestions, it can offer depth during the moments when people are already reflective. That shift changes more than feature sets. It affects onboarding, retention expectations, and content strategy. Onboarding should clarify the context in which the product is most useful rather than overselling universal, everyday transformation. Retention should be measured not only by streaks, but by whether users reliably return during high-intent moments. Content should align with the rituals you are supporting (seasonal resets, pre-event planning, packing flows) rather than pushing constant micro-engagement.

This also raises questions about segmentation. Retention should be aligned with how people naturally experience style. If someone gets dressed twice a day and loves documenting it? Maybe there’s space for daily use. If someone thinks about their wardrobe once a week? Design around that. The frequency has to match the emotional cadence of the category.

For Users…

Maybe this just means being honest.

Where do I actually want convenience?
Where do I want effort?
Do I want help with logistics or with identity?

For the longest time, I refused to use online platforms to shop secondhand. Making a day out of thrifting, putting on my headphones, digging through racks alone was therapeutic to me. Recently, my priorities shifted. I find relaxation in other parts of my life. So the ease of online secondhand shopping makes sense for me now (I recently made my first Depop purchase.)

The ritual moved elsewhere in my life. The category stayed the same; my relationship to it evolved.

That fluidity is important. Rituals are contextual. They are shaped by season, workload, emotional bandwidth, and identity shifts. If fashion tech aims to endure, its engagement model has to account for that variability.

If you think about it, embracing friction and inconvenience in our lives really has to do with our relationship with time. More on that in your next read:

So in the name of Valentine’s Weekend and my very real love for Substack, this is a love letter to the publications I keep in rotation and genuinely look forward to seeing in my inbox.

Think of these recommendations like those little “open when” jars. Something to meet you where you are on any given day.

This is for the modern-day fashion lover who cares about tech, circularity, business models, psychology, operations, and cultural commentary. The whole ecosystem.

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Technocouture

Not to be biased or anything but… ’s has been my favourite (and comfort publication) for a while.

She explores the intersection of fashion and tech in a way that feels refreshing and grounded. There’s a strong focus on consumer behaviour, psychology, and her own personal experiments. I love her writing voice because it bridges research, introspection, and forward-looking advice without feeling overly academic or overly trend-driven.

The Technocouture’s Take at the end of every piece is genuinely one of my favourite recurring formats on this platform.

Her posts spark ideas for my own writing constantly.

Read when: you’re looking for a fresh perspective on fashion tech that’s easy to read but still thoughtful.

One of my favs:

technocouture

Are We Trying Too Hard to Have Personal Style?

This is not another “how to find your personal style” article…

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5 months ago · 4 likes · Navya Kilaru

Fashion, In Context

’s somehow packs a lot of great info into the perfect post length.

She writes with a multidisciplinary lens across culture, sustainability, and tech. Beyond that, her guidance on startups, careers, and strategic thinking makes her pieces valuable whether you’re deep in the space or just circling it.

What I appreciate most is that she points out gaps in the industry and actually pushes the conversation forward. It moves past commentary and into something more directional.

Read when: you want a quick, smart read with your coffee and insights from someone actively in the trenches of consumer and startup fashion tech.

One of my favs:

Fashion, In Context

Is Sustainable Fashion Falling Out of Style?

The evidence is mounting… sustainable fashion is fading: ultra-fast fashion brands are reclaiming ground, while the once ever-present narrative of sustainability is losing its loudspeaker…

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6 months ago · 1 like · 3 comments · Olivia Terase Baba

Circular Fashion News

’s has been one of my daily go-tos for months. I genuinely check it almost every day.

Her coverage goes beyond fashion tech. The news is curated in a way that surfaces things I wouldn’t normally come across, especially insights into the European space. It’s polished, visual, and her graphics are chef’s kiss.

Somehow whatever I’m wondering about, she covers shortly after. Specifically, her databases and resources are ones I repeatedly come back to.

Read when: you want news you wouldn’t normally see, or updates that actually change the way you think about interacting with clothes more sustainably.

One of my favs:

Circular Fashion News

11 Upcycled fashion brands to watch + 3 companies building the upcycling infra

This post might be too long for email, see the full version in your browser here…

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4 months ago · 23 likes · 2 comments · Tiina Nyman

Thought Coded

Jess’s always leaves me thinking, damn she is so smart, I gotta get like her.

I’ve learned things from her posts that I didn’t even know needed to be learned.

Recently she’s been writing about the operational side of fashion tech, digging into the systems, infrastructure, and backend opportunities that quietly power the industry, which is an angle I don’t see explored often in my usual fashion tech circle.

I also really enjoy her writing voice, especially in her lighter posts where her wit comes through.

Read when: you want to learn something you didn’t even know you wanted to learn and start thinking like someone actually running an optimized fashion business.

One of my favs:

thought coded

How Tech Will Save Fashion

Big Fashion’s Unfair Advantage…

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4 months ago · 7 likes · 2 comments · Jess

Hayley Pascual

’s publication has been answering one very specific question I’ve been thinking about lately: how do fashion tech apps actually retain their users?

She explores this through email marketing and lifecycle strategy. Everything she posts (especially her Substack notes) makes me want to hit restack. Her work makes retention feel tangible and actionable rather than abstract.

As someone who spends a lot of time thinking about consumer fashion tech and user behaviour, her writing connects directly to questions I’m already asking.

Read when: you want takeaways you can experiment with and a clearer understanding of how retention works in practice.

One of my favs:

Hayley Pascual

Fashion Tech Adopted Fast Fashion's Worst Email Habit

Fashion tech startups promise to revolutionize how we shop and dress, yet most are using the exact same tired email strategy as the fast fashion giants they claim to oppose…

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4 months ago · 2 likes · Hayley Pascual

Honourable Mentions:

, because it all starts with fashion psychology.

’s publication, because every single title makes me want to click it immediately.

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If you’ve been curious about fashion tech and aren’t sure where to start, these are the publications that have genuinely shaped how I think. They’ve helped me understand the systems behind the aesthetics, the business behind the creativity, and the human behaviour behind the products.

I’m constantly learning in public, and a big part of that is reading people who are thinking deeply about this space.

And if any of the writers mentioned here ever want to collaborate, I would very enthusiastically say yes. Interviews, co-written pieces, shared research, whatever form it takes. I care about documenting and building within this ecosystem, and collaboration is part of that.

If you’ve been wondering where AI actually belongs in fashion, I recently spoke with a founder building in the space about exactly that. Here’s your next read:

As WWD reported earlier this year, luxury leaders have been clear on one thing: AI is already integrated behind the scenes, but when it comes to design, the real concern isn’t job loss. It’s taste. Used carelessly, AI can flatten taste and push fashion toward sameness. Increasingly, the call is for “quiet tech”; technology that supports creativity without overtaking it.

If AI isn’t meant to decide what’s fashionable, then what should it do?

One answer lies in rethinking how fashion decides what gets made. Instead of relying only on forecasts and predictions, a feedback-driven approach pays attention to real signals before anything gets made. It’s less about guessing demand and more about actually waiting to see it.

That shift is being explored right here in Canada. One example is Couth Studios, a Toronto-based fashion tech brand using AI to narrow possibilities, then handing the final decision back to its community through voting.

I spoke with founder Sajna Massey about her background, what she’s building at Couth, and what it’s been like to do this kind of work in Canada.

