AI Shopping Assistant — Conversational commerce that converts
An always-on conversational shopping experience grounded in your real catalog, customer behavior, and Synerise's full AI stack — not a generic LLM wrapper.
Use cases
Personal Styling at Scale
The AI Shopping Assistant becomes a virtual fashion stylist for every shopper, suggesting complete outfits, complementary pieces, and curated looks tailored to individual preferences and behavioral signals.
Dynamic Cross-Sell in Fashion
Beyond a single product, it recommends matching items — pants for a chosen jacket, shoes to complete the look, accessories that elevate an outfit — driving meaningful basket growth instead of one-off transactions.
Frictionless Customer Journey
Conversational guidance removes friction from product discovery and decision-making, turning browsing into a coherent, enjoyable shopping experience that mirrors a high-end in-store interaction.
Six things an LLM wrapper can't do
Fast implementation (DC, SDK, API)
Multiple integration paths — Dynamic Content, SDK, and REST API — let retailers go live quickly without ripping out the existing stack. Plug the assistant into your current commerce, search, and data layers, and start serving conversations in weeks, not quarters.
Profile context access (behavioral data)
The assistant taps directly into Synerise's behavioral profile of each shopper — events, preferences, and purchase history — instead of starting every conversation from zero. Every reply is informed by who the customer actually is, what they've browsed, and what they're likely to want next.
Product context access
Live access to the catalog and product attributes — stock, variants, pricing, metadata — keeps every answer grounded in the retailer's real assortment. The assistant only recommends what's actually buyable, in the right size, color, and price point, eliminating dead-end suggestions.
Recommendations & full behavioral AI stack
Built on Synerise's recommendation engine and behavioral AI stack — BaseModel, Cleora, and propensity models — so suggestions are personalized and predictive, not generic LLM guesses. The assistant inherits years of retail-grade modeling instead of relying on a chat model alone.
Cost-optimized for enterprise scale
The system is engineered to keep per-conversation cost low while sustaining enterprise traffic volumes — through model routing, caching, and efficient retrieval. That makes large-scale rollout economically viable, not just a flagship demo for a handful of sessions.
Proactive engagement
The assistant doesn't just answer when spoken to — it proactively initiates and sustains engagement at the right moments to drive conversion and retention. From timely nudges to follow-ups across the journey, it behaves like an attentive associate, not a passive chat box.
See it in action
Pilot the AI Shopping Assistant with your catalog and behavioral data — see conversion lift on guided sessions.