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E-Commerce

"What was that coffee I ordered in January?" Your assistant should know.

Most shopping assistants treat every conversation like the first one. Hebbrix gives yours a memory that builds over weeks and months. Returning customers get recognized. Recommendations get sharper. The whole experience starts feeling less like a search bar and more like a personal shopper.

Start BuildingHow Memory Works

What your assistant sees when a customer returns

A real customer profile built automatically from past interactions.

Customer Profile: Rachel M.
12 INTERACTIONS
Size
M / 8 (shoes)
Style
Earth tones, minimalist
Budget
$100-300 range
Favorite Brands
PatagoniaAllbirdsEverlane
Recent Orders

Patagonia Torrentshell 3L (Jan 15), earth tone, size M

Allbirds Tree Runners (Dec 2), natural white, size 8

Everlane Organic Cotton Tee x3 (Nov 18), gift for someone, sizes S/M/L

Browsed but didn't buy
Arc'teryx Beta jacket ($450, over her usual budget)
Knowledge Graph Connections
Rachel → prefers → organic/sustainable brands. Rachel → gift_buyer → November (annual pattern). Patagonia → similar_to → Arc'teryx (but lower price point fits better).

Memory builds with every visit

Each interaction adds to the picture. By the fourth visit, your assistant knows this customer better than most human sales associates would.

1
Visit 1

Browses hiking jackets, asks about waterproof options

Interested in hiking gearWants waterproofSize M mentioned in chat
2
Visit 2

Buys Patagonia Torrentshell, mentions earth tones preference

Patagonia fanEarth tones preferredBudget ~$250Ships to Portland, OR
3
Visit 3

Asks about running shoes, mentions she walks to work

Active lifestyleCommutes on footInterested in comfort + style
4
Visit 4

"I need a new jacket for hiking."

Assistant already knows: size M, earth tones, loves Patagonia fit, budget $200-300, prefers sustainable brands. Recommends perfectly on the first message.

What you can build

Personalized recommendations

Suggest products based on actual purchase history and stated preferences. Not collaborative filtering, not "people also bought." Actual memory of what this specific person likes.

One-click reorders

"That coffee I got in January." Your assistant finds it instantly, knows the quantity they usually order, and offers checkout in one step.

Smart cross-sells

The knowledge graph connects products to each other. Bought an espresso machine? Hebbrix knows to suggest a grinder that pairs with it, not just any grinder.

Browse-to-buy conversion

Remember what customers browsed but didn't buy. When they return, gently surface those items with new context: "The Arc'teryx jacket you looked at is now 20% off."

How Hebbrix features map to e-commerce

Each capability solves a specific shopping problem.

Multi-tenant collectionsEvery customer gets isolated memory. One store can serve thousands of users.
Knowledge graphMaps brand affinities, product relationships, and gift-buying patterns automatically.
3-tier memoryRecent browsing in short-term. Ongoing preferences in medium-term. Size and style in long-term.
Smart ingestion (infer:true)Feed in conversation transcripts. Hebbrix extracts the facts: size, brand, budget.
Sub-50ms searchFast enough for real-time chat. Customer doesn't wait while the assistant thinks.
Natural memory decaySeasonal trends fade naturally. Core preferences persist. No cleanup scripts.

Give your store AI that actually knows your customers

Start with the free tier. 1,000 memories, full hybrid search, automatic knowledge graphs. No credit card needed.

Start Building FreeIntegration Guide