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Welcome Sajna…

I’m Sajna Massey, the founder of Couth Studios. My background is a bit non-traditional for fashion: I’m an engineer with an MBA, and I’ve spent my career working across tech, product, and operations before starting Couth.

I built Couth Studios at the intersection of fashion, data, and community. We use AI to analyze runway trends from around the world, then invite our community to vote on what actually gets made. Only the designs people love move forward, which allows us to produce in limited runs and avoid overproduction.

Right now, I’m focused on growing Couth as a fashion tech brand that prioritizes intentional design, thoughtful production, and real-world connection through both our collections and the community experiences we host around them.

You’ve worn a few different hats across engineering, program management, and mentorship as a Monday Girl Mentor. Before we get into Couth, can you tell us a bit about your path and what ultimately pushed you to start a fashion tech company?

My path has never been linear. I started out in engineering and spent years in program management roles across tech, finance, and retail, often sitting at the intersection of strategy, execution, and scale. I’ve always been drawn to building systems figuring out how things work, where they break, and how to make them better.

Couth really came from two realizations colliding. The first was working in retail and seeing how disconnected fashion had become: overproduced, trend-chasing, and optimized for speed rather than intention. The second was realizing that while so many industries had been transformed by data and technology, fashion hadn’t meaningfully changed at its core.

Starting a fashion tech company felt like a way to bring those worlds together. Couth is my attempt to apply a more thoughtful, systems-driven approach to fashion using data to inform design, giving the community a real voice, and building something that feels slower, more intentional, and more human.

When people first hear “AI-powered fashion,” they often come in with a lot of assumptions. How do you usually explain what Couth Studios does to someone outside the tech world?

We use computer vision to study runway shows from around the world and distill those trends into data (things like silhouettes, proportions, colours, and recurring design details). That data then informs our design process through AI, but it’s still guided by human taste, editing, and intention.

Couth invites its community to vote on AI-generated designs before anything is produced, with the goal of reducing waste. What has surprised you most about how people engage with this process?

The voting helps us capture real, low-friction signals about taste at scale. Voting on Instagram mirrors how people already interact with fashion—quick reactions, gut preferences, and “would I wear this or not?” moments.

What matters is less about the depth of each individual vote and more about the aggregate. Over time, those simple signals add up and help us reduce guesswork and avoid producing pieces that don’t resonate.

Pretty cool, right? Check out Couth Studios on Instagram and Tik Tok for updates on their latest collections and events!

From your perspective as a founder, what parts of the Canadian fashion or tech ecosystem have been genuinely supportive, and where have you felt the most friction?

Toronto is an incredible place to start a business. There’s a real culture of showing up for local entrepreneurs here. When we hosted our first event in November, the room was filled with people who were essentially strangers who came out simply because they were curious and wanted to support something being built locally. That kind of turnout is something I don’t take for granted.

On the tech side, though, there’s still a bit of friction. Fashion isn’t always taken seriously as a legitimate tech problem, and tech isn’t always welcomed in fashion. That can make a company like Couth feel like it’s sitting in the middle without a clear home. We don’t fit neatly into a traditional fashion box, but we’re also not a pure software company and that ambiguity can be challenging in an ecosystem that often prefers clearly defined categories.

The fashion tech scene in Canada can feel small or hard to navigate. Based on your experience, what advice would you give to people trying to build careers in this space?

It doesn’t just feel small and hard to navigate…it is small and hard to navigate. There are very few fashion tech companies in Canada, which means there isn’t a clearly defined career path or built-in community to plug into.

A lot of the most meaningful connections I’ve made have actually come through non-traditional channels, especially TikTok. Being open about what we’re building has led to conversations with people across design, engineering, and product who are interested in this intersection but don’t always know where to find each other.

Because of that gap, we’re excited to start hosting a fashion tech–focused event in Toronto to intentionally bring this community together. It’s still early, but the goal is to create the kind of space we wish existed when we were starting out so stay tuned!

To close, which Canadian startups or builders in the fashion or consumer tech space are you currently cheering on?

There are a few founders and startups in Canada I genuinely admire because they’re tackling real problems at the intersection of fashion, technology, and sustainability.

I’m rooting for Cache which is building an influencer-led resale marketplace to help make secondhand fashion easier to buy and sell - something that feels essential to making the industry more circular.

I also love what DOUBL is doing with fit technology. They’re using smartphone scanning and AI to help people find the right size and reduce returns and waste, which is a huge pain point in online apparel.

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What stuck with me…

What stayed with me from this conversation is how much of Couth’s work happens before anything is made. Rather than asking the community to react at the end, their input is baked into the decision itself. AI shows up as a way to narrow options and reduce guesswork, but taste, editing, and judgment stay human. Kind of like what we had initially hoped for with AI optimization. It also made me notice how fashion tech in places like Toronto doesn’t really announce itself, it shows up through founder-led experiments and small events. If you’re curious about this space, those moments are often where the most interesting ideas show up first.

Have a fashion tech startup you’d love to get up close and personal with?Message me and I’ll get the inside scoop from them!

The demand for thoughtful and intentional design is clear, but what makes a piece investment worthy? Find out here:

We have wardrobe apps, AI stylists, resale platforms, rental subscriptions, style quizzes, Pinterest boards, and secondhand search engines. On paper, fashion tech should have made getting dressed and shopping easier than ever.

But I still feel overwhelmed curating an intentional wardrobe. My aspirational self keeps missing my actual, day-to-day self. And pressing “checkout” feels high-stakes because I can already feel the buyer’s remorse sneaking in.

The problem lies in treating fashion tech as a series of one-off solutions for individual purchases, which leads to drop-off in user retention. It’s a lose–lose.

Style isn’t based on single decisions. It operates as a system. Once we start applying system-level thinking to these tools and treating style as a practice, not an end-all, shopping feels more confident, wardrobes get used, and learning your taste becomes easier.

This piece is my attempt to map that out: a repeatable style practice, powered by fashion tech thinking, that I’ll return to whenever my closet feels overwhelming and I’m screaming, “I hate all my clothes!”

I’ll be putting this guide to the test to see if it actually works and iterating from there, so subscribe and share what you’re learning about how you dress and shop.

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If you’re Type A, this framework will feel natural:

Grounding rules

This will always be a process and a becoming. Style isn’t something you arrive at and lock in. It changes as your life does. The goal isn’t perfection — it’s feeling mostly good in what you’re wearing, most of the time.

That’s also why it’s important to search internally before externally. Pinterest isn’t the enemy, but it becomes unhelpful when you borrow without understanding why you’re drawn to something. Asking why gives you ownership over your taste instead of outsourcing it to someone else’s style.

Stage 1: Building awareness (the discerning audit)

This isn’t a spring-cleaning exercise where you decide what to keep or toss based on guilt or impulse.

Instead, sort your closet using a simple matrix: what excites you vs. what doesn’t, and what’s functional vs. what isn’t. In an ideal world, all of your clothes would be functional and in use, and many would also excite you.

Not exciting + not functional

These are pieces you never reach for. Resell, donate, or upcycle them. You can even use a company like Newless that will upcycle them for you. If it’s an objectively cool piece but just not right for your life, consider peer-to-peer rental like Pickle, By Rotation or Rax.

Not exciting + functional

Your everyday essentials. These serve their purpose and feel comfortable, even if they don’t add much to your style. For the most part, keep these foundational pieces Though, you might still choose to upgrade them over time.

