AI for ecommerce
Your catalog is your storefront, and AI is now shopping it.
ChatGPT, Perplexity, and Gemini are rerouting how people discover products. If your catalog data is thin, unstructured, or inconsistent, you disappear from the answers layer entirely, before a shopper ever sees your site.
AI referral conversion rate
weeks to first value
conversion lift from enrichment
The forces reshaping ecommerce right now
Discovery, data quality, support costs, and competitive visibility are all shifting at once, and the gap between prepared and unprepared stores widens fast.
AI is rerouting discovery
AI-driven referral traffic grew 12x in seven months. Shoppers increasingly ask an AI for recommendations instead of searching Google, and thin catalog data means your products never surface.
Poor product data leaks revenue
49% of shoppers abandon when product information is incomplete or inconsistent. Every missing attribute, image, or spec is a silent conversion loss you never see in analytics.
Support cost scales with orders
Every new SKU and every holiday spike adds more tickets. Most are the same handful of questions, but each one still takes a person to answer and a minute to resolve.
Blind to competitor pricing
Competitor price changes and promotions surface through manual checks or customer complaints, days after the window to respond has already closed.
What we hear
Your product catalog is a living commercial asset. When it lacks structured, channel-ready data, it quietly costs you revenue every single day.
We add SKUs faster than we can write good descriptions for them.
Half our support tickets are the same five questions.
Six ways to make your catalog work harder
Each targets a real drain on conversion, visibility, or margin, plotted by the impact it creates against the effort to stand it up. Upper-left is the most return for the least lift.
Generative engine optimization (GEO)
AI assistants recommend products by reading structured data. If yours is thin, you are invisible in the answers layer.
Where a person stays. AI ranking factors evolve quickly; your team reviews enrichment suggestions before they go live.
Time-to-value estimates draw on published deployments and are indicative only. Every organization's data, systems, and starting point are different.
What would your team do with half the catalog grind gone?
The biggest cuts land on the high-volume, repetitive content and support tasks that scale with your SKU count, not your merchandising expertise.
Catalog updates
Writing, enriching, and formatting product data
Support deflection
Answering routine order and product questions
Content drafting
Descriptions, comparisons, and marketing copy
Listing creation
New SKU setup from intake to publish-ready
Most teams channel those hours into merchandising strategy, vendor relationships, and the creative work that actually differentiates the store.
What tends to move
Conversion lift from enrichment
Complete, structured product data closes the gap between browsing and buying.
source ↗Fewer repeat support inquiries
When the first answer resolves the question, customers stop coming back with the same one.
AI referral conversion rate
Traffic from AI assistants converts at 14.2% compared to 2.8% from traditional organic search.
source ↗Higher conversion from ChatGPT referrals
Shoppers arriving through AI recommendations show stronger purchase intent than those from conventional channels.
Ranges from published deployments and industry studies; your starting point sets where you land.
What would your team do with a catalog that sells itself?
Tell us how your store runs today and we'll map where AI fits your operation, and where it honestly doesn't, for your setup.