Accessible to buyers directly on the product page. Runs automated in the cloud.
maketribe simulates how clothing fits on real bodies — based on precise body measurements, real garment data, and a mathematical model. Not "size M fits you", but: how each piece actually fits.
maketribe is a cloud-based fitting intelligence platform. The fit analysis is accessible to buyers directly on the product detail page — no app, no redirects. Behind the scenes, the technology calculates in real-time how a garment fits on an individual body — based on a mathematical model instead of statistical prediction. With 0.59cm measurement accuracy, per-body-area analysis, and consideration of fabric properties and elasticity, maketribe sets the new standard for fitting intelligence in fashion e-commerce.
Partners & backers — trusted by innovative fashion brands
Most systems rely on purchase and return data. This sounds logical — but has fundamental flaws:
Result: statistical guessing instead of real fit calculation.
The central question is not:
"What size do similar customers buy?"
But rather:
How does this garment behave on this body?
Physics-based calculation instead of statistical prediction — four layers, one result.
Precise reconstruction of individual body measurements with up to 0.59cm accuracy. Not height/weight, but real measurements: shoulders, chest, waist, hips, arm length — per body area.
Analysis of garment dimensions — from standard size charts to detailed cut patterns, material composition, and elasticity. The system works with a basic size chart and becomes more precise with additional data.
Physics-based calculation of how the garment behaves on the individual body. Not a statistical model from other users' purchase data — but a deterministic simulation based on real parameters.
Per-body-area analysis: "Size M fits shoulders and chest perfectly, sits slightly close at the waist. Size L gives more room." The customer understands the fit and makes an informed decision.
maketribe's fit analysis is accessible to your customers directly on the product detail page — the platform itself runs in the cloud.
Part of the normal buying decision.
maketribe is an automated infrastructure for fitting intelligence.
Works with standard size charts out of the box — no perfect dataset required. The system processes unstructured data, adapts to brand logic, and becomes more precise automatically as more product information is added.
Body × garment: Fit is calculated in real-time for every combination. Analysis of all sizes, consistent across the entire catalog.
Integration of return and fit feedback. Continuous improvement — the system gets better with every interaction.
Ideal for Shopify stores: one-click installation, no developer needed. Also available for Magento and other platforms. REST API for custom integrations.
The most common concerns — and why they don't apply to maketribe.
No problem. maketribe works with a standard size chart. No body scanning required, no detailed pattern data needed to start. The more data you have, the more precise — but getting started is simple.
One-click installation as a Shopify app. No custom setup, no IT team needed. Live in minutes — not weeks. Also available for Magento and other platforms as a simple plugin.
No customer has to do a body scan. maketribe offers multiple input methods — from simple inputs (height, weight, age) to photo-based capture to precision measurement. You choose what fits your audience.
Plans from €130/month — suitable for small and mid-size stores. Fast ROI through fewer returns and higher conversion. No long-term commitment required.
Adaptation to brand-specific sizing logic. Using all available product data — including unstructured data.
Cut, material, elasticity — each product is individually modeled, not generically categorized.
Return reasons, fit feedback, and usage signals flow into the optimization.
Oversized, stretch, individual preferences, product-specific fit instructions — the platform maps real-world complexity.
Result: robust fit calculation under real-world conditions.
We handle the entire process. Zero development effort on your side.
A standard size chart is enough to get started. We analyze your existing product data, fill in missing information, and create an individual fit concept for each product type. No perfect data needed — we work with what you have.
A digital twin of your shop is created — so we can test the software together in a test environment and make final adjustments.
After testing, we set up your cloud environment (server in Stockholm) and integrate the size recommendation into your Shopify shop. Zero development effort on your side.
From body measurement capture to size recommendation — directly in the browser, in under 60 seconds.
Adapted to your target group: from quick inputs to photo-based capture to precision contour measurement. Always in the browser, no app.
Our AI creates a 3D avatar with all relevant body measurements — neck, shoulders, chest, waist, hips, arm length, leg length.
The Size and Fit Engine (SFE) combines body data with product measurements, fabric properties, and individual fit preferences.
Your customer receives the right size with detailed fit analysis — and can virtually try on the garment right away.
maketribe is the infrastructure for accurate digital fit — size recommendation, visual try-on, and style recommendations, connected by an automated data pipeline.
We don't just say "you're a Medium." That's too simple — because the size label alone says little about fit. Instead, our mathematical model analyzes how each available size fits the customer's shoulders, chest, waist, hips, and arm length. The customer sees: "Size M fits your shoulders and chest perfectly, sits slightly close at the waist. Size L gives more room — which do you prefer?" This analysis combines high-precision body measurement (0.59 cm deviation from actual measurements) with the garment's real dimensions, fabric type, and elasticity.