Exciting + not functional

The pieces you love that satisfy your aspirational self, but you don’t reach for often. Hold onto them. These are great for rentals or for adding some “razzle dazzle” to otherwise simple outfits.

Exciting + Functional

These are your heroes. Pay attention to them. They tell you more about your style than any quiz ever could.

Go through your wardrobe category by category. If you live in an extreme climate, doing this seasonally can make it feel more manageable. I like to do this twice a year.

Once you’re done, the most important part begins: asking why. Why do certain things work? Why do others consistently fall flat? Patterns here will help you repeat purchasing behaviours that work and avoid ones that don’t.

Pro tip: While you’re already doing this work, it’s worth uploading your pieces to a wardrobe app. I’d use Alta for the virtual try-on feature, Whering for the community styling emphasis and Indyx for the premium styling options. I would refrain from using AI styling at least not until you’ve done Stage 2.

Stage 2: Pattern recognition (developing your style signals)

Your style signals won’t come from trends. It comes from repetition.

This stage is about combining what you’ve learned from your closet with external inputs in a more intentional way. Bring back style quizzes! More specifically ones from fashion tech tools. They tend to be useful when they’re based on your behaviour and interactions rather than aspirational questions. Tools like Beni and Indyx tend to surface more grounded insights.

I like to get creative with this part. When quizzes generate keywords (eg. minimalist), I don’t immediately look for outfits. I look for those words in other contexts. Interior design, architecture, even graphic design can help you understand the visual language you’re drawn to without copying someone else’s look. I’m big on “non-fashion” fashion inspo.

Pinterest still has a place here, but only with discipline. Before you pin something, ask yourself if you’d actually wear it. This isn’t about fashion you admire; it’s about what you’d realistically put on your body. Once you’ve pinned enough, step back and look for patterns. What silhouettes, colours, or details repeat? Write that down.

If you’re data-inclined, this is the moment where you could create a model for catching these patterns. Think data analytics for runway looks.

This becomes your style signal. It’s not a rulebook but a great reference point to pressure-test future decisions against. Ideally, it reflects both who you want to be and the life you actually live.

Stage 3: Testing assumptions in real life

Your style signals will almost always surface ideas you haven’t fully tried yet. This stage exists to test them without committing.

Go to the mall or a curated vintage store (shopper’s choice) with one rule: no buying. Try on pieces that intrigue you or feel slightly out of your comfort zone. Removing the pressure to purchase turns this into play rather than obligation (if you really want it, save it to Locker).

This step matters more than it seems. For a long time, I thought I wanted a dramatic oversized fur coat. When I finally tried one on, it completely swallowed me up. I went in the opposite direction and bought a shorter, slimmer coat…and I regret that purchase.

What I learned was: I don’t hate big coats. I just prefer shorter, shaggier versions that don’t consume me. If I’d given that insight time to simmer, I would’ve made a better decision.

Trying things on gives you data. Time gives you clarity.

Stage 4: Integration (working with what you already own)

Now it’s time to use what you’ve learned.

The most sustainable thing you can do is work with what you already have. Using your style signals as a guide, experiment and be silly with it. Document outfits you like.

If digital styling helps you think, tools like Alta can make this easier without the physical effort of changing outfits.

At this stage, I’d actually step back from AI styling. You’ve collected enough information. You understand your patterns. You’re better equipped than any algorithm at this point.

Community can help here too. If you’re using Whering, this is a great time to lean into shared styling. Indyx offers personal stylist features. Or go fully offline and ask friends to react to mirror selfies (your next PowerPoint night idea). Talking through your choices often sparks ideas you wouldn’t have found alone.

Stage 5: The decision layer (to checkout or not to checkout)

Gaps will start to appear and that’s usually when I start to stress about buying the next right piece. But this time, we’re staying neutral.

Use the photos from your try-ons and the outfits you’ve already styled as references. When it comes time to shop, the goal isn’t to add more emotion — it’s to reduce it.

You’ve already done the emotional and aesthetic work. At this point, removing emotion from the final decision is actually the smartest thing to do. explores this well in her Technocouture post, framing AI as an anti-shopping sidekick rather than a persuasion tool:

technocouture

Can AI Be Your Anti-Shopping Sidekick?

There’s a growing frustration simmering beneath fashion culture right now. Many people have recognized how often their purchases aren’t driven by functional need, but by algorithmic nudges, optimized for conversion. Alongside this frustration is a desire to resist overconsumption and move towards restraint…

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4 months ago · 2 likes · 1 comment · Navya Kilaru

Your style signals exists to answer one question: where does this align with what I know about my style?

Search tools like Beni make secondhand shopping far less overwhelming (I’m a die-hard fan). Platforms like The Mall, SSQRD and Arete Studios help surface smaller brands that align more closely with niche styles. For Canadians, Siista Boutique makes it easier to access those brands without paying duties (because we all know that struggle).

And if the answer isn’t clear, you don’t even have to buy. Rental services like BNTO or Nuuly make personal style discovery possible without commitment.

I also stand by ’s mindset of making space for the next version of yourself. Buy one thing, let go of one thing, thoughtfully and sustainably.

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What changes when you stop treating style as one-off decisions

The more I learn about fashion, the less confident I feel about shopping. The information overload is real, and for a long time I didn’t know how to apply what I was learning.

The panic that sets in when buying something, wondering whether it truly fits into a “curated” wardrobe or whether it was just a momentary feeling, is familiar.

Translating taste into action is complicated and ever-evolving. But when you stop treating style as a series of isolated decisions and start treating it as a system, that anxiety softens.

If you enjoy learning how to use fashion tech in a practical, everyday way, this is your next read:

As I’m exploring ways to transition into fashion tech work, I realized I didn’t actually have a clear answer to a basic question: how are these consumer fashion tech startups making money? So, I’m answering exactly that in this post. But let’s take this investigation a step further and talk about how understanding this actually gives you a leg up if you want to work in this space.

Here’s what I’m trying to get to the bottom of:

• The core metrics you’ll hear everywhere (because what is unit economics??)

• How investors think about consumer fashion tech right now

• The most common revenue models in the space

• Turning this knowledge into action for builders and job seekers

This is going to be one juicy byte…

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1. But first, the glossary

I only recently wrapped my head around unit economics, so I’m starting here. If you already know this, you can skip to the good stuff.

GMV (Gross Merchandise Value): The total value of everything sold through a platform before fees. Investors love big GMV because it shows demand and scale but they always ask about the take rate.

Take Rate: The slice the platform keeps from each transaction. Low take rates are user-friendly but tough to profit from. High ones can scare people off.

CAC (Customer Acquisition Cost): How much it costs to get one new user. In fashion, it can get expensive with the visually competitive nature of ads.

LTV (Lifetime Value): The total revenue one customer brings over time, not just a single purchase.

LTV/CAC: The ratio that really matters. Rough rule of thumb is 3x. Spend $1, make $3 back.

Payback Period: How long it takes to recover what you spent to acquire a user. Under a year is usually the target.

Other helpers: Repeat buys (do they come back?), AOV (do they spend more per order?), and retention (% still using after 90 days).

Set the scene: why this matters now

According to Waveup, consumer tech funding is becoming a lot more disciplined as VCs demand clear paths to revenue and profitability. Fashion tech players who operate more like software than just retail will have more eyes on them.

The key themes investors are looking for are:

• Clear monetization paths: not just vague “we’ll figure it out with ads later”.

