SWYF — See What You Fit

An AR + AI platform that reimagines online fashion shopping by providing personalized, privacy-first virtual try-ons to reduce returns, increase conversions, and improve sustainability.

What inspired the project

SWYF was born from clear, recurring problems in fashion e-commerce:

High return rates. Roughly (25%) of online orders are returned — about 32.9 million orders daily — costing roughly \$1.48 billion per day in logistics and restocking. Average cost per returned item: \$45.
Abandoned carts. Around (70%) cart abandonment (~307 million daily) due to hesitation, hidden costs, or friction at checkout.
Lack of personalization & decision fatigue. Shoppers struggle to know whether an item will fit or suit them.
Sustainability concerns. Returns cause extra shipping, emissions, and waste.
Safety & privacy in physical trial rooms. Issues like hidden cameras increase demand for private virtual alternatives.

These issues motivated a privacy-first, AI-driven virtual fitting solution to improve buyer confidence, reduce returns, and lower environmental impact.

What we learned

Key insights from research, prototypes, and industry examples:

AR + AI materially improves outcomes. Case studies show AR/AI can reduce returns (often by up to ~40%) and significantly increase conversion.
Personalization matters. Recommendations tuned to body shape, measurements, and skin tone increase confidence and loyalty.
Omnichannel integration is powerful. Linking online try-ons with in-store experiences creates seamless customer journeys.
Privacy must be built in. On-device processing and strong cryptographic controls earn user trust.
Sustainability is both ethical and commercial. Fewer returns = lower carbon footprint and cost savings for retailers.

How we built it

Core idea: an AI-powered virtual fitting room that provides realistic AR overlays, accurate sizing, and personalized style recommendations while keeping sensitive data private.

Key features

Real-time Virtual Try-On (AR overlay): Camera/webcam or in-store smart mirrors show garments on the user in real time.
AI Personalization: Models build user profiles from measurements, preferences, and skin-tone analysis.
Accurate Fit & Sizing: Per-product sizing recommendations to minimize size mismatches.
Privacy & Security: On-device processing for sensitive inputs (selfies, body metrics). Optional encrypted storage via IPFS + blockchain for consented use.
Omnichannel Experience: Web, mobile, and retail mirror support with consistent recommendations.
Tokenized Rewards: Users earn tokens for engagement, referrals, and purchases (redeemable for discounts/perks).

User journey

Upload a selfie or use live camera (processed locally).
AI extracts body proportions and skin tone.
Platform suggests sizes, colors, and outfits.
User tries items virtually with AR overlays and adjusts fit parameters.
Checkout via secure payment; users can track orders and returns.

Tech stack (representative)

Frontend: React + TypeScript, Vite, TailwindCSS, Radix/Shadcn UI, React Router, TanStack Query, React Hook Form, Zod.
Backend / Services: Flask or FastAPI (Python), OpenCV, NumPy, TensorFlow / PyTorch models, Colormath for color analytics.
3D & AR: Three.js, Unity (AR Foundation), Blender for assets.
Data & infra: Firebase / PostgreSQL / MongoDB, cloud training pipelines for models.
Security: AES encryption, OAuth2, HTTPS/TLS.
Blockchain (optional): Hybrid PoS + PoA for token ledger and tamper-evident records; IPFS for user-consented media storage.

Challenges faced

AI & AR realism: Realistic cloth simulation and accurate fit across body types is computation-heavy and data-intensive.
Privacy compliance: Ensuring true on-device processing and transparent consent flows.
Integration complexity: Building flexible APIs and plugins to work with varied e-commerce platforms and catalog formats.
Performance: Delivering low-latency AR on low-end devices while keeping model size and battery use reasonable.
Continuous training & data quality: Maintaining high-quality labeled data and scalable pipelines for model updates.
Partner adoption: Convincing brands to adopt new UX and to provide accurate product metadata (measurements, material behavior).

Impact model (math)

Define:

N = daily orders
r = baseline return rate (fraction)
Δr = relative reduction in return rate due to SWYF (fraction)
C = average cost per return (USD)

Then daily savings:

Daily Savings = N × (r × Δr) × C

Example:
N = 1,000,000
r = 0.25
Δr = 0.40
C = 45

Daily Savings = 1,000,000 × (0.25 × 0.40) × 45
= 1,000,000 × 0.10 × 45
= 4,500,000

So: $4,500,000 saved per day

Conclusion

SWYF combines AI, AR, and privacy-first design to reduce returns, increase conversions, and promote sustainable shopping. With strong brand partnerships and continued model improvement, it can materially lower costs for retailers while improving customer experience and reducing environmental impact.