Your customers see how every available size looks on their body — realistic and in real-time. They make the right purchase decision before ordering.
Smart outfit recommendations: the customer tries on a t-shirt — and gets matching pants in the right size suggested. Cross-selling based on real fit data.
The data we collect becomes valuable business insights — a closed feedback loop from customer orders to production planning.
Key capabilities
maketribe virtual try-on software provides: high-precision body measurement reconstruction with multiple capture methods adapted to each target group — without app download, AI garment fit prediction using real body measurements instead of avatars, clothing size recommendation AI with confidence scores, automated data pipeline that connects all modules and cleans messy datasets, plugin integration for Shopify, Magento and other ecommerce platforms, and a REST API for custom integrations. The technology serves fashion ecommerce, second-hand and resale platforms, workwear, and made-to-measure. All data processing is GDPR compliant and hosted in Europe.
No IT team needed. We integrate the virtual try-on into your shop — or you use our API.
Ideal for Shopify: one-click installation directly from the Shopify App Store. Live in minutes, no developer needed. Also available for Magento and other platforms.
Full documentation for technical teams that need maximum control and custom integrations.
All data processed in Europe. Privacy from day one. No transfer to third countries.
Our focus: DTC fashion ecommerce. Our virtual try-on technology: flexible for any industry.
Plugin integration for online stores. Strengthen purchase decisions, increase conversion — and reduce returns as a result.
Custom software solutions: digitally measure employees and automatically assign sizes — without physical fitting, without manual spreadsheets.
Performance fit for demanding athletic apparel — precisely matched to individual body shapes and movement profiles.
Digital body measurement for custom clothing — scalable, without tailor visits, deployable worldwide.
Allow buyers to visualize unique vintage and secondhand items on their own body and predict fit for one-of-a-kind garments — even without model photos. Dramatically increases resale conversion.
Typical applications and the potential our virtual try-on technology unlocks.
Customers who know their size, buy. maketribe's virtual try-on gives them that confidence at point of sale — as a plugin, no IT effort.
When customers order the right size, returns drop automatically — less logistics, more margin.
Instead of manual measurement: employees capture their measurements digitally. Automatic size assignment — scalable for any team size.
Secondhand platforms suffer from poor product presentation. Virtual try-on gives single items a professional on-body visualization — and buyers the confidence to actually purchase.
How the leading approaches to virtual try-on and AI size recommendation compare.
| Criteria | Quiz-based | Avatar System | maketribe |
|---|---|---|---|
| Measurement accuracy ≤ 1 cm | ✕ | ~ | ✓ 0.59 cm |
| Real body measurements (not self-reported) | ✕ | ✕ | ✓ |
| Per-body-area fit analysis | ✕ | ✕ | ✓ |
| Mathematical model (not statistical) | ✕ | ✕ | ✓ |
| Fabric & elasticity analysis per product | ✕ | ✕ | ✓ |
| No cold-start problem (no historical data needed) | ✕ | ✕ | ✓ |
| No app download | ✓ | ~ | ✓ |
| Realistic visualization | ✕ | ~ | ✓ |
| Messy data support | ~ | ✕ | ✓ |
| No IT team required | ✓ | ✕ | ✓ |
| Body measurement prediction based on target group analysis | ✕ | ✕ | ✓ |
| Cross-industry (Fashion, Workwear, Second-Hand, Made-to-Measure) | ✕ | ✕ | ✓ |
| GDPR compliant | ✓ | ~ | ✓ |
20% reduction in returns (at avg. €20 per return = immediate ROI). +1% conversion rate increase. Cross-selling through fit-based outfit recommendations. Your customers receive the same personalized fit guidance online as they would from the best sales associate in your flagship store — at scale, 24/7. Data enrichment and stronger buyer confidence.
80% faster decision-making. The ability to choose garment fit — e.g. whether a dress should sit tight or loose at the waist. See your own avatar in different outfits. Less time and money spent on returns.
For the Environment
Fewer returns — less packaging, shorter transport routes, less textile waste, less CO₂, more sustainability.
Mathematician with 10+ years in management consulting, including 5 years as Data Science Manager developing AI solutions for DAX corporations. MSc in Financial Mathematics with focus on Data Science.
Virtual try-on software (also known as a virtual fitting room, digital try-on, or online try-on) is technology that allows customers to see how clothing looks on their own body before they buy. Unlike traditional size charts or quiz-based tools, modern virtual try-on software uses real body measurements and delivers significantly more accurate garment fit prediction results than conventional clothing size recommendation methods.