• Healthy unit economics: Proof that users are worth more than they cost to acquire. But also ensuring you’re not underinvesting in growth.

• Category trends: Continued interest in resale, circularity, and AI-powered discovery, paired with much tougher questions about profitability

Why does this matter for you? Revenue literacy is a career edge. If you want to work in this space, being able to talk about revenue without flinching can make all the difference.

Deep Dive into Revenue Models

One thing that surprised me is that most consumer fashion tech companies aren’t built on a single revenue stream. They’re storytelling three revenue arcs at once: transactions (GMV‑based), attention (affiliate, media, data), and tools (subscriptions, SaaS, and fintech rails)

3.1 Data Monetization

This is where the “free app” conversation usually points.

Fashion apps collect a lot of behavioural data. What users browse, save, buy, return, and how they interact with styling or creators.

That data turns into money in a few ways:

1. Use data to improve their business: better recommendations, smarter pricing, and smarter inventory decisions lead to more sales and fewer markdowns.

2. Wrap data into B2B products: offer brands dashboards and insights: trend reports, customer segments, what styles are taking off, etc. and charge for access to these tools. The Mall’s website for retailers gives us a glimpse into this.

3. Sell access to data‑driven media: Retail media networks are a big example: using first‑party data to sell better ad targeting and sponsored placements for brands. Here, the “product” is audience attention informed by data, and brands pay for that targeting.

How investors think about it

They like data monetization when:

• The data is unique (you see behaviour others don’t).

• It’s privacy‑compliant and aggregated, not creepy.

• It can be sold or used many times with very low marginal cost.

3.2 Marketplaces and Commissions

These are your Depops and The RealReals (peer-to-peer or consignment marketplaces). This model can partially extend to rental marketplaces like Pickle or Isle of Monday as well.

How the money flows:

• The big number is GMV. The platform takes a cut of each sale, which can be a percentage or a flat fee.

• They often add extra fees: shipping and handling, authentication, payment protection, or premium tools for sellers and brands.

• Some also charge brands for running their official resale programs, or sell services like warehousing and cleaning.

What I didn’t fully appreciate before is how operationally heavy these businesses are. Resale is massive, but logistics eat into margins quickly. That’s why you see platforms experimenting with fees, brand partnerships, and value-added services.

How investors think about it:

Investors seem excited by the market size, but cautious about whether the economics really work long-term. They want to see lots of repeat buyers/sellers, strong take rates, and prove the platform can make money not just from fees but logistic services and brand resale programs.

3.3 Affiliate, creator commerce and AI styling

Think ShopMy or Locker. It’s the “free app” with an affiliate backbone. Instead, they recommend products and send shoppers to other stores to complete the purchase. AI styling is often the “smart layer” that makes the recommendations better.

The model is pretty straightforward:

• Recommend products

• Send users to retailers

• Earn a commission if a purchase happens

As these platforms grow, sponsored placements and premium features usually enter the picture.

How investors think about it:

This model is asset-light and operationally lean, which investors like. However, the risk is dependence on external social platforms and commission rates. Strong differentiation in recommendations that matter and loyal audiences matter a lot here.

3.4 Subscriptions, memberships and freemium

What comes to mind is BNTO, Nuuly, Cake and Mile. This is when users pay on a recurring basis for access, benefits, or premium features.

Another common one:

• In fashion tech, that might be a rental membership, a paid wardrobe app, access to exclusive inventory, or a “Pro” tier with deeper analytics.

• The free tier pulls users in. The paid tier has to deliver enough value to keep them.

How investors think about it:

Recurring revenue is attractive because it’s predictable and makes it feel more like SaaS than pure ecomm. At the same time, churn and expectation of discounts can break the model if people don’t stick around.

3.5 BNPL and embedded fintech

Buy now, pay later and rewards are often layered into fashion apps to increase conversion and order size (eg. The RealReal uses affirm)

The fashion tech company usually earns indirect value:

• Higher average order values

• More completed purchases

• Faster cash flow via payment providers

How investors think about it?

Fashion is one of the biggest categories for BNPL so there’s a lot of transaction volume to tap into. However, investors are looking at if the finance layer actually makes the customer more loyal and valuable.

Unit economics investors actually interrogate:

Unit economics are the simple math behind whether a fashion tech startup can make money after paying to get users. Investors ask: “Does each customer pay back what it costs to find them and then some?”

Fashion apps fight for attention on crowded social feeds, so CAC is high. Investors want proof of strong LTV boosters from repeat behaviour, higher order value, memberships, and community-led stickiness.

Here’s a chart based on the revenue models I described:

I’m still learning all of this, and there are definitely nuances I haven’t touched, especially around AI commerce. Brian Sugar breaks this down really well from an investor perspective and it pushed my thinking further (particularly “The Questions That Kill Every AI Commerce Pitch” at the end.)

AirSugar

I Am Going To Say It Out Loud

“It’s all a fugazi. You know what a fugazi is? It’s fake. It’s not fucking real.” — Mark Hanna, The Wolf of Wall Street (2013…

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6 months ago · 65 likes · 4 comments · Brian Sugar

What this looks like in action: Builders and Job Seekers

For founders and early employees

Think clearly about how transaction, attention, and tool-based revenue fit together. Avoid over-indexing on GMV alone or vague promises about future data monetization.

For candidates who want a leg up

Revenue literacy can be the difference between getting a callback and getting ghosted.

Try to:

• Identify how a product likely makes money

• Form simple hypotheses about LTV, CAC, and revenue experiments

• Ask thoughtful questions about monetization and data use

• And most importantly, only use terms you actually understand

That confidence shows.

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Wow, look at us go! We now understand how these startups make money and how we can use that to actually bring value to them. If you want to dig into the differences between consumer and B2B fashion tech, this is the perfect next read.

Our TikTok saves are overflowing with “5 cool girl brands you should know”; we’ve clicked every “Notify Me” button and subscribed to every brand’s email list, waiting for their next drop.

Yet, when it comes time to shop, we default to the same two or three brands.

What’s up with that?

That’s the tension that became the starting point for a spec beta → App Store launch case study for the fashion tech app, The Mall. The product already tackles it — what I wanted to explore was how their launch could encourage that shift from mental curation to action.

Most frame this as a discovery problem. As if we’re not seeing enough brands or inspiration.

But while working through this case study, it became clear that discovery isn’t what’s broken.

We already know what we like. The issue is follow-through.

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The problem isn’t taste. It’s holding intent

The core user for The Mall isn’t someone without taste. It’s someone with very specific taste and very limited attention.

They’re already doing the work:

• saving brands they want to come back to

• tracking pieces they’re waiting to go on sale

• mentally noting which drops they don’t want to miss

But that intent lives everywhere.

Sales land in cluttered inboxes mixed in with work emails. Brand discovery is scattered across Notes apps, Safari tabs, Pinterest boards, and TikTok saves. Each system holds a piece of what they care about, but none of them connect to the moment of action.

Over time, this fragmentation creates a familiar loop. Even people with great taste default to the same few brands. Not because they want to but because it’s easier than keeping up with everything else.

The effort required to stay “on top of it” outweighs the reward.

What The Mall is actually solving

What drew me to The Mall wasn’t just the feature set; it was the behavioural shift it enables.

Instead of asking users to browse endlessly, The Mall gives them a place to:

• save and track brands intentionally

• get notified when items restock or go on sale

• build a personal “mall” around their taste

In other words, it holds the things you already care about for you.

