Shyna
Kovil AI Assistant
AI assistant. Verify important details with the Kovil AI team.
Deploy fashion styling and fit advisors that size customer profiles per item — not a generic size guide — construct visual outfit matches, and process multi-variant bundle carts in one step.
Use Cases
Deliver sizing fit advice and outfit visual bundles that reduce the #1 cause of apparel returns.
Asks a short set of body-measurement and fit-preference questions, then maps the answer directly to your brand's specific size chart per item — not a generic S/M/L guess — accounting for cut, fabric stretch, and category.
For multi-brand storefronts, the agent normalizes sizing across brands with different size charts, so a customer who knows their fit in one brand gets an accurate equivalent recommendation in another.
Builds complete outfit recommendations from a single item — matching shoes, outerwear, and accessories based on visual style compatibility and current catalog availability, not just 'customers also bought.'
Lets customers confirm an entire outfit — multiple sizes and colors across several products — into a single draft cart with one conversational confirmation, rather than adding each item manually.
Feeds confirmed returns data (wrong size vs. changed mind) back into the sizing model, so recommendation accuracy for a given SKU improves continuously rather than staying static after launch.
In Practice
A real deployment, walked step by step — from per-item fit modeling to a multi-agent outfit handoff to the merchandiser check before any chart changes.
Kovil AI built a per-item fit estimator on top of the brand's existing size charts, replacing a generic S/M/L guide with a model that accounts for fabric stretch, cut, and category before a customer ever checks out.
Instead of a static PDF size guide, the agent queries per-item chart data and current variant availability directly, so every recommendation reflects what's actually on the shelf.
Once a size is confirmed, the Outfit Orchestrator proposes coordinated pieces, and the Bundle Checkout Agent assembles every size and color into one draft cart — no manual re-entry between steps.
The returns-aware learning loop flags chronically mis-sized SKUs for review, but no chart is updated automatically — a human confirms every adjustment before it goes live.
The fit agent runs alongside the existing storefront, lowering returns while lifting basket size — without a single line of theme code touched.
How It Works
The agent captures body measurements and fit preference (snug, regular, relaxed) through a short conversational flow.
Measurements are matched against the specific item's size chart — accounting for fabric, cut, and category — not a generic scale.
If requested, the agent builds a full coordinated outfit, checking stock across every suggested piece.
Confirmed selections move to a draft cart; later purchase and return outcomes feed back into sizing accuracy.
Reliability
We embed conversational advisor blocks using standard widgets or headless storefront API calls, keeping your theme completely clean.
Multi-brand catalogs keep separate size chart logic per brand, preventing cross-contamination of fit recommendations.
Every size suggestion ships with a confidence indicator, and the agent proactively surfaces the next-best size when confidence is low.
Vetted models read stock levels across colors and variants, ensuring recommended sizes are always actually available.
Compatibility
Size Guide vs. Fit Agent
| Capability | Generic Size Guide | Kovil AI Fit Agent |
|---|---|---|
| Sizing basis | Generic S/M/L size guide chart | Per-item fit modeling with brand-specific charts |
| Multi-brand catalogs | One-size-fits-all sizing assumption | Per-brand chart normalization |
| Outfit building | Static 'frequently bought together' | Visual style-compatibility matching, stock-checked |
| Learns from returns | No feedback loop | Continuous accuracy improvement from return data |
| Checkout flow | Manual add-to-cart per item | Single-command multi-item bundle checkout |
FAQ
Answers regarding styling workflows and catalog sizing.
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