Stereotyped is AI sizing intelligence built for fashion brands that are done losing revenue to fit uncertainty. We connect what consumers need with what brands can give — instantly, accurately, at scale.










Seventeen years in public accounting — including deep work with fashion e-commerce brands through Pland Accountancy Studio — gave Courtney something most founders don't have: she saw returns as a P&L problem long before she saw them as a product opportunity.
She also lived the problem. Shopping with a top that fits an 8 and hips that need a 10–12, she understood firsthand how broken online sizing is — generic size charts, no standardisation, and no intelligence connecting what a body needs with what a garment delivers.
In December 2022, reading about Afterpay on a flight, something clicked. Nick Molnar hadn't invented credit — he'd bridged a gap between two parties who couldn't communicate. Courtney saw the exact same dynamic in sizing. Stereotyped is the conduit.
The fashion industry has a sizing problem that goes deeper than vanity metrics. It's structural, expensive, and largely invisible to brands until it shows up as a returns line item that nobody wants to talk about.
Stereotyped was built to fix the three root causes.
Most brands use generic size charts that don't reflect the actual dimensions of individual garments. A size 10 dress and a size 10 jacket can fit entirely differently — and consumers have no way to know that without trying it on.
Grading from a single size 8 fit model — who is often taller than the median size 8 customer — distorts the entire size range. Small brands can't afford fit models across every size, so they guess. Those guesses compound into structural sizing problems across the whole range.
Brands absorb returns as a sunk cost instead of reading them as data. Every return is a customer telling you exactly what went wrong with your fit. Stereotyped turns that signal into intelligence — so brands can fix the problem, not just absorb it.