The insight that shaped the launch strategy was simple:

People already curate fashion mentally — they just don’t have a system that can hold that curation over time.

Once intent has a place to live, discovery stops being passive.

Why this couldn’t be a typical “explainer” launch

A lot of early-stage app launches focus on explanation: feature walkthroughs, tutorial videos, UI demos.

But for The Mall, that approach felt mismatched. The value of the product isn’t something you understand after being taught. It’s something you recognize when you see it reflected back at you.

So instead of asking “How do I explain the app?”
I reframed the problem as “How do I show life with The Mall?”

That reframing shaped the entire launch approach.

Designing the launch around behaviour, not features

Rather than leading with features, the launch strategy focuses on taste expression.

Rather than polished ads, it leans into social-native formats people already trust to understand someone’s point of view.

Rather than abstract discovery, it anchors everything in real user collections.

The App Store launch isn’t positioned as a big announcement. It is treated as a behavioural unlock: a moment where users can externalize the mental mall they are already carrying.

This is where the core format emerged.

From mental curation to social behaviour

The launch centres on a simple, familiar structure inspired by formats like “What’s in my bag.”

In the same way that format reveals someone’s inner world through what they carry, “What’s in My Mall” reveals how someone curates their taste:

• the brands they’re tracking

• the pieces they’re waiting on

• the sales or drops they’ve acted on

Not as a tutorial but as a point of view.

Seeing someone else’s “mall” isn’t about copying their taste. It’s about recognition.
“Oh. That’s exactly how I want to shop too.”

That recognition is what turns passive interest into action.

I’m sharing a few slides from the deck below that visualize this idea at a high level (not every execution, but the thinking behind it.)

How success was defined

This isn’t designed to maximize reach or virality.

The goal was much narrower:

• high-intent installs

• first-session action

• early App Store momentum

Success is measured by whether users who downloaded the app actually did something meaningful (saving items, following brands, creating collections) within their first session.

The launch prioritizes niche fashion and creative communities over broad distribution, on the assumption that early participation would matter more than impressions.

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What this clarified for me

This project reinforced the value of designing launches around behaviour rather than explanation. The strongest moments come from giving people a structure to express what they already know about their taste and letting participation itself signal value.

I’m still thinking through how this applies beyond fashion, especially in products where taste, identity, or intent already exists long before someone downloads an app.

If this sparked a reaction, I’d love to hear how others would approach this. Feel free to comment below or message me here or on LinkedIn!

And it’s always in the least expected way. Like little taps on your shoulder.

But most importantly, this butterfly effect of meaningful things shows up exactly when you need them the most.

For me, fashion tech was that thing.

“Fassion is my Pashion”

As a child, my imagination lived in dress-up. Scarves became mystical thneeds before I even knew The Lorax. I loved wearing clothes wrong (inside out, upside down and all the way around). And a trip to the mall was my version of Disneyland. That eight-year-old version of me knew instinctively that fashion wasn’t just about clothes on a body. It was self. And I knew not to abandon it even when my parents begged me not to wear my neon pink graphic tees that had food-with-faces paired with my floral tights. That was probably the only time I stuck to my personal style.

As a first-gen immigrant raised in Canada, I often felt far removed from being Indian. But shopping with my grandma at Indian boutiques brought that princess dress-up fantasy to life in a way that felt ancestral and real. To this day, I feel the most beautiful in a hand-adorned lehenga.

A fork in the road

In high school, when it came time to decide what I’d do for the rest of my life (a completely reasonable expectation for a 17-year-old, obviously), I was torn between typical art school, graphic design, and fashion.

Fashion was written off almost immediately.

Even though I loved what it did for my expression and sense of self, the industry felt… icky. Gatekeepy. Snooty. I could already picture the rooms I wouldn’t belong in, the feeling of not being stylish enough or knowing enough or fitting the mould. I also already saw how bad it was for people and the planet, and getting involved in that seemed messy.

So fashion became something I thought about for five minutes every morning when I opened my closet doors.

For the record, I chose what I thought would be graphic design and somehow ended up with a degree in commercial print. (still don’t know wtf happened there)

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Orbiting before it landed

Looking back the series of clues and events were all pointing to fashion tech and I noticed it when I needed it the most.

Clue #1: Phia…still need an intern??

In my third year of university, while searching for internships, I came across . They were hiring an intern. I remember thinking, Cool! Shopping but they’re fixing it? I didn’t know a single thing about startups at the time and thought the whole thing felt sketchy, so I didn’t look deeper into it.

Fast forward to now: they raised a $30M Series A.
I am screaming, crying, throwing up.

Clue #2: She walked into a thrift store

By my final year, I fell in love with thrifting and started talking through my styling on TikTok. One day, standing in my kitchen, I told my roommate:

“I kind of wish I did the fashion degree I originally wanted.”

Clue #3: So I started a fashion tech startup…kinda

Around that same time, I had to create a business plan for my capstone project. I knew it had to be fashion as my last hurrah. We landed on customizable 3D-printed smart accessories that functioned as jewelry while connecting you to people and places.

Looking back, it’s kind of insane. I created a fashion tech business plan without knowing that fashion tech was even an industry. I had wandered into wearables and personalization completely by instinct.

That project is still the most important thing I’ve ever made. I could write an entire post about it.

(Also… it was a Shark Tank–style pitch competition. And we won. Yupppp)

Clue #4: Literally the road to Fashion Tech

I was not a podcast person until two years ago. I went on a road trip and listened to . Specifically the episode on The Clueless Closet. If you’re in fashion tech, closet digitization was probably your gateway drug too.

I was gobsmacked. People were actually making this happen!

I immediately searched “closet organizer.” Whering popped up. I spent 12 painful hours digitizing every piece of clothing I owned. And afterward? I felt (surprisingly) energized. Lit up. Like I wanted to help other people do this too.

I told everyone about it: the tool, the purpose, the why.
Which makes me realize… I was basically Miss Woven Bytes before I even knew it.

Clue #5 The breaking point (but really the making point)

And then, fashion tech found me at the worst time in my life.

In the midst of grief, loss (and sprinkle of deep identity confusion), I couldn’t listen to music because it made everything too emotional. So I turned to podcasts. The first thing I searched was “fashion…”

What came up was Fashion & Founders.
What I listened to was Fashion Tech Masterclass with Mary Korlin-Downs

Listening to those episodes on my commute (at a time when getting out of bed felt unbearable) genuinely gave me purpose. It reintroduced curiosity. It showed me a brighter side of such a grim industry and reminded me that fashion could be thoughtful, human, and hopeful.

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Fashion tech did not pass me by

Six months ago, something flipped in my brain. I decided: I want to work in this industry.

Since posting content, I’ve met some of the most brilliant minds talking about my favourite topic. I even got to intern at , a Toronto-based fashion tech startup, where I was trusted early, thrown into real work, and given space to articulate these thoughts. I’m endlessly grateful for that.

Lately, I’ve been spending my days having conversations with people who know me for talking about fashion tech. AND THAT IS SO COOL.

It made me realize something simple and grounding:

I love the people in this industry.

And I never want to stop listening to them.

Looking ahead, I have so much more to learn and so much I want to share. I feel deeply connected to this space, and I know I have something to offer here.

So if you’re reading this and you feel something tugging at you, even just a little… trust it.

