Shyna
Kovil AI Assistant
AI assistant. Verify important details with the Kovil AI team.
Deploy conversational beauty agents that analyze skin profiles, shade-match foundation tones from a photo, and bundle full routines directly into checkout — replacing static quiz funnels with a real advisor experience.
Use Cases
Deliver personalized consultations that replicate the in-store beauty advisor experience online.
Runs a conversational intake — skin type, concerns, climate, current routine — and builds a structured customer profile that grounds every downstream recommendation, replacing static quiz funnels with a real dialogue.
A vision-language model analyzes an uploaded selfie under normal lighting, extracts undertone and depth, and cross-references your actual variant catalog to recommend the correct foundation, concealer, or powder shade code.
Once a profile exists, the agent assembles a complete AM/PM routine — cleanser, treatment, moisturizer, SPF — checking real-time stock and adding the full bundle to a draft cart with one conversational confirmation.
Cross-references stated allergies or sensitivities (fragrance-free, sulfate-free, pregnancy-safe) against your ingredient metadata before ever recommending a product, flagging conflicts instead of silently ignoring them.
Follows up after delivery with usage guidance and check-ins timed to product consumption rate, catching early dissatisfaction and prompting timely replenishment before the customer even thinks to reorder.
In Practice
A real deployment, walked step by step — from native store tools to multi-agent handoffs to the human check before anything ships.
Kovil AI designed an integrated agent network on top of the brand's existing Shopify store, automating retention campaigns and supplier spec-sheet ingestion end to end.
Instead of a flat data export, a custom MCP server lets the agents query product descriptions, live inventory levels, and order state directly — and reason over them, not just read them.
The Manager triggers the Design Agent to generate a campaign banner from a supplier spec sheet, then routes the finished asset to the Communication Agent to draft the marketing email — one workflow, no hand-offs between tools.
Pricing overrides and campaign launches are held for review and routed to the merchant as a Slack approval request — the agents act, but nothing ships without sign-off.
Fewer tickets reaching a human, more recovered carts, and zero manual hours spent on recurring content — all running on the store's existing stack.
How It Works
The agent runs a structured skin/hair profile questionnaire through chat, not a static multi-page quiz form.
If shade matching applies, a vision model processes the uploaded photo and extracts tone data in real time.
Profile and shade data are matched against your live product catalog via vector search, respecting current stock.
Recommendations move to a draft cart for one-click checkout, with post-purchase coaching scheduled automatically.
Trust & Data Privacy
Skin photos and health-adjacent data need stricter handling than a typical product query — here's how we treat it.
Uploaded shade-match photos are processed in memory and discarded immediately — never stored, never used for model training.
Agents are scoped to cosmetic recommendation language only, with disclaimers directing dermatological concerns to a professional.
Customer profile data (skin type, concerns) is stored in your own Shopify metafields — never a third-party database outside your control.
Shade match results show a confidence score rather than presenting an automated guess as certain fact.
Compatibility
Quiz App vs. Conversational Agent
| Capability | Generic Quiz App | Kovil AI Agent |
|---|---|---|
| Recommendation basis | Static multiple-choice quiz | Conversational profile + real catalog + vision model |
| Shade matching | Not available or manual | Vision-model shade extraction, sub-5-second response |
| Ingredient safety checks | Manual label reading by customer | Automatic cross-check against stated allergies |
| Stock awareness | May recommend out-of-stock items | Real-time inventory checked before every suggestion |
| Post-purchase engagement | Generic drip email sequence | Consumption-timed coaching and replenishment prompts |
FAQ
Answers regarding our vision model shade match pipelines and data handling.
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