In fashion ecommerce, return rates average around 40%. The primary reason: customers cannot assess fit and size online. This leads to cart abandonment (lost conversion) and multi-size ordering (returns). Virtual try-on software solves both problems simultaneously — customers get size confidence, stores get higher conversion and fewer returns. Precision body measurement reconstruction is the foundation that makes this accuracy possible.
maketribe combines multiple precision body measurement methods with AI size recommendation and virtual try-on in a single plugin for online stores. The process: depending on the target group, multiple precision capture methods are available — from simple inputs to contour-based measurement. Everything runs directly in the browser, no app download required. From the data, maketribe creates a precise body model and recommends the optimal size for each garment. Optionally, the fit is visualized on the customer's own body model through virtual try-on.
AI size recommendation answers the question "What size should I order?" — typically as a text recommendation (e.g., "Size M, Perfect Fit"). Virtual try-on goes further and visually shows how the garment looks on the customer's body. maketribe offers both: AI size recommendation as a conversion driver on every product page, and virtual try-on as the visual purchase argument. Together, they provide comprehensive garment fit prediction.
Many virtual fitting room solutions require end customers to download a separate app — a massive friction point that reduces adoption and conversion. maketribe's virtual try-on software works as a plugin directly in the online store: Shopify, Magento, and other platforms are supported. Integration is handled by maketribe — no IT team required on the client side. For technical teams, a complete REST API with documentation is also available.
Virtual try-on technology is rapidly evolving from a novelty to a core component of fashion ecommerce infrastructure. As body measurement reconstruction technology becomes more accessible and adaptive to different user groups, the barrier to adoption is disappearing. The shift from quiz-based sizing tools to real body measurement-based systems represents a fundamental change in how online shoppers interact with fashion products.
Key trends shaping the future of virtual try-on software include: integration of AI size recommendation directly into product pages (not as a separate flow), real-time garment fit prediction that accounts for fabric stretch and drape, cross-brand size consistency powered by standardized body measurement data, and expansion beyond fashion into second-hand and resale platforms, workwear, uniforms, sportswear, and made-to-measure applications.
For ecommerce operators, the question is no longer whether to implement virtual try-on, but which approach delivers the most accurate results. Body measurement-based systems like maketribe consistently outperform quiz-based and avatar-based alternatives because they start from ground truth: the customer's actual body dimensions.
Conventional size recommendation tools like True Fit or Fit Analytics rely on statistical models built from millions of purchase and return events. They predict which size you'll keep based on what similar customers kept. The approach works at scale, but it has a fundamental limitation: it says "Size M" without explaining why, and it can't account for how that specific garment actually fits your specific body. maketribe takes a fundamentally different approach. Our technology is built on a mathematical model developed by founder and mathematician Sona Gallinger. Instead of relying on purchase statistics, we measure the customer's actual body with 0.59 cm accuracy (the industry standard is 1.5–2 cm) and compare those measurements against the garment's real dimensions — including fabric type, elasticity, and cut geometry.
The result is not a simple size label. It's a detailed fit analysis per body area: how does Size M sit on your shoulders, chest, waist, hips, and arms? The customer sees exactly how each available size will fit their body, and can choose based on their personal preference — whether they want a close fit or more room. This is the difference between "you're a Medium" and "here's how Medium fits your body — would you prefer Large?"
This approach doesn't depend on having millions of historical purchase records. The precision comes from the mathematical model itself — from body measurement accuracy, garment dimension data, and fabric behavior modeling. That's why maketribe delivers higher accuracy than tools that have been on the market for a decade with far more data: because the model is better, not bigger.
Fashion ecommerce faces a structural returns problem: approximately 40% of online clothing orders are returned, with 70% of those returns caused by wrong size or poor fit. The economic impact is severe — each return costs an average of €20 in Europe, destroying margin and creating environmental waste through logistics and repackaging.
The root cause is simple: customers cannot try clothing on before buying online. Traditional size charts vary between brands, self-assessment is unreliable, and photos don't communicate fit. This uncertainty leads to two costly behaviors: cart abandonment (customers who want to buy but don't trust the size) and bracket ordering (buying multiple sizes with intent to return).
Virtual try-on software with real body measurement technology addresses both behaviors. By giving customers objective size confidence at the point of purchase, virtual try-on simultaneously increases conversion rates (more confident buyers complete checkout) and reduces returns (fewer wrong sizes ordered). The combination of AI size recommendation and visual garment fit prediction creates the most effective approach available today.
We analyze your top products, train the AI on your data, and show you the predicted return reduction — before you scale. Plans start at €130/month. Custom pricing for larger catalogs.
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