And if this post becomes even a tiny seed in someone else’s fashion tech journey. Well…then it’s done exactly what it needed to do.

xoxo,

Miss Woven Bytes

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“Personalization” was one of the fashion tech buzzwords of 2025. And yet, the most powerful personalization tool has existed for years. It just happens to work in brand and resale aggregators’ favour better than anywhere else.

As someone with a shaky foundation in my personal style, I’ve been trying out apps that help bridge the gap between what I envision and what I can actually buy. Pinterest has obviously been a big part of that process. But more actionably, I’ve been experimenting with Beni, The Mall App, and Phia. And one thing has become very clear:

The most effective personalization in fashion isn’t predictive or trend-based. It’s relational. “More like this” works because it lets users teach the product what they mean by taste, especially in multi-brand environments.

It’s not a silver bullet. But it is the most practical starting point. Here’s why.

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The real problem: getting lost in the sauce of brand discovery

With fashion discovery now living largely on Pinterest and social media, shopping has become less about the brand and more about silhouettes, styling, price bands, and occasions.

Can a brand help bridge that taste? Absolutely.
But starting with the brand in a noisy fashion landscape (where a new label seems to pop up every day) often leads me straight into option paralysis and indecisiveness.

Personalization in fashion failed because it asked the wrong questions. It asked: “What brand will you like?” or “What trend should we push?” And in doing so, it limited discovery to drops and campaigns. It was marketing-led, not product-led.

Similarity-based discovery doesn’t even need to ask you a question, because it already answers one:

What about this do you like?

• fit?

• silhouette?

• fabric?

• mood?

• styling context?

The key is that it shows you what you might like instead of asking you to describe it.

A huge challenge for me in fashion discovery is that I can see what I like in my head—but I can’t always explain it in words (probably a skill issue on my part, but I know I’m not alone). I have to see it to know it. This is partly why I’ve struggled with emerging search-based discovery tools like Daydream, where the right result depends on me having the right language.

“More like this” works differently in aggregators

I’ve been using “more like this” since my fast-fashion days. I used to fill entire H&M carts purely by going down similarity rabbit holes. But its use in fashion tech (especially in aggregators) gives it a real edge.

On a single brand site, similarity is limited:

• same brand

• limited SKUs

• one aesthetic system

You’re essentially trapped inside a single point of view.

In aggregators, “similar” opens up an entirely different world: cross-brand, cross-price, cross-category discovery. It mirrors how people actually shop.

Like: “I love these Miu Miu flats, but I need this silhouette for less.”

Similarity-based discovery becomes exponentially more powerful when it’s applied across brands, not within a single catalog. And that’s why fashion tech aggregators are better positioned to solve personalization than individual brands.

But wait…there’s more!

After experimenting with different fashion tech apps, I’ve noticed some meaningful nuances in how similarity-based discovery shows up in practice.

Beni

Beni lets you shop the world of resale, which makes similarity-based discovery especially powerful because of the sheer volume on the secondary market. You can search for similar items within the same brand (great when you discover a label you really like) or (my favourite) across similar styles, which helps surface the same idea at different price points.

I create and share Beni collections for others to shop from, so I’m very particular about how these are curated. And when I tell you I reach a flow state using their similarity tools…I mean it. It’s honestly one of my favourite pastimes.

Phia

I have less hands-on experience with Phia, but I’ve noticed that their similarity searches aim to match as closely as possible to the product you’re viewing, across both the primary and secondary markets. It feels very precision-driven, less exploratory, more exact.

The Mall

Most recently, I’ve been intrigued by The Mall. Like many people, I only have a mental rotation of about five brands I default to. This app helps expand that universe.

What stood out most was how its similarity-based suggestions worked after I added products to my collection. It didn’t just suggest more of the same. It suggested items that could live side by side in my wardrobe.

It felt less like “buy another version of this” and more like “this fits into your world.” And that’s what actually helps me find my style, not just fill up my hangers.

The point is…

What makes “more like this” so effective isn’t even the technology. It’s the mindset behind it. It mirrors the way the creator economy has been shaping taste: not by handing you a catalog, but by showing you one thing that resonates and letting you explore sideways from there. Platforms built around creators and social discovery understand this intuitively. They don’t ask users to articulate their taste upfront; they let taste reveal itself through interaction, curation, and context.

As fashion tech looks toward 2026, progress doesn’t necessarily come from inventing entirely new discovery mechanics. Sometimes it comes from recontextualizing what’s been written off as basic and letting it operate at a scale and richness the fashion industry never had access to before.

When you think of fashion tech, you’re probably picturing all the apps you’ve downloaded that promise to fix your styling problems, manage your wardrobe, or help you thrift like you majored in it. That’s the consumer tech side. But you can’t forget the B2B side: the one powering the systems brands rely on behind the scenes.

Most shoppers don’t think about B2B tools, and that’s fine.
But you’re here because you want to work in the industry Vogue Business and BOF keep spotlighting.

“Fashion tech” is an umbrella term. Under it are two very different kinds of products, companies, and career paths. If you don’t know which one you’re targeting, your resume, projects, and talking points won’t land.

This is the guide I wish I had earlier.

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1. Consumer Fashion Tech (the shopper-facing side)

These are the apps you’ve downloaded and played with:

• wardrobe apps

• outfit planners

• resale apps

• AI stylists

• virtual try-on for fun and fit

These products win when users love using them; when features feel fun, sticky, shareable, and instantly valuable.

What Success Looks Like

• retention

• daily active users

• avatar creation or try-on sessions

• shareability

• virality and content loops

• downstream purchases

Product Priorities

• playful, polished UX

• AI styling and try-on

• social mechanics

• community participation

• fast onboarding

• dopamine-forward delight

If you work here, you’re thinking about:

• What makes someone try something on?

• What makes them come back tomorrow?

• What makes them share it?

If you understand people, culture, and aesthetics at an instinctual level, consumer tech will feel like home.

Roles here feel like: growth, content, community, creator partnerships, consumer product, brand marketing, UX optimization.

2. B2B Fashion Tech (the behind-the-brand side)

These tools don’t show up in the App Store. They’re used inside companies to design, plan, source, and run the backstage engine of fashion.

Think:

• PLM platforms

• 3D design tools

• sourcing & merchandising systems

• virtual try-on engines that plug into PDPs

• AI for forecasting, planning, workflows, and content creation

• tech-enabled sustainable material innovations

These products win by improving efficiency, reducing returns, making teams faster, and enabling more sustainable production.

What Success Looks Like

• fewer returns

• better conversion

• fewer sample rounds

• faster workflows

• higher adoption

• measurable ROI

Product Priorities

• integrations (ERP ↔ PLM ↔ e-comm)

• consistent rendering and sizing

• dashboards and analytics

• multi-channel reliability

• security and scale

• workflow mapping

If you work here, you’re thinking about:

• How do I help a brand integrate this?

• Does this improve conversion or cut errors?

• What do multiple teams need from this tool?

Roles here feel like: product, ops, implementation, customer success, data, enterprise partnerships.

3. Doji vs. Aiuta: A Clear Example of the Split

Both use “virtual try-on,” but they work differently.
This is the difference that actually matters when applying for jobs.

Who the product is for

Doji → Consumer:
A playful, aesthetic-led app where shoppers:

• build AI avatars

• try on outfits from anywhere

• explore, post, experiment

It feels like Pinterest meets a virtual mirror.

