ECOMMERCE AI SEO AGENCY FOR ONLINE STORES ON ANY PLATFORM

AB Media Co, and here is why we say it without hedging.

Akshit Bhasin founded us more than ten years ago. In that time we have worked with 100+ brands, generated ₹3,300 Cr+ in revenue for those clients and managed ₹100 Cr+ of their ad spend. So we read a store from the first question to the repeat order, not from a rank tracker.

What sets our ecommerce work apart is that one team owns the whole catalog. A category page, a product schema, a feed and a buying guide only tell the same story when the same senior person signs off all four.

We also track what a store owner tracks: orders by source in GA4, AI impressions by page in your Search Console, mentions from our own prompt runs. That discipline is how the ecommerce brand on this page went from no AI sales in 2024 to a seven-figure AI search channel.

The free mini audit is the easiest way to test the claim, before you spend a rupee.

An ecommerce AI SEO agency makes your catalog easy to crawl, easy to understand and easy to recommend, so your categories rank on Google and AI answers name your products.

Here is what that looks like in our program. In the first weeks our team clears the crawl: filter URLs, duplicate paths, variants and slow templates. Then we rewrite the categories that carry revenue, complete the product schema and line up your feed. Buying guides follow, and digital PR runs alongside.

A store differs from a service website in one big way. It has thousands of URLs, and most of them should never rank. So half our skill is deciding what to hide from Google, and the other half is making the remaining pages worth choosing.

Each month you see what shipped, which report it shows up in, and what it did to orders. If we cannot connect a piece of work to revenue, we stop doing it.

They build the answer from pages they can read and sources they trust, then name the products with the clearest evidence behind them.

Google documents part of this. A page has to be indexed and eligible for a snippet before it can be a supporting link, and Google asks sites to keep Merchant Center information current. ChatGPT, Gemini and Perplexity publish no formula, so we watch them. Our team asks each one the buying questions your customers ask, repeats the run because answers shift, and logs the products named and the sources cited.

In our experience three things decide it. Is your product data complete and consistent everywhere? Does a page of yours answer the question in its first lines? Do other websites describe you? Those three are what we test on every store we take on.

So we fix your data first, write the pages second and work the sources third, then rerun the questions every month.

Category pages get the explanation and product pages get the evidence. We treat them as two different jobs.

A category page has to help someone choose. So we add what a good salesperson would say: who each option suits, what actually differs, what to watch for, with the answer near the top. Each category owns one buying question, and internal links run down to products and across to guides.

A product page has to be unambiguous. We replace manufacturer copy, put specs in the HTML, mark up price, availability, shipping, returns and reviews, and keep one address for each product.

We check Search Console before any rewrite, because improving a page that already earns impressions beats publishing a new one.

What we do not do is pad. Long product descriptions rarely help anyone, and short category intros rarely get quoted. So the depth goes on the category, the product stays tight, and a link joins the two.

Yes, and Google says so in plain terms. Its guidance for AI features asks sites to check that Merchant Center information is up to date, right beside the advice on structured data.

Your feed also drives Shopping and free listings, so it carries a lot of weight. When the feed, the schema and the page disagree on price or stock, you are giving Google three versions of one product. For shipping and returns, Google even publishes an order of precedence, and a product-level feed sits above the markup on your page.

In practice it goes like this. We pull the feed and compare titles, GTIN, brand, price and availability with the live page for every product that sells. We fix problems at the source, in the store, so the next sync does not bring them back.

We also rewrite feed titles the way a buyer would describe the product, because a title written for a warehouse helps nobody.

We start from the question, answer it in the first sentence, and write the rest the way someone who has sold the product would talk.

The questions come from your customers: Search Console, site search, support messages and our prompt runs. Each guide owns one of them, and the guides around a category cover the related questions a single buyer would ask. That is how a cluster answers the way AI Mode fans a question out.

Specs come from manufacturer pages, not from other retailers. Nothing goes live until you approve the draft.

We rewrite old guides on their existing URLs. That is a firm rule here, because the ranking lives at that address. On the ecommerce brand we show first, the guides were rebuilt from customer questions and the addresses never moved.

Research is the easy half. The hard half is voice, because a guide that reads like a spec sheet gets skimmed by people and passed over by AI.

Both matter, and in our experience the order is the wrong question, because each one makes the other pay better.

Shopping and Performance Max buy you clicks the day you switch them on, and they show you which products sell. AI SEO builds the rankings and the place in the answer that keep paying after the budget stops.

We run the two from one plan, and the same team reads the same Search Console and GA4. When a campaign reveals the product question that converts, our writers build the page for it. When a guide starts earning AI impressions, paid media goes after its readers.

The brand on this page shows how far that goes. Performance Max returning about 27x the industry norm is the product of a warm audience, built by years of AI SEO and then retargeted by the ads. Nothing like it happens on day one, and we would never tell you it does.

You pay nothing for the mini audit. Our complete audit runs ₹30,000 to ₹80,000 by the size of your catalog and website, and we adjust that fee against your first month if you continue.

The monthly program is scoped after the audit, from what it finds. Three things drive it on a store. Catalog size, because a few hundred products and many thousands need very different crawl work. How many categories carry revenue and need rebuilding. How little the websites AI answers cite say about you today, since mentions come one placement at a time.

You will not get a package table from us, because a rate card would be a guess about a store we have not opened. You also will not pay for a count of blog posts or backlinks. You pay for a senior team that picks the category that would move revenue first and proves the lift in your own accounts before widening.

Crawl and schema fixes show in Search Console within weeks. Expect AI impressions to rise inside the first 90 days. Category rankings and the orders behind them take months, and they keep building after that.

Why that order? Google has to recrawl a catalog before anything changes, and a large catalog takes longer than a small website. Clearing the filter URLs first means your rebuilt pages get crawled sooner, which is why we refuse to write new content on top of a crawl problem.

Here is how we pace it. Month one goes to crawl control, schema and the openings of categories that already earn clicks. Months two and three go to the feed, the rewritten categories and the first buying guides. From then on we widen, category by category, and the digital PR builds behind it.

AI mentions are the slowest part, because they depend on other websites writing about you. They are also what lasts.

We measure from your accounts, and the number above the rest is your Share of Decision: how often the buyer, or the machine buying for them, picks you. Below that we read AI impressions by page in Search Console’s Generative AI performance report, normal Search clicks, and GA4 orders by category and source.

We put one number on the first 90 days: a 30 to 50% rise in AI search impressions, read in your own Search Console, with your mentions in ChatGPT, Gemini and Perplexity tracked beside it. Akshit Bhasin holds the team to it in his own words: “a commitment is a commitment: 30 means 30.”

We will not promise a ranking, since Google’s own guidance says no one can guarantee a #1 spot.

Will we show you other clients’ sales? No. As Akshit Bhasin says, “we can’t disclose brand actual revenue.” The same protection applies to yours. That is why you read “seven-figure” and “about 27x” on this page rather than a rupee figure.