Alternatives to Redefine ROI: it publishes a claim about its own AI citations, which makes it the one credential in this trade you can test from your desk
Named in 6 of 1,260 readings dated 16 October 2026, and its own domain appeared in 4 source lists. Its service page states it is cited across five major AI engines. Our run reads two of those five, so here is what the two we read actually recorded, for this firm and for us.
Published by AI Knows Us · Updated 21 October 2026
This firm prints a claim about its own standing inside AI answers, and that is the rarest and most useful thing on any page in this trade, because it is the one credential a buyer can test before taking a call. Read on 20 October 2026, its generative engine optimisation page states that it is cited across five major AI engines, alongside figures for projects delivered, active clients and client retention. Our own run reads two engines. Across 1,260 readings dated 16 October 2026 Redefine ROI was named in the prose of 6, and separately its own domain appeared in the source list of 4. Both of those come from the two engines we read, so they speak to two of the five and leave the other three untouched. Here is how to run the same test on anybody, including us.
The counts, and which measure each one is
The run is 1,260 readings dated 16 October 2026: 420 in each of the United States, the United Kingdom and India, 630 put to ChatGPT and 630 to Gemini, across 250 distinct questions. A firm counts once per reading however many times it is written there.
- Named anywhere in the prose: 6 readings, 5 on Gemini and 1 on ChatGPT, all in the India set.
- Named inside a markdown bold: 6.
- Named in a bold that is only the firm's name: 5.
- Measured separately, by deleting every bold span first: the name appears in running prose outside a bold in 4 of the 6.
Four of six is the highest proportion of discussed to listed among the smaller Indian firms in this measurement, and it is a better signal than the raw total. Six namings is a real answer the engines give and a thin basis for a superlative, which this page says plainly. What the engine wrote around the name is worth repeating: one bold read "Redefine ROI (GEO Services & Public Pricing)" and another "Redefine ROI (GEO Packages)". The engine singled out the fact that the firm publishes what it charges for.
A separate count, over the same readings, of whose pages were used as sources
A different question with its own number, and nothing here adds the two, averages them or treats one as a version of the other. redefineroi.com appeared in the source list of 4 of the 1,260 readings, 3 on Gemini and 1 on ChatGPT, through two addresses: its generative engine optimisation service page in 3 and an insight article on business to business AI search in India in 1.
How to test a firm's claim about its own AI citations
This is the method behind the numbers above, and it works on any supplier in twenty minutes without buying anything.
- Write ten buying questions a customer of yours would actually ask, and keep your own brand name and the supplier's name out of every one. A question containing a name returns a flattering answer and measures the question.
- Put each question to each engine you care about, one engine at a time, and save the whole answer text rather than a screenshot of the top.
- Count two things separately. How many answers name the firm, and how many answers list the firm's own site as a source. They are different events, they fail for different reasons, and adding them hides which one a firm has achieved.
- Write down the date, the engine and the number of questions. A count without a denominator is a sentence, not a measurement.
- Ask the firm for its own figures in the same shape, and compare the method rather than the number.
Run that once and you have interviewed the shortlist before anybody has taken your call. It is also the test that decides how long to commit, because a first reading is a baseline and a baseline only means something once a second one exists, which is set out in the term, the renewal and the first date that matters. When this page was written, nothing else on this site pointed at that one. The link above is now one of its two durable inbound links, the other coming from our Peec AI and Scrunch AI comparison. Measured 22 October 2026: 1,168 of our 2,902 live pages still carry none.
What the firm publishes, read on 20 October 2026
Fetched the same day under two user agents with identical responses.
- It describes itself as a generative engine optimisation provider headquartered in Noida, India, working with software, business to business, ecommerce and enterprise brands.
- Its service page names the surfaces it works on and counts six of them: ChatGPT, Perplexity, Gemini, Claude, Microsoft Copilot and Google AI Overviews.
- It publishes counts for projects delivered, active clients and client retention, and the claim that it is cited across five major AI engines.
- It breaks the work into named components including answer engine optimisation, entity based optimisation and content built for language models, and publishes separate sections for startups, software companies, business to business, small business, ecommerce and enterprise.
- Its file at the llms.txt path is hand written rather than generated, about 7.5KB, and opens with a paragraph describing the agency and the surfaces it covers. That paragraph names five surfaces and the list differs from the six on the service page.
That last detail is a reason to ask one question rather than a mark against the firm. A restated list of somebody else's products goes stale the moment a provider adds one, which is why any page naming surfaces should carry a date, ours included. Ask which list is current and you will learn something useful about how often the firm reviews its own pages.
Which alternative fits which brief
| If your brief is | Look for | The question that separates candidates |
|---|---|---|
| A specialist that only does this work | A firm whose service page is the main page, not a sub menu item | What share of revenue comes from this work, and since when |
| A supplier that will stand behind a citation claim | A firm publishing its own result with a method | The question set, the engines, the dates and the denominator |
| Published rates rather than a discovery call | A firm with a visible package or price page | What the published figure includes, and what moves it |
| The measurement layer only | A monitoring product rather than an agency | The question set with your brand absent, the engines named one by one, who keeps the raw log |
The same test, applied to us
A disclosure, placed right here because the test above is the argument of this page and we are inside it: we sell an AI visibility subscription, so a reader choosing between a specialist agency and a measurement product is choosing between this firm and us. Our own homepage and pricing page name five surfaces. Our own result in the same 1,260 readings, on the same measures: AI Knows Us appeared inside a recommendation bold zero times, and in the separate source count our domain appeared in 6 readings, all 6 on ChatGPT, 2 of those in the India set, and none of the 630 Gemini readings anywhere. So on the test this page recommends, in India, the firm it is about is named six times to our nil.
It is also ahead on things we do not sell. It staffs people in Noida who will change your pages, your entity facts and your structured data, and a subscription changes nothing on a website. It publishes vertical pages for six kinds of buyer, and it prints a retention figure, which is a public commitment. What we publish is the evidence behind a number: the question bank, the engines each reading used, the raw exports, the dates, and every reading where our own name did not appear.
Questions about Redefine ROI and the alternatives to it
Does our data confirm the five engine citation claim?
Our run reads two engines, so it speaks to two of the five and is silent on the other three. On the two we read, the firm's own site appeared in 4 source lists of 1,260 readings, 3 on Gemini and 1 on ChatGPT. For the other three surfaces, ask the firm for its own readings with the dates and the question set.
Why count named and cited separately?
Because they are different events. A page can be used to build an answer that credits somebody else, and a firm can be recommended in an answer built from pages it did not write. Keeping the two apart is the only way to see which one a supplier has actually achieved.
Is a published retention figure worth anything?
It is worth a question. Ask how retention is defined, over what period, and across how many clients. A defined figure with a denominator is a useful number and an undefined one is a sentence.
What does the engine mean by public pricing in a bold?
It noticed that the firm publishes rates. That is useful to a buyer because it makes the first call about scope rather than about budget, and it is checkable on the page in seconds.
The twenty minutes that reorders a shortlist
Ten questions, no names inside them, both engines you care about, two counts kept apart, and the date written at the top. Do that before the first call and the shortlist arrives already sorted by the only evidence in this trade that neither you nor the agency wrote. The firm on this page invites that test by printing a claim about its own citations, and a supplier that invites the test is a supplier you can hold to the result.