Aiuta → B2B:
A white-label try-on layer that brands integrate into their website, app, or store screens.
The shopper interacts with it, but the customer is the retailer. Though, it is worth noting, Aiuta does have their own app users can download.

Who pays

Doji:
Monetizes consumers (eventually): affiliates, brand deals, in-app purchases.
The bet: if they own the shopper’s try-on habit → brands will follow.

Aiuta:
Monetizes brands directly: SDK/API fees, usage pricing, enterprise onboarding.
The shopper isn’t paying for Aiuta. The retailer pays for higher conversion and fewer returns.

Product & UX focus

Doji:
Optimizes for delight, shareability, avatar realism, mobile-first smoothness, virality.

Aiuta:
Optimizes for speed, sizing accuracy, integration reliability, analytics, omni-channel support.

What this means if you work there

Doji-type job:
You speak the language of trends, culture, community, aesthetics, and user behaviour.
You care about what makes something fun, clickable, and addictive.

Aiuta-type job:
You speak the language of KPIs, conversion, returns, pilots, and enterprise partnerships.
You care about adoption, integration, and measurable impact.

4. The “Which Side Am I?” Skill Guide

Here’s the part that helps you tailor your applications.

If you’re leaning Consumer

Highlight skills like:

• user behaviour & basic growth principles

• UX instincts

• light product analytics

• community & user feedback synthesis

• product thinking

• fashion/cultural awareness

Resume examples:

• “Grew TikTok-driven traffic by X%”

• “Launched UGC campaign with Y posts”

• “Improved signup flow completion by Z%”

If you’re leaning B2B

Highlight skills like:

• PLM/3D familiarity

• Excel, dashboards, light SQL

• workflow or operations projects

• stakeholder communication

• e-commerce metrics (conversion, returns)

• understanding the product lifecycle

Resume examples:

• “Reduced reporting time by X hours/month”

• “Supported onboarding of Y retail clients”

• “Mapped cross-team workflow that eliminated Z bottlenecks”

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A Twist!

After telling you how different these sides are, what if i told you…there is overlap.

Take Fabra — the “Canva for product design.”
Its power users are designers and brands, but it’s still friendly enough for any creator or student to experiment with. It sits right in the middle.

Point is: you don’t have to pick your forever lane.
But choosing one for right now makes your job search feel more intentional…and honestly, so much less overwhelming.

Grab a Byte is a series of casual reads on my fashion thoughts and personal explorations. It’s the less polished, less LinkedIn-y side of me — more like my Carrie Bradshaw monologue. You’re basically snooping through my Notes app, so grab a byte and enjoy.

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Fresh Off The Runway…My Thoughts While The Energy was Still High:

1. Everything is a system, even when it looks like chaos
   From the inside, a fashion week runs like a living body: dozens of teams moving in their own directions, somehow breathing in sync. It’s messy, rhythmic, almost musical. Like watching a full-on symphony.

2. The unglamorous holds up the glamorous
   There’s something humbling about realizing the magic rests on small, overlooked tasks. I didn’t fully understand until teardown. Me, unscrewing the red carpet backdrop on the floor, whispering to myself, this was behind ALL that?!

3. The human design of a runway show
   The clothes were only amplified by the human-centred design of the runway room. From the timing, pacing, lighting, sound, the tension + release of a perfect walk…it all evokes an emotional response that transcends the clothes.

4. Agile IRL
   As I study Agile Project Management, which was originally developed for software development projects, I realized it fits event production perfectly. Between shows, everything becomes a sprint. Quick changes, faster decisions, the kind of communication that’s half-sentences and intuition. When the runway layout changes seconds before go-time, no one panics…we just shift, breathe, keep moving.

5. Learning to read a runway and the audience
   Being on the runway team meant I watched every show, inches from the action. It felt like collecting data: what made the room lean forward, what designers chose to emphasize, what patterns threaded their collections together.

6. The investment into Toronto’s fashion community
   From backstage, it’s obvious: Toronto’s fashion scene is held up by people who care deeply. The pride they have for their city, the designers, the craft, each other—it’s loud and clear. It’s tender and chaotic and hopeful. And it made me believe the city is closer to the global stage than it thinks.

I haven’t felt this energized and in love with creative work in so long.

xoxo,

Miss Woven Bytes

Clothes have always carried our feelings, but recently they’ve started carrying value, too. Fashion tech is finding sophisticated ways to convert wardrobes into assets. From apps that help you monetize what’s already hanging in your closet to tools that predict the resale value of what you’re about to buy.

Cache turns idle clothes into cash, transforming closets into an income stream. Third is betting on Birkins and Chanel classic flaps, treating pre-loved handbags like stocks. Croissant guarantees a resale value when you buy, meaning we’re now shopping while looking into our crystal ball for the future— calculating not just what something costs, but what it could be worth.

As says best, “Trade clothes, not stocks.” And they’re not wrong. The way we shop has become strategic. It’s less about will I wear this for years? and more about what will this mean for me in a few? For some, that means buying timeless pieces and wearing them into softness. For others, it means not wearing them at all. Treating clothes like long-term holdings (like literally…you can now invest in hedge funds dedicated to the Birkin) untouched, waiting to appreciate.

The “Investment Piece” Problem

Everyone’s talking about “investment pieces.” It’s one of those phrases that sounds self-explanatory until you try to define it. What makes a piece investment-worthy? Longevity? Craftsmanship? Resale potential? Brand status?

The deeper you look, the less clear it gets. So I pulled at a few threads to see what unravels.

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Thread #1: It Starts with Process, Not Product

My first assumption was that “investment piece” was just another word for “high quality.” But as Bliss Foster points out in his dissection of the meaning of quality in high fashion, high quality isn’t as simple as GSM, long-staple cotton, or selvedge denim. Those metrics are often more marketing than substance.

He also challenges the belief that luxury automatically means durability. Longevity, he notes, has as much to do with care as it does with construction. Fast fashion may mimic the aesthetics of luxury at lightning speed, but true quality, he says, can’t be replicated or rushed. It’s the thing luxury used to stand for before it became about the celebrity and limited drops.

As Bliss puts it, quality isn’t a checklist. It’s the care of the maker: their attention, knowledge, and commitment. I’d even add that care is the product of time. Time spent thoughtfully, ethically, and with intention. You can feel it when it’s there. Quality, ultimately, is a “when you know, you know” kind of thing.

Thread #2: Quality as a Choice

Another voice that stuck with me was from TikTok creator and fellow Substacker, . Her take echoed Bliss in a more personal way: that quality is subjective. It’s about what you want out of your clothing.

Even the natural-versus-synthetic debate isn’t black and white. We tend to assume natural is better, but sometimes synthetics are more durable or more ethically produced. The same goes for weight — heavier doesn’t always mean better.

Durability, then, becomes collaborative: shared between maker and wearer.

She also points out that quality extends to ownership. How you wash, store, and treat your clothes matters just as much as how they’re made. Durability, then, becomes collaborative: shared between maker and wearer. Care becomes a design choice, one you make every time you decide how to handle what you own.

Thread #3: The Reddit Reality Check

Then there’s Reddit, where the takes are raw and convoluted. In r/SustainableFashion, many argue that the “investment piece” is just a marketing ploy to justify mid-tier luxury prices. Unless you’re talking about collector’s items — Birkins, Burberry trenches, Manolos — most clothes won’t appreciate. They depreciate. The truth is, those heritage pieces weren’t built as investments; they became them through decades of storytelling.

