210+ reviews on the Shopify App Store

Boost AI Search Alternative

Why Shopify Brands Choose Kimonix Over Boost

Great search and filters help shoppers find products. Kimonix goes further, data-driven merchandising that decides which products lead, a conversational AI shopping assistant, and 1:1 personalization, unified in one Shopify platform.

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Kimonix vs. Boost: a side-by-side comparison

	Kimonix Our pick	Boost
Core focus	All-in-one product discovery Merchandising, personalization & AI search in one platform	Search & filter app AI search + advanced filtering, with merchandising layered on
Collection merchandising	Data-driven & automated Sorts on margin, inventory, variant-level stock, returns & behavior, A/B tested	Rule-based controls Pin, boost, reorder; automated placement rules
Faceted filtering	Standard collection filters Filtering isn't Kimonix's primary focus	Advanced filter trees Custom filters per collection, tag, metafield, variant
On-site search	Conversational AI assistant Add to cart in chat, revenue attribution, 50+ languages	AI semantic search Typo tolerance, fast, contextual results
Product recommendations	Data-driven & 1:1 Margin & inventory in the logic, across the store & email	Behavioral + bundling Related products & AI predictive bundles
Collection A/B testing	Built in Test sorting strategies and keep what sells	Not a stated focus
Personalization	1:1 across the whole journey Collections, recommendations & search from one engine	Behavior-based Within search & recommendations
Pricing	Published, order-based + 14-day trial Scales with your average monthly orders	Published, GMV-based + 21-day trial Scales with your monthly GMV

Kimonix Our pick

Boost

Core focus

All-in-one product discovery

Merchandising, personalization & AI search in one platform

Search & filter app

AI search + advanced filtering, with merchandising layered on

Collection merchandising

Data-driven & automated

Sorts on margin, inventory, variant-level stock, returns & behavior, A/B tested

Rule-based controls

Pin, boost, reorder; automated placement rules

Faceted filtering

Standard collection filters

Filtering isn't Kimonix's primary focus

Advanced filter trees

Custom filters per collection, tag, metafield, variant

On-site search

Conversational AI assistant

Add to cart in chat, revenue attribution, 50+ languages

AI semantic search

Typo tolerance, fast, contextual results

Product recommendations

Data-driven & 1:1

Margin & inventory in the logic, across the store & email

Behavioral + bundling

Related products & AI predictive bundles

Collection A/B testing

Built in

Test sorting strategies and keep what sells

Not a stated focus

Personalization

1:1 across the whole journey

Collections, recommendations & search from one engine

Behavior-based

Within search & recommendations

Pricing

Published, order-based + 14-day trial

Scales with your average monthly orders

Published, GMV-based + 21-day trial

Scales with your monthly GMV

Real results from Shopify brands

178%

Revenue Increase

44%

Conversion Rate Increase

+41%

Higher Revenue

Case studies

+22%

Conversion Rate Increase

Brand or Data? How Goldbergh Increased Conversion by 22% Running an A/B Test

+22% Conversion Rate Increase+17.6% Revenue Growth+15% Sales Quantity Increase

Read the Goldbergh case study

10.7x

ROI on App Cost

How Bally Turned Smarter Product Recommendations into a 10.7X ROI

10.7x ROI on App Cost

Read the Bally case study

Why Shopify brands choose Kimonix over Boost

Boost is a well-liked Shopify search-and-filter app, and its advanced filtering is genuinely strong. Kimonix solves a different part of the problem: not just helping shoppers find products, but deciding which products lead, and doing it on your economics. Here's where that depth shows:

Merchandising that decides what leads, automatically
+
Boost merchandises through rules you set by hand (pin, boost, reorder). Kimonix automates collection sorting on margin, inventory, variant-level stock, returns, reviews and real-time behavior, keeps it current as things change, and lets you validate it with built-in A/B testing.
Search that sells, not only filters
+
Alongside filtering, Kimonix adds a conversational AI shopping assistant: shoppers describe what they want, add to cart inside the chat, and you see the revenue each conversation drives, in 50+ languages, with no extra setup.
Three pillars sharing one data-driven engine
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Merchandising, recommendations, and search all run on the same logic, so your collections, product suggestions, and Klaviyo emails pull in one direction instead of being tuned separately.
Recommendations that balance relevance and business goals
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Kimonix's product recommendations weigh margin and inventory alongside behavior, so the products you suggest are good for the shopper and for your bottom line, on the storefront and in email.
Keep the filtering you love
+
Kimonix doesn't try to out-filter a dedicated filter app, and it doesn't have to. Many brands run it alongside their existing search-and-filter setup, letting that own faceted filtering while Kimonix owns what surfaces, the recommendations, and the shopping assistant.
Personalization across every surface
+
The collection order, the recommendations, and the search results a shopper sees all adapt to them in real time from a single engine, consistent 1:1 personalization across the whole discovery journey.

Frequently asked questions

Is Kimonix a good alternative to Boost AI Search & Discovery?

It depends on what you need most. Both are Shopify-native and no-code. If your priority is advanced faceted filtering, Boost is strong there and Kimonix doesn't try to replace it. If you want merchandising that automatically decides which products lead, on margin, inventory, and demand, plus a conversational AI shopping assistant and data-driven recommendations in one platform, Kimonix is built for that, and many brands run the two together.

How is Kimonix different from Boost?

Boost is a search-and-filter app with merchandising layered on; its strengths are AI semantic search and advanced filter trees. Kimonix is a product discovery platform whose core is data-driven merchandising, it decides what surfaces first, unified with recommendations and a conversational AI shopping assistant. Put simply: Boost helps shoppers find products; Kimonix decides which products to put in front of them, and why.

Does Kimonix have product filters like Boost?

Kimonix supports standard collection filtering, but deep faceted filtering, custom filter trees per collection and extensive tag, metafield, and variant filters, is Boost's specialty, and Boost does it well. Kimonix's focus is what leads on the page (data-driven merchandising) and a conversational shopping assistant, not the filter UI. If advanced filtering is essential, keep it, Kimonix runs happily alongside it.

Can Kimonix replace Boost for merchandising and search?

For merchandising, recommendations, and a conversational search experience, yes, Kimonix covers those as a unified, data-driven platform, with automated collection sorting and an AI shopping assistant that adds to cart in chat. For advanced faceted filtering specifically, Kimonix isn't a like-for-like replacement, which is why some brands keep a dedicated filter app and use Kimonix for everything around it.

Can I use Kimonix alongside Boost?

Yes. Because Kimonix focuses on merchandising, recommendations, and a conversational shopping assistant rather than the filter UI, it coexists cleanly with a search-and-filter app. A common setup is to let Boost own faceted filtering while Kimonix decides what leads in your collections, powers data-driven recommendations, and handles conversational search.