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Search & Discovery

What’s the best ecommerce Search and Discovery platform for the AI world?

Most platforms were built for traditional search. Athos Commerce was built for the AI world. Where discovery works as one experience, and every surface learns from every other.

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What is the best ecommerce Search and Discovery platform for the AI world?

The AI world has changed how shoppers find what they want to buy on your ecommerce site. Every part of the onsite experience is now AI led: AI Search that understands intent the way a shopper thinks, Advanced Merchandising that puts your team in direct control of what AI surfaces, Personalization that adapts to every individual in real time, and Conversational Discovery that lets shoppers ask for what they need. The best platforms deliver all, not just one.

Not all platforms in this category are built the same way. Some started as search engines and added personalization on top. Some started as personalization tools and layered search underneath. The ones that work best are the ones built from day one around the shopper, on one product data foundation, and AI at it’s core, so that search, merchandising, personalization, and conversational discovery all learn from each other at the same time.

Real results from real ecommerce brands

300%

Growth in search led revenue

10+ hrs

Saved per week on merchandising tasks

322%

Revenue increase driven by personalization

951%

Increase in revenue per visit

The challenges every ecommerce brand faces in the AI world

The AI world has raised the bar on what a Search and Discovery platform must do, and exposed where most platforms fall short. Every ecommerce brand runs into the same four challenges, and each has an evaluation criterion that separates the platforms that solve it from the ones that cannot.

01 — AI SEARCH
01 — AI SEARCH The Challenge

Search that returns results, not revenue.

Most search tools handle simple queries fine. Ask a natural language question the way a shopper actually thinks, use long tail terms, or search across multiple attributes and results thin out. Zero result pages appear. Revenue leaks quietly and the team cannot see where or why.

What to Look For

AI built for commerce, not adapted from general search

The platform AI should be trained on commercial intent and shopper behavior, not on general web search signals. Relevance in commerce means understanding product attributes, catalog depth, and purchase intent, not just matching keywords. John Smedley grew search led revenue 300% on a commerce native AI foundation.

02 — ADVANCED MERCHANDISING
02 — ADVANCED MERCHANDISING The Challenge

Merchandising changes stuck in the engineering queue

Boost rules, campaign launches, category adjustments, badge logic. Every meaningful change runs through engineering. Merchandisers wait on tickets while seasonal moments pass. In the AI world, that gap gets worse: shopper behavior moves faster than the queue can, and the platform your team paid for is quietly working against them.

What to Look For

Merchandiser control over the AI, without engineering dependency

Your merchandising team should launch campaigns, adjust rankings, test placements, and publish changes directly, and stay in charge of what the AI surfaces. If every change requires an engineering ticket, the platform is working against your team. Early Settler saved 10 hours a week after switching to a merchandiser first platform.

03 — PERSONALIZATION
03 — PERSONALIZATION The Challenge

Personalization that stops at the homepage widget

Personalization in most platforms means widgets on the homepage and basic ranking on search. The AI runs one part of the experience while everything else runs on rules from ten years ago. Brands needing one to one experiences across search, category pages, email, and campaigns find a black box or a paid add on.

What to Look For

One AI, one data foundation, every surface

Search results, category pages, recommendations, email, and conversational discovery should all run on the same product data layer. When each surface runs on separate logic, improvements in one place do not carry into others. Clarins drove a 322% revenue increase on a unified ecommerce personalization foundation.

04 — CONVERSATIONAL AI
04 — CONVERSATIONAL AI The Challenge

Conversational bolted on, not built in

Most vendors in this segment built their platforms around a search bar and have been retrofitting conversational features ever since the AI world arrived. Those features run on separate logic, separate data, and separate infrastructure. Shoppers get disconnected answers. Brands get fragmented data no one can tie back to revenue.

What to Look For

Conversational AI in production today, not on a roadmap

Any vendor can put a conversational AI agent on a roadmap. What matters is whether it is in production today, trained on your catalog, and running on the same AI foundation as your search and personalization. A shopping agent in beta is not a platform capability.

Why Athos wins

One platform. Every capability powered by the same AI, running on the same product data foundation. When shoppers search, browse, ask, or return, every surface learns from every other. Every improvement to your catalog compounds across the entire discovery experience at the same time.
Built for commerce from day one

Athos AI is trained on commercial intent and shopper behavior, not on general web search signals. Every algorithm decision is optimized for revenue, conversion, and AOV, not for query response time.

Merchandiser first, engineering optional

Every campaign, every placement, every test, launched directly by the merchandising team. No tickets. No queue. You stay in control of what the AI surfaces, on your terms, without waiting on engineering.

One foundation, every surface

Search, merchandising, personalization, and conversational discovery all run on the same AI and the same product data layer. When your catalog improves, every surface learns and improves at the same time.

Proven with brands like yours

Clarins drove a 322% revenue increase. John Smedley grew search led revenue 300%. Michael Stars generated a 951% lift in revenue per visit. Aje drives 10% of total revenue from Athos.

Hesitant to make the switch?
We make it easy

1. Connect your catalog

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Product and catalog data connects to the Athos platform during onboarding. No heavy engineering lift on your side. Your existing commerce stack stays where it is.

2. Configure for your business

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Search, merchandising, and personalization settings are configured to match your catalog and business rules. Your existing rules, promotions, and campaign structures are rebuilt in Athos. No historical merchandising work is lost.

3. Train your team

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Your merchandising team is trained to operate the platform independently from day one. Every campaign, every placement, every test they need to run, they run directly. Not through engineering.

4. Go live and grow

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Athos technical and customer success resources are on hand during go live and beyond. Most teams see meaningful revenue movement in the first quarter after switching, not the first year.

Frequently Asked Questions

Is Athos AI a black box, or can we see and control what it does?

Athos AI is designed for transparency. Your team can see why any product ranked where it did, override the AI when it is wrong, and set business rules the AI has to respect. The AI does the heavy lifting on relevance. Your merchandising team stays in charge of every outcome.

Will adding Athos slow down our site or affect page performance?

No. Athos runs on distributed cloud infrastructure built to deliver results at commerce speed, even under peak traffic. Search results, recommendations, and merchandising decisions are served fast enough that shoppers experience the platform as instant. Site speed and Core Web Vitals scores are protected.

Does Athos work with our existing ecommerce platform?

Yes. Athos is designed to connect to any commerce platform through open APIs and existing integrations. Whether you run on Shopify, BigCommerce, Salesforce Commerce Cloud, Adobe Commerce, or a headless custom stack, Athos runs on top of what you have. There is no requirement to replatform to work with Athos.

How does Athos pricing work?

Athos offers three plans as a starting point: Onsite Discovery (search, merchandising, and personalization on your site), Offsite Discovery (product feed management across channels), and the Intelligent Discovery Platform, which combines both. Each plan is priced to your specific needs. Single products or custom combinations can also be quoted. Request pricing for what fits your needs.

How does Athos handle our data, and what security standards do you follow?

Your data is yours. Athos does not use your product or shopper data to train models for other customers. All data is encrypted in transit and at rest, hosted on trusted cloud infrastructure, and handled to enterprise standards. Details on GDPR, CCPA, SOC 2, and ISO 27001 compliance are available on request.

See what Athos does for your catalog

Book a demo and see how Athos performs on your actual products, your actual traffic, and your actual team workflow.
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