AI Shopping Assistant

Turn conversations into transactions.

The conversational layer of your store. Rooted in the same behavioral AI infrastructure that already powers your platform - it advises like an excellent salesperson, on-brand and at scale.

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Conversion on assisted sessions consistently outperforms standard product pagesResponse time below 400 ms - a real-time experienceIntegration in weeks, not quarters

Starting point

Conversation isn't everything

Most “shopping chatbots” are a thin layer over a language model - they answer fluently, but they don't know who is asking, what the person is currently viewing and whether the recommended product is even in stock. The result: the conversation sounds good, but it doesn't close the sale.

The Synerise assistant works the other way around. It uses the same behavioral engine that already knows your customers - their purchase history, preferences, browsing paths and current intent. Every answer is grounded in a real catalog, current availability and a specific person's profile, not in the model's averaged patterns.

Conversion on assisted sessions consistently outperforms standard product pages (pilot deployment at an international fashion retailer)

Response time below 400 ms - a real-time experience, not a wait

Integration in weeks, not quarters thanks to DC, SDK and REST API paths

Conversational shopping advice

The assistant holds a natural conversation with the customer - it understands their needs, style and budget described in everyday language, just like a conversation with an experienced salesperson in a showroom.

Instead of typing keywords and battling through filters, the customer says: I'm looking for an autumn jacket, smart-casual, up to 200 zł - and immediately receives precise recommendations. For the customer this means finding the right product faster, less frustration and the feeling of being served as in a showroom, not as in a search engine.

Business value: a higher conversion rate on assisted sessions, deeper engagement, lower cart abandonment

Recommendations based on real-time behavior

The assistant doesn't display yesterday's bestsellers - it analyzes current behavioral signals: what the customer was just browsing, what they saved, what they bought before.

Every answer is rooted in the current session context, not in generic patterns. The customer receives proposals matching their current intent, saves time on browsing and doesn't have to scroll through irrelevant suggestions.

Business value: more accurate recommendations than static top-product lists, higher AOV, better retention of returning customers

Recommendations based on real-time behavior

Checking stock and variants

The assistant verifies product availability live in a specific size, color or variant - it eliminates dead ends in product discovery.

The customer asks: “Is it available in size M?” and at that very moment gets an answer grounded in the real catalog. As a result, they are confident that the recommended product is actually available, don't waste time on items they won't buy, and make a purchase decision faster.

Business value: elimination of suggestions for unavailable products, reduced customer frustration, a higher session-closing rate

Cross-sell and completing outfits

The assistant suggests complementary products - not random ones, but matched to the chosen item and the customer's preferences.

Instead of a one-off transaction, it builds complete sets: jacket + trousers + shoes, coffee + machine + mug. The customer gets ready-made, coherent outfits without having to compose them on their own, shopping inspiration and a complete solution instead of individual products.

Business value: basket growth, selling sets instead of individual products, higher revenue per session

Proactive customer engagement

The assistant doesn't wait for the customer to ask a question - it initiates the conversation itself at the right moment: during long browsing, hesitation before a purchase or a return after a break.

It works like an attentive salesperson in the store, not like a passive chatbot. The customer gets help exactly when they need it, doesn't have to look for answers on their own and feels that the brand cares about them.

Business value: a higher conversion rate on sessions without an active query, rescuing abandoned sessions, building loyalty through relevant help

Customer behavioral profile (context from Synerise)

From the very first message, the assistant knows who the customer is - it knows their purchase history, category preferences, budget and behavior patterns.

It doesn't start every conversation from scratch, but continues the relationship that Synerise builds across the entire customer journey. The customer doesn't have to explain their preferences from scratch every time, feels the continuity of the relationship with the brand and receives recommendations that take their whole history into account.

Business value: personalization without the need to collect data in the conversation, more accurate recommendations from the first answer, higher NPS

Blog

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