AI Shopping Assistant
The conversational layer of your storefront. Grounded in the same behavioral AI infrastructure that already runs your platform — it advises shoppers like a great in-store associate, on-brand and at scale.
Two ways to build an AI shopping experience: bolt an LLM API onto a search bar, or ground it in behavioral infrastructure. We're doing the second.
Context Aware Agent
Shopping Assistant
Synerise Shopping Assistant is built on the behavioral AI infrastructure that already powers your platform — every conversation is informed by real-time signals. Customers describe what they want in natural language: a style, a budget, a use case, a vague idea. The assistant returns products that actually match — not the top SKUs by margin, not yesterday's bestsellers, but items grounded in what this specific shopper is signaling right now.
Point the assistant at an existing search index, map your item attributes, describe your brand, and it's live. Context comes for free, because the rest of the Synerise stack is already collecting it.
The Perfect Blend
Agents
Agents built for productivity, interoperability and growth
Behavioral AI
AI Search, Ranking, Promotions, Real-time recommendations, Time optimization, Predictions
Working AI
Real, measurable value: understanding your customers, predicting their needs, and communicating with them in the right way at the right time.
Selected Use Case
Use Case Spotlight: Virtual Fashion Stylist
One of the clearest expressions of the AI Shopping Assistant is the virtual fashion stylist — a conversational experience that helps shoppers compose outfits, discover complementary pieces, and complete looks in real time.
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.
While fashion is a flagship example, the same conversational logic applies across categories — beauty, home, electronics, and beyond — wherever shoppers benefit from expert guidance and curated combinations.
The AI Shopping Assistant changed how our customers discover outfits — it doesn't just answer questions, it styles. Conversion on assisted sessions consistently outperformed our standard product pages.
Differentiators
Six things an LLM wrapper can't do
Six properties that separate it from anything you can build on top of an LLM API alone.
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.
Under the hood
System architecture
The Shopping Assistant Service orchestrates semantic caching, custom nodes, search & reranker, tools and the LLM gateway — grounded in Synerise's behavioral models, item attributes, search, recommendations and Brickworks, with embeddings shared across the stack.
Request with user prompt
Shopping Assistant Service
Semantic Caching Layer
Custom Nodes (e.g. fast intro node)
Search & Reranker node
Tools
LLM Gateway
LLM Model (via Azure Foundry)
Grounded in Synerise capabilities
Synerise ML/AI models (SLMs, classifiers, …)
Items Attributes
Items Search
Recommendations
Brickworks
Embedding models
Interoperability
Open by Design
Synerise embraces open protocols to ensure its agent capabilities integrate seamlessly into any AI ecosystem. MCP and A2A compatibility make Synerise a collaborative platform, not a walled garden.
MCP Server
Synerise exposes its capabilities through the Model Context Protocol, enabling external AI systems to leverage Synerise as a tool provider.
Agent-to-Agent (A2A)
Compatible with the Agent2Agent protocol for seamless interoperability — Synerise agents can collaborate with agents from other platforms.
Claude Code plugin
A native Claude Code plugin makes Synerise a first-class tool inside the engineering agent workflow — query, configure, and act on the behavioral AI infrastructure directly from the terminal.