AI competitor benchmarking for Indian D2C brands

What benchmarking means when an AI picks the shortlist

In search you have a position, and everyone below you still exists on the page. In an AI answer there are three or four brands and then nothing. Being eleventh and being absent look identical to the shopper, so the useful measure is not your rank in a long list, it is what share of all the naming went to you.

That is share of voice: across every buyer question in a scan, count how often each brand was named, and express yours as a proportion. It answers the question a founder actually asks, which is not "where do I rank" but "when an AI recommends something in my category, how often is it me".

The four things it should tell you

Any tool can hand you a number. These are the four cuts that turn it into something you can act on, and they are worth testing on a free scan of any tool you are considering, ours included.

• Who your rivals actually are, discovered rather than declared. The brands you compete with in AI answers are frequently not the ones on your competitor slide. If a tool asks you to type in your competitors, it can only ever confirm what you already believed.
• Share of voice against every one of them, not just the three you asked about. Our own last scan named 87 distinct brands in one category. A tool tracking five of them would have reported a tidy picture of a field it could not see.
• The split by engine, because they disagree sharply. In our own scan the leader on ChatGPT and Claude was one brand and the leader on Google AI Overview was another, and our own position moved by 36 places between engines.
• The split by language and by city, which is where D2C differs most. A shopper asking in Hinglish gets a different set of brands than one asking in English, and answers to local shopping questions differ between metros. AnswerTrace slices share of voice by engine, by market, by city and by segment for this reason.

A worked example: our own scan, including the bad parts

On 25 August 2026 we ran AnswerTrace against AnswerTrace. The engines named 87 distinct brands in our category. Peec AI led with 10.1% of all naming. We came 40th, with 1 mention and 0.4% share.

Split by engine it was ChatGPT #16, Google AI Overview #33, Claude #52. The same brand, the same questions, the same week, and a 36-place spread depending on which engine a buyer happened to ask.

We publish that because a benchmarking page illustrated with a flattering example teaches nothing. This is what the output looks like when the answer is bad, which is the case you most need it to handle. It is also the number that started the work: everything published on this site in the last month exists because of that 0.4%.

Why the rival list has to be discovered

The single most common way benchmarking goes wrong is starting from a list. You name five competitors, the tool tracks those five, and the report tells you how you compare against exactly the brands you already had in mind.

AI answers do not work that way. An engine answering a shopper reaches for whoever its sources talk about, which regularly includes brands you have never heard of, international players you did not think competed with you, and marketplaces rather than brands. AnswerTrace takes the rival set from the answers themselves, so the report can tell you something you did not already know.

The cost of that approach is a longer and messier list, including names that are not really competitors. That is the honest trade: a short clean list you supplied, or a long real one you did not.

On "used by Indian D2C brands"

We do not publish customer names, so we cannot answer that part of the question and will not imply otherwise. Every tool in this category can write "trusted by leading brands" and most do; it is unverifiable either way and you should discount it wherever you read it, here included.

What you can check instead costs nothing. The Free plan runs a real scan on ChatGPT with no credit card and returns the brands the engine actually named in your category. If the rival list it comes back with surprises you, the measurement is doing something. If it returns exactly the five brands you would have typed in, it is not worth paying for.

What the benchmarking costs

Share of voice against every named brand is in the report on every paid plan and in the free scan. Competitor Intelligence, which tracks how those positions move between scans, starts at Growth at ₹4,999 a month, before 18% GST that GST-registered businesses claim back.

The language and city slices that matter most to a D2C brand depend on your plan covering those languages and cities: 12 languages of which 10 are Indian, and 101 cities across 7 countries, 42 of them in India.

When another tool fits better

This page deliberately does not rank tools on benchmarking, because the competitor facts we hold come from pricing pages and pricing pages say almost nothing about benchmarking features. Ranking rivals on a capability we never sourced would be a claim we cannot stand behind. The ranked comparison, built on facts we do hold, is on the alternatives page.

If you sell across more countries than the 8 AnswerTrace measures, benchmark coverage follows country coverage and you should read that page first. And the general limits apply here as everywhere: It covers eight countries live today where Otterly.AI covers 50+, and adds more on request with a buyer-question bank and search geography of their own rather than by relabelling an existing one. It does not carry Microsoft Copilot at all, and engines unlock by tier rather than all arriving on the cheapest plan.

The limit that applies to everyone

Benchmarking tells you where you stand. It does not move you, and no tool including this one can make an AI engine name you. Engines decide what to say from sources nobody controls and change their answers between runs. What a measurement is good for is knowing whether the thing you published last month changed anything, which is a smaller promise and a real one.