SEO and GEO for a store make thousands of product pages findable and quotable by search engines and AI assistants, without filler text or URLs lost in a platform change.
KINTO’s store cases show what SEO and GEO work consists of.
Content SEO for a service company is mostly about writing. For a store with thousands of products, the work is mostly about data, URLs and weight. The page that matters is a product page, and there are thousands of them. If they all carry template text, a search engine has little to quote and an AI assistant has nothing worth citing.
This page answers a narrower question than the general SEO and GEO page: how does KINTO handle a large catalog, and what happens to search presence when the store changes platform?
None of these cases reports later traffic, sales or conversion as evidence. The measured results are page weight, response time, catalog size and data quality. A store should expect a faster, cleaner and better described catalog, not a promised ranking.
The general approach is on the SEO and GEO page, and the store build on headless e-commerce.
Headless e-commerce
By making the ERP, not the web catalog, the source of truth, KINTO got an auto parts distributor’s WhatsApp agent to offer ≈26% more real products and stop offering hundreds it did not have.
Headless e-commerce
KINTO migrated a networking distributor’s catalog to a headless store that weighs a fifth of what it did and, with no public prices, turns every product page into a WhatsApp quote request.
Migrations & rescue
A store with thousands of products kept going down under a swarm of bots crawling filters; KINTO stabilized it in days and migrated it to a headless shop whose listing weighs ≈1% of what it did.
WhatsApp & voice agents
KINTO fixed the weak link of an e-commerce brand that sells over WhatsApp with a multi-model audited agent and order-by-order reconciliation; the owner reported a month with channel sales ≈30% above their history.
KINTO starts from the data. For a specialty retailer, the problem included thousands of products with poor data. KINTO enriched brand and type programmatically so that filters finally filter, and left technical specs in review mode, never blindly automatic, in the networking distributor's catalog.
It can if URLs are treated as an asset. For the networking distributor, KINTO migrated from WooCommerce programmatically with 301 redirects from the old URLs and brand collections. The specialty retailer migrated over a thousand products from a public catalog of several thousand, enriching brand and type.
It matters for mobile speed, and it is measurable. The networking store's listing dropped about 80% in weight, from about 0.5 MB to under 0.1 MB. The specialty retailer's went from several MB to about 0.1 MB, and its filter URLs responded in about 0.5 seconds instead of about 3.
A page that states the answer in a form a machine can quote: structured data for products, FAQs and listings, and an llms.txt file. KINTO added these to the networking distributor's store, with automatic rebuild when the catalog changes, and product SEO with batch alt text to the auto parts store.
Tell KINTO what is going on. The first conversation is to understand it, not to sell.
