Project Overview
The Challenge
For a premium jewellery brand, the purchase decision is deeply personal. A customer may love a necklace in isolation, but the real question is: does it suit my face, my skin tone, my outfit, and my occasion? That question was impossible to answer online and it was costing the brand sales.
Key Retail & Digital Problems
- Online conversion rate of 2% was well below the category benchmark of 4% for jewellery e-commerce.
- High return rate: 25% of orders were returned citing 'does not look as expected', with each return costing INR 400 in logistics and restocking.
- Average customer browsed 20 product pages per session but added only 1 or 2 items to the cart — representing high intent but low confidence.
- Showroom staff could style customers in person, but the website had no equivalent virtual capability.
- WhatsApp-based manual styling queries were taking 24-48 hours to respond to, handled by a dedicated 6-person team.
- Competitors in the mid-market segment were beginning to introduce basic virtual try-on — the brand needed to differentiate at the premium end.
The brand had the inventory depth, the brand equity, and the customer intent. What it lacked was a way to bridge the gap between browsing and buying — specifically, the ability to show a customer how a combination of jewellery pieces would look on her, across different looks and occasions.
The Solution
Vidhema Technologies designed and deployed an AI-powered Virtual Try-On and Style Generator — a feature embedded directly into the brand's product pages and a dedicated 'Style Studio' section on the website and mobile app.
How It Works — The Customer Experience
The flow is designed to be simple enough for a first-time user, with zero technical knowledge required:
| Signal | What It Captures |
|---|---|
| 1. Select Your Jewellery | Customer browses the catalogue and pins up to 8 SKUs to her 'Try-On Tray' — mixing categories (necklace + earrings + maang tikka + bangles) and metal types (gold, diamond, kundan, polki). |
| 2. Upload Your Photo | Customer uploads a selfie or portrait photo. The system accepts standard smartphone photos. Face detection validates the image quality before proceeding. |
| 3. Choose Your Looks | Customer selects one or more styling themes: Traditional, Western, Bridal, Suits & Fusion, or All Looks. Each theme applies a different background, attire context, and jewellery layering logic. |
| 4. AI Generates the Looks | The AI engine composites the selected jewellery onto the customer's image — preserving her face, skin tone, and features — while rendering each look in the appropriate styling context. Processing time: 18-25 seconds per look. |
| 5. Review, Share & Buy | Customer receives a gallery of generated images — downloadable, shareable via WhatsApp, or saveable to her profile. Each image has direct 'Add to Cart' links for the jewellery pieces shown. |
Technical Architecture & Core AI
- Image segmentation model (fine-tuned on Indian skin tones and facial structures) to accurately detect face, neck, ears, wrists, and hairline.
- Generative AI layer (diffusion-based) for compositing jewellery images onto customer photos — preserving facial identity while adapting lighting, shadow, and reflectivity per jewellery type.
- SKU rendering pipeline: each jewellery product photographed with consistent lighting and converted to a compositing-ready asset (transparent background, multi-angle renders for earrings, bangles, necklaces).
- Look-specific style prompting engine: each theme (Bridal, Western, etc.) carries a parameter pack controlling attire context, background, jewellery density, and lighting temperature.
- Combination logic model: learns from session data which SKU combinations are selected together and predicts high-affinity pairings for the Mix & Match look.
- Cloud-native deployment on AWS; auto-scaling to handle high-concurrency periods (festival season, wedding season peaks).
- Privacy-first design: uploaded photos processed in-session only, not stored, with explicit user consent flow built in.
The Five Looks Generated
What the AI generates based on look themes chosen by the customer:
Traditional / Original
Customer's photo rendered with selected jewellery against a classic Indian backdrop. Attire context: saree / lehenga. Lighting and jewellery placement adjusted for traditional aesthetic. Ideal for everyday and festive wear decisions.
Bridal
Full bridal styling — high jewellery density, layered necklaces, statement maang tikka, heavy bangles. Background: mandap / floral setting. Skin tone warmth adjusted for bridal photography lighting. Most-used look for wedding shoppers.
Western
Clean, minimal styling for the same pieces in a contemporary context. Single statement piece foregrounded. Background: neutral/studio. Demonstrates versatility — showing that a traditional piece can transition to evening wear.
Suits & Fusion
Mid-weight styling suited to salwar kameez and indo-western outfits. Jewellery combination logic adjusted — lighter neckpieces foregrounded, statement earrings emphasised. Preferred by working professionals and NRI customers.
Mix & Match (Auto-Suggested)
AI suggests an alternative combination from the customer's Try-On Tray — pairing pieces that complement each other based on metal type, weight, and occasion fit. Surfaces lesser-viewed SKUs from the tray and increases basket size.
Results — 6 Months Post Launch
Integration Points
To deliver a seamless experience, the AI Style Studio was tightly integrated into the brand's retail technology stack: (1) Website / App: React-based 'Style Studio' embedded in PDPs and as a standalone section; mobile-optimised with progressive loading. (2) Product Catalogue: Live API sync with the brand's PIM — real-time SKU availability, pricing, and cart integration per look. (3) WhatsApp: Generated looks shareable directly to WhatsApp with product deep-links — extends reach to family/friend purchase influencers. (4) CRM: Style sessions saved to customer profile; used for personalised remarketing ('Complete the look you tried last week'). (5) Analytics: Look engagement data (which themes used, which SKUs tried most) fed into merchandising and buying decisions.
Operational Impact
The WhatsApp-based manual styling query queue previously handled by a 6-person team over 24-48 hours dropped by 70%. The team was redeployed to handle high-value bridal consultation queries only, improving response quality for the highest-ticket customers. The Mix & Match auto-suggestion look directly impacted inventory performance: 20% of SKUs that had low page views but were surfaced via AI combinations saw a 3-5x increase in add-to-cart events within the first 60 days.
Why This Matters for Jewellery & Premium Retail
| Metric | Without AI Recommendations | With AI Recommendations |
|---|---|---|
| Visual Representation | Product photos on generic models | Customer sees jewellery rendered accurately on their own face |
| Occasion Context | No occasion context; items viewed in isolation | 5 looks ranging from bridal to casual generated in seconds |
| Return Rate Drivers | Returns driven by unmet expectations ('does not look as expected') | Styling confidence set before purchase; returns dropped by 34% |
| Styling Advice | Styling advice gated behind showroom visits | 24x7 digital personal stylist accessible to all users online |
| Customer Support Queue | WhatsApp queries queue takes 24-48 hours to resolve | Instant AI-generated styling shareable immediately with buying influencers |
| Average Basket Value | Low cart value; single SKU consideration only | Multi-SKU coordinate looks drive a 3.0x increase in basket value |
Project Details
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