The Personalization Gap in E-Commerce
Most online stores treat every visitor the same: the same homepage, the same product grid, the same promotions. But your customers are anything but the same. A first-time buyer exploring casually has completely different needs than a loyal customer ready to reorder.
This one-size-fits-all approach is costing you revenue. Studies consistently show that 80% of consumers are more likely to purchase from a brand that provides personalized experiences. Yet most e-commerce platforms still rely on static merchandising rules set by a human team — rules that can't scale, can't adapt in real-time, and can't reflect the unique journey of each individual shopper.
Kainic was built to close that gap.
What Kainic Actually Does
Kainic is an AI-powered personalization engine that sits on top of your existing e-commerce stack. It continuously analyzes user behavior — browsing history, search queries, purchase patterns, session context, and more — and uses that data to deliver real-time, highly relevant product recommendations across every touchpoint of the shopping experience.
Real-Time Recommendation Widgets
Kainic powers multiple recommendation surfaces throughout your store:
- You May Also Like — shown on product detail pages, surfacing items similar to what the user is viewing
- Frequently Bought Together — on cart and checkout pages, increasing average order value
- Trending Now — homepage and category pages, showcasing what's popular in real time
- Recently Viewed — helping users pick up where they left off
- Personalized Homepage — a dynamic layout that reorders itself based on each visitor's profile
Sub-50ms Inference Latency
Speed matters. Kainic's recommendation engine is engineered for real-time delivery — returning personalized results in under 50 milliseconds. That's fast enough to be invisible to the user, but powerful enough to make every page load feel curated.
Hybrid AI Models
Kainic combines multiple recommendation techniques to ensure quality across your entire catalog — including new products with little interaction data:
- Collaborative Filtering — learns from the collective behavior of users with similar tastes
- Content-Based Filtering — recommends based on product attributes and descriptions
- Hybrid Models — blends both approaches for the highest accuracy
- Auto Model Selection — Kainic evaluates which model performs best for your catalog and traffic patterns, automatically
5-Minute Integration — No Engineering Team Required
One of Kainic's core design principles is zero-friction onboarding. Whether you're on Shopify, WooCommerce, Magento, or a custom-built platform, getting Kainic live doesn't require months of engineering work.
- Install the Kainic app or plugin from your platform's marketplace
- Connect your product catalog — Kainic automatically ingests and indexes your products
- Drop recommendation widgets onto your pages using a visual editor — no code required
- Kainic starts learning from Day 1 and improving recommendations with every interaction
For developers who want more control, Kainic also exposes a full REST API and JavaScript SDK for custom integrations and advanced use cases.
From Day One to Compounding Returns
Unlike traditional A/B testing tools or rule-based merchandising, Kainic doesn't require you to manually define what "good" looks like. The AI learns your catalog, your customers, and your conversion patterns — and continuously optimizes.
The result is a compounding effect: the longer Kainic runs, the smarter it gets, and the more revenue it generates. Stores typically see measurable uplift within the first week, with performance improving steadily over the following months as the model accumulates more behavioral signal.
Built for Scale
Whether you're processing 1,000 orders a month or 1 million, Kainic scales with your business. The infrastructure is built on Google Cloud, designed for high availability and global performance. You don't need to worry about traffic spikes or catalog size — Kainic handles it.