Every sale now starts with a question.
Shoppers no longer only search; they also ask AI what to buy. Seyaq measures whether your store appears in those answers and shows what you can improve.
Product discovery has changed three times in twenty years
Each era rewrote the rules for who gets found. The stores that noticed early won each time.
The search era
Shoppers typed keywords and browsed ten blue links. Page one was everything, and a whole industry — SEO — grew around getting there.
The social platform era
Discovery moved into social feeds and paid placements. Merchants bought attention ad by ad, and margins quietly paid for it.
The answer era
Shoppers describe what they need and an assistant may reply with a short list, including product details and links to stores.
Ten blue links became one answer.
There is no page two of an AI answer. A store is either in it, or it isn't.
Your store speaks to people. AI systems read something else.
A storefront can look beautiful to people while remaining difficult for AI to read. Most merchants never see this gap because their dashboards do not measure it.
Why SEO is no longer enough on its own
SEO taught pages how to rank in search. Visibility in AI answers is a different test:
Ninety seconds, one winner, and no trace
This is a new moment of competition for stores. Without measurement, merchants cannot easily see when their store is missing from the answer.
The assistant weighs what it can verify — live prices, confirmed stock, delivery promises, answers in the shopper's language.
One store is named, with a live price and a link. The shortlist has one name on it.
For every other store, that evening looks like nothing happened. No visit and no abandoned cart—the loss produces no data.
You cannot fix a loss you never see. That sentence is why Seyaq exists.
The tool that makes this moment visible
Seyaq replays that moment on demand — for your store, against your competitors — and turns what it finds into a plan.
Real shopping questions in English and Arabic, tested with a configurable AI engine and web search.
The actual answers: who got recommended, who was skipped, and why.
The catalog and store factors costing you visibility, ranked by priority and impact.
Recommendation outcomes tracked over time — improvement measured, not assumed.
What visibility and recommendation audits repeatedly reveal
The same patterns recur across visibility measurements. They are usually ordinary information gaps that may give another store a better chance of being recommended.
Arabic shoppers, English evidence
A store sells in Arabic, but everything machine-readable about it exists only in English — or nowhere. Asked in Arabic, assistants recommend the store they can verify in Arabic.
“In stock” that only humans can see
If availability is visible to shoppers but missing from clear, structured data, AI may not be able to verify it. That can reduce the chance of your product being recommended over another store's.
One product, two identities
The Arabic storefront and the English catalog name the same product differently. To an assistant, that is two weak candidates instead of one strong one — the evidence splits.
Outdated information may affect AI recommendations
Without a current source to consult, AI may rely on old data, recommend products that are no longer available, and miss newer or bestselling products.
The winner is not always the biggest store
In many cases, AI recommends the store whose data it can understand, verify, and cite most easily—not necessarily the largest store. That is something you can improve.
Findings like these appear in many first audits and often surprise store owners.
It started with an uncomfortable experiment
We tested the questions shoppers ask every day with an AI engine powered by web search—a gift under 60 US dollars, an abaya delivered by Thursday, a coffee grinder that ships to Riyadh—and watched who was recommended. Stores we knew and loved, with great products and loyal customers, barely appeared. Not because they were not good, but because AI did not understand enough about them.
The merchants behind those stores were doing everything right by the old rules. They paid for SEO. They ran the ads. Their storefronts looked beautiful. But a growing share of buying decisions was being made inside a single generated answer, and nothing in their toolkit could see that conversation — let alone win it. A loss that never shows up in a report is the easiest loss to ignore.
Help your store become one of the options AI recommends to shoppers.
So we built the tool we could not find: one that tests real shopping questions in English and Arabic with an approved AI engine and returns answers and evidence—who was recommended, why competitors appeared, which product and store factors may limit visibility, and whether measured results changed after improvements. No grand promises or guaranteed rankings: measure, improve, verify.
We are building Seyaq for every store, in the languages of its customers, products, and markets. Our goal is clear: the customer, purchase, and checkout stay on your store. We measure your opportunities to appear in AI answers and recommendations, then show what to improve when your store is missing.
Four principles we won't trade away
Evidence over promises
No one can guarantee that AI will recommend a specific store. We show actual results, the competitors that appear, and measurable change over time instead of selling promises.
Open standards, not lock-in
We use open technologies and standards such as MCP, JSON-LD, and llms.txt so your store presence and data remain under your control, without dependence on a closed system.
The sale stays yours
AI helps shoppers discover and compare options, while purchases and payment happen on your store. Seyaq does not handle payment data.
The shopper's language comes first
Shoppers ask in Arabic, in English, and in both at once. Every question we track and every report we write works natively in both languages.
AI is playing a larger role in purchase decisions
AI is gradually moving from answering questions to comparing options, making recommendations, and taking more steps in the buying journey. That makes a clear, readable store presence increasingly important.
Assistants recommend
Shoppers ask, and AI presents a limited set of options. Appearing in these recommendations is becoming like reaching the first page—and many stores are not there yet.
Assistants compare
During the same conversation, AI can compare prices, availability, and delivery across stores. Stores with clear, readable data enter the comparison; others may not appear.
Assistants buy
The experience is moving from recommendations toward purchases within the same conversation, with agents able to complete more of the buying process for users.
Assistants become the regular customer
Shopping agents monitor prices, reorder products, and compare alternatives for customers. Over time, some customers may be software making purchase decisions for real people.
Seyaq's mission stays the same: keep your store visible, understood, and among the options—however search and shopping evolve.
Your customers are asking AI right now
Find out what AI assistants say when shoppers ask about products like yours — free, in minutes, with real evidence.