About 30% of fashion goes straight to landfill, incineration or the bin. One in three garments made every year is never sold. And over half of all returns come down to one simple issue: fit.
That’s where our latest conversation with Nathan Bush on the Add To Cart podcast starts – with a brutally honest look at why the fashion industry is, simply put, broken… and why we think the fastest path to fixing it runs through code, not clothes.
“Nine years after launching Citizen Wolf to unf*ck the fashion industry, I realised the revolution wouldn’t come from clothing. It would come from code.”
In the episode, Zoltan Csaki (co-founder of Citizen Wolf and Magic Fit) talks about what we’ve learnt running a fully on-demand fashion brand – and why we spun out the tech to help other apparel brands lift conversion and cut waste at the same time.
From brand to tech partner
Citizen Wolf started nine years ago with a simple but uncomfortable truth: the world didn’t need another fashion brand. If we were going to create one, it had to be a blueprint for how the industry should work.
That meant:
- Custom-Made-To-Order garments
- On-demand manufacturing
- A factory in Sydney instead of offshore mass production
All of that is still true. But along the way, one thing became crystal clear: no matter how good your ethics or your supply chain, you can’t change the industry at scale if you only ever sell your own label.
The real leverage was hiding in the tech we’d built to make Citizen Wolf work – the algorithms, pattern software and factory tools that quietly turned three or four data points into perfectly fitting clothes, thousands of times over.
Magic Fit is that technology, unbundled from the Citizen Wolf brand and made available to apparel retailers everywhere.
Your digital twin made easy
Most fit tools either:
- Ask for a ridiculous amount of data (photos, body scans, tape-measure sessions), or
- Ask you to pick which cartoon belly or bum looks “most like yours” 🤷♂️
We took a different path.
On any product page where Magic Fit is installed, the customer simply enters:
- Height
- Weight
- Age
- Bra size (for women)
From just those three or four inputs, Magic Fit builds a statistical model of the body that’s ~96% accurate at the population level – and then lets the customer refine it with a plain-text note:
- “Long torso, short legs, big arms.”
- “Broad swimmer’s shoulders.”
- “Petite with longer arms.”
After almost 100,000 Australian bodies through the system via Citizen Wolf, we’ve seen every shape you can imagine – and we’ve learnt how to translate that into fit that actually feels right in the real world.
Why no AR scans, 3D mirrors or semi-naked twirls in front of your phone? Because most people don’t want their near-naked body data floating around the internet – and they definitely don’t want to break their shopping flow to do it.
Magic Fit keeps the UX simple, fast and respectful, while still delivering fit recommendations that work.
Fit as a strategic growth lever
Most sizing tools are sold as “returns reduction” solutions. And yes, Magic Fit does reduce returns – typically by 15–20%+ once it’s properly embedded.
But the really interesting impact shows up earlier in the funnel:
- Conversion goes up because customers feel size confident at the moment of truth.
- Average order value goes up because confident shoppers are willing to add the extra colour, the matching pant, the jacket.
- Customer satisfaction goes up because they get what they hoped for the first time, instead of enduring the pain of returns.
For brands, that turns fit from a cost problem into a strategic differentiator. In a world where marketplaces, apps and soon GPT-powered interfaces will all compete for the same sale, your owned site needs a reason to exist.
Helping customers buy the right size, in the right style, the first time is a powerful reason.
And as Nathan called out on the pod, the sustainability story is not some fluffy add-on: if you’re cutting returns and improving sell-through, you’re also cutting waste. ESG teams, boards and investors care about that as much as the e-commerce team cares about AOV and conversion.
The invisible data brands have never had before
Today, Magic Fit lives in the pre-purchase experience. But under the hood it’s quietly capturing signals most brands have never been able to see, such as:
- How many customers tried to buy but couldn’t because their size was out of stock.
- Where demand exists outside your current size range – for example, how many people were “just too big” or needed petite/tall adjustments.
- How customers actually want a given style to fit versus how the product team designed it.
That means we can produce reports like:
- “This month, 700 customers wanted this style in a size you don’t offer.”
- “10,000 customers tried to buy this jacket but were outside your size breaks.”
- “X% of people who used Magic Fit for this core style had a body shape we’d classify as petite.”
It’s not full made-to-order, but it is a meaningful step towards smarter inventory decisions, better size curves and fewer garments made that never had a chance.
Listen to the full episode
If you’re an apparel or fashion brand and any of this hits a nerve – returns, size anxiety, wasted stock or just wanting a stronger point of difference on your site – this episode is worth a listen.
Fixing fit won’t unf*ck the entire fashion industry on its own. But it’s one of the fastest, most commercially compelling places to start.
👉 Listen to the full conversation on Add To Cart with Nathan Bush and Zoltan Csaki