Her size chart says 34C.
It doesn't say whether it will fit.
Dopplr is a fit-intelligence layer that intimate-apparel retailers embed on the product page. It reads how each style is constructed and each shopper's own measurements, then shows her how a style is likely to fit and sit on her, building the confidence to buy.
Bras are returned more than almost anything else online. Size charts are why.
A shopper can know her size and still not know whether a specific style will fit her. Cup volume changes with band. Two styles labeled 34D fit differently depending on wire, molding, coverage and shape. She can't tell any of this from a product page, so she guesses: she orders two sizes, or she doesn't order at all.
Both outcomes are expensive. Bracketing inflates gross sales and destroys net margin. Hesitation shows up as an abandoned cart you never see.
Why virtual try-on doesn't work on bras.
Most virtual try-on tools render a garment over an image of a body. That works when the question is how does this look. On a bra the question is how does this fit, and fit is structural.
Cup volume isn't a fixed value.
A 34D and a 32DD hold nearly the same volume. Any system that treats the letter as the size is already wrong.
Two identical labels fit differently.
Molded versus cut-and-sew, wired versus wire-free, full coverage versus demi: same size, different outcome on the same body.
Shape changes the answer.
Two shoppers at the same measurements, one round and full, one bell-shaped, need different advice about the same style.
None of this shows up in an image overlay, because none of it is a rendering problem. It's a garment-construction problem, and Dopplr models the garment.
Three layers, one answer.
The fit engine
Every style is specified before it goes live: cup construction, wire, band width and elasticity, coverage, strap configuration, fabric behavior. Every shopper is specified by measurements and bust shape. Fit outcomes are computed from the two, deterministically, the same way every time.
The fit conversation
She asks in her own words. Dopplr answers specifically, because it knows both the garment and her.
The fit visualization
The answer is shown on a 3D body built to her measurements and shape, the one she watched change as she entered them. Hotspots mark the construction features the answer depends on: the molded cup, the broad band, the strap. The visual doesn't restate the answer, it shows where the answer comes from.
A shopper meets a sports bra.
A guided run through a live sports-bra product page: she finds her fit, asks her own questions, and buys with confidence. This is the actual Dopplr experience, start to add-to-cart.
A different bra is a different question.
The fit answer, and the questions a shopper asks, change with the garment. Dopplr tailors both to the exact style on the page, not a generic size guide.
Reduce, and stay smooth.
Built to cut projection. The answer is about how much it flattens, and whether it stays invisible under a fitted shirt.
The chat, personalisedLock it down.
Built to control movement. The answer is about support and bounce at her size and impact level.
The chat, personalisedNatural, all day.
Built for all-day wear. The answer is about a natural shape, comfort by evening, and a clean line under a t-shirt.
The chat, personalisedLive with category leaders, with proven impact.
Not a pilot deck of projections: figures from a live intimate-apparel deployment, on real bra product pages.
From the Enamor (Modenik) deployment: the intimate-apparel proof case for the category. Full methodology: measurement window, session base, and observed-vs-modeled definitions, shared under NDA.
Every question she asks is something your merchandising team doesn't currently know.
Dopplr sees what shoppers actually worry about, style by style: which sizes generate hesitation, which shapes are underserved by the current size run, which construction features drive fit failure, and which questions precede a purchase versus an exit.
That becomes a feed into merchandising, size-run planning and design, not just a conversion tool on the PDP.
Fits your stack. Doesn't touch your roadmap.
Live on Shopify or Salesforce Commerce Cloud through a JS embed, with the security, data, and reporting answers your teams need, up front.
We ship as a Shopify app
A Shopify app built for your storefront and your styles, installed through a private link we create for you, not a generic App Store listing. Live on your product pages in days.
Goes live in weeks
Shopify, Salesforce Commerce Cloud, or any product page via a JS embed. No re-platforming.
Minimal lift from your team
Tech packs or garment specs, product imagery, and your size charts: the minimum to specify a style.
Your data, handled right
Measurements only: no images, no biometric identifiers. Retention and deletion policy stated up front.
Security & privacyProof in your dashboards
Per-style fit funnel, engagement, and conversion & return-impact reporting, down to the SKU.
Start small. Scale on evidence.
The fastest way to know if Dopplr moves your numbers is to run it on your own styles, briefly, against targets you set. No leap of faith, no year-one lock-in.
Scope your pilot together.
Three numbers from you, and we size a pilot that reaches a statistically significant read on conversion and returns, not a vanity metric.
for a 95%-confident read on a 15% lift (conversion + returns).
Measure
Engagement, conversion, and returns, scored against the success criteria you set before launch, so the pilot is judged on your number, not ours.
Grow
Clear the bar, and we shape a long-term program together, priced on the styles you put live.
Talk to us.
Whether you're scoping a pilot, running a security review, or just want to see Dopplr on your own product, our team will help. Book a call, or send us a note.