Other users frame “investment pieces” as classics and staples — the trench coats, cashmere sweaters, white shirts of the world. Which raises a question I can’t shake: can only the boring and basic pieces be worth investing in? I guess basic is what you make of it based on your personal style — but that’s opening a whole other can of worms.

The one idea I found worth holding onto, though, came from a smaller comment thread: a piece is worth buying if it can be mended over time. That supports the mindset of careful ownership, the idea that value lies in your willingness to maintain it — not just own it. That, to me, feels closest to the truth of what “investment” means.

So What Makes It Investment-Worthy

After all that unraveling, I’ve come to think of “investment” less as a financial term and more as an emotional one — a reflection of care, time, and intention. A few truths seem to hold:

• Care is the foundation. The care of the maker, and the care of the owner. Both take time, and time is the most expensive part of quality.

• Quality is subjective. It’s defined by what you need from the garment: longevity, comfort, utility, memory.

• A piece worth mending is worth buying. The willingness to repair something is proof that it holds value to you.

Quality isn’t something you can measure by weight or weave. It’s not a static trait; it’s a relationship. The meaning you assign to the piece is part of what makes it last.

Maybe the real investment isn’t in the clothes themselves, but in the care. The time, attention, and thought we choose to give them.

There’s so much to learn, so much that there isn’t enough time to take it all in. Every day I find myself opening doors to entire worlds in fashion I never even knew existed.

Every brand name, every designer leads me into a lore so deep I feel like Alice falling into an eternal rabbit hole.

All these fashion brands I come across, and that’s maybe 0.00001% of what’s out there. The techniques of garment-making, the reasoning behind business moves from luxury giants, the next wave of trend forecasting, add AI into the mix and it feels like I’m always missing something, even though this is all I think about in my spare time.

Grab a Byte is a series of casual reads on my fashion thoughts and personal explorations. It’s the less polished, less LinkedIn-y side of me — more like my Carrie Bradshaw monologue. You’re basically snooping through my Notes app, so grab a byte and enjoy.

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Doing everything other than hitting the checkout button

I’ll scroll endlessly on The RealReal.

I’ll spend hours walking through little curated boutiques on Queen Street West and Ossington.

I’ll touch the fabrics, bring them up close to my face (as if I know a thing or two about stitching), read the labels, look up the brand, examine each piece like I’m an artisan in an atelier.

And then? I put it back on the rack, walk out, and say “thank you, have a great day” to make up for leaving empty-handed.

And I’ll stop you right there, this isn’t one of those “I’ve seen too much about fashion’s impact on the planet so I can’t buy clothes” rants. Quite the opposite, actually.

You guessed it…I don’t have time

As you can probably guess from the opening, I simply don’t have time to buy clothes.

Fashion isn’t my full-time job yet (manifestation, do your thing). The time I spend with it is purely on the side, and there’s just so much to learn and analyze—tech, innovation, business, supply chain, craftsmanship, retail—that buying clothes for myself always falls to the bottom of the list.

It feels like a chore.

And to buy clothes means I also have to understand what I want, which leads to my next point.

It just means too much

I can’t bring myself to buy clothes because knowing what I know now, it feels too heavy. There’s too much emotion, and honestly, magic attached to it.

Each purchase carries the weight of so much history. To make a sustainable, high-quality purchase that lasts for years, aligns with my personal style, and fits my budget is a big ask. Suddenly, buying a new jacket feels as important as buying my first house. It’s too much pressure.

Yes, there are fashion tech apps designed to solve these exact issues, but so far none have really done it in the way I want them to… for my needs at least (that’s a deep dive for another time).

The shopaholic era

Once upon a time, I was a shopaholic.

It started in my fourth year of uni when I decided to see what the hype around thrifting was about. I got addicted fast.

The low prices and unique pieces that fed my thrift blindness didn’t last long in my closet. But the thrill of the hunt and the joy of experimentation is something I deeply miss.

Still, it doesn’t call to me as much as this knowledge archive I’m building in my mind these days.

Trading in learning for shopping isn’t that freeing

I’ve replaced the impulse to shop with the impulse to learn about fashion.

Though I’d argue that’s not the healthiest trade-off (I should probably broaden my horizons, but enough self-critiquing, I digress).

I don’t feel relief from wanting less. I still yearn for that aspirational version of myself, the one with a curated closet filled with niche brands and pieces that carry rich stories before and after me.

A wardrobe that reflects the depth of my knowledge in this space.

Maybe I’ll get there slowly but surely. Maybe this isn’t disconnection at all, but a commitment to building something with intention. Even if that means buying one piece a year, it might be worth it.

I’ll keep you posted in, say, 50 years.

xoxo,

Miss Woven Bytes

They’re shopping it secondhand. And that shift has pushed luxury into a quiet identity crisis.

Luxury’s embrace of resale isn’t a sustainability awakening, it’s a relevance strategy.

The Paradox Luxury Didn’t Plan For

For decades, luxury sold newness as status. But for Gen Z, status now lives in selectivity.

The rare vintage Fendi find. The Loewe archive piece. The Coach bag revived through repair.

Luxury brands are realizing that the resale market has become their most valuable cultural channel, and they’re not the ones controlling it.

That’s why Gucci, Calvin Klein, and Coach now offer repair, resale, and deadstock capsules. They’re not doing this to “go green.” They’re doing it to stay in the conversation.

Because when Gen Z buys secondhand Gucci on The RealReal, Gucci doesn’t see that revenue but it still defines their brand relevance.

Sustainability by Necessity

Luxury’s new circularity is what I call “sneaky sustainability.” Not loud eco-marketing, but quiet structural change.

Luxury’s pivot isn’t about “saving the planet.” It’s about saving mindshare. Circularity has become the new form of brand insurance.

The Psychology of Quiet Credibility

In signaling theory, consumers use purchases to communicate identity.

For Gen Z, the signal isn’t the new runway piece, it’s the smart buy. The rare resale find that proves discernment, sustainability fluency, and taste.

That’s why brands shouting about sustainability often lose credibility. The modern flex is subtle.

Sneaky sustainability lives in:

• Resale partnerships

• Circular design using deadstock or upcycling

• Take-back and repair programs (recently Re-Calvin)

• Regenerative material sourcing

Gen Z doesn’t need luxury to talk about sustainability. They expect it to function sustainably.

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Shop ZERO: Where Sustainability Is the Baseline, Not the Branding

Shop ZERO is a perfect case study. It curates brands that have cleared the highest bar of sustainability but never markets itself as “eco.”

Its shoppers come for uniqueness, not virtue. Sustainability is the entry requirement, not the full storyline.

By centring discovery over guilt, ZERO mirrors the mindset reshaping fashion:

• Sustainability as assumed infrastructure

• Transparency over tokenism

• Individuality as the new luxury

This is what authenticity looks like when you strip away the marketing.

The Takeaway

All of this brings me back to my Sustainability in Print class in undergrad, where I first learned that sustainability has to be baked into the process, not tacked on at the end.

The same truth applies to fashion: you can’t retrofit responsibility.

Gen Z can see right through “eco” capsules and limited green drops.

What they’re buying into are brands where sustainability isn’t a marketing story…it’s simply how things are made.

What I learned then still holds true now: when sustainability isn’t integrated from the beginning, it always feels like a fix, not a philosophy.

Luxury’s next chapter depends on learning that lesson. For real this time.