Buyers increasingly ask chatbots for recommendations instead of scrolling search results. A young company founded by Vaibhav Palhade and Pratyush Shrivastava is building tools to measure a shortlist most businesses cannot see.
Ask an AI assistant to recommend an accounting firm in Dubai, a payroll tool for a small business in Ohio, or a manufacturer of cotton textiles in western India, and it will usually do something a search engine never did. It will choose.
The answer arrives as a short paragraph with a handful of names and a sentence or two about each. Companies on that list have entered the buyer’s consideration set before a single website is opened. Companies that are not on it may never learn that the conversation happened.
That gap, between where buying decisions are now being shaped and what businesses can actually measure, is the market Marketing Kernal is betting on.
The numbers behind the shift
The scale of the change is no longer speculative. In October 2025, OpenAI chief executive Sam Altman said ChatGPT had passed 800 million weekly users. In February 2024, the research firm Gartner predicted that traditional search engine volume would fall 25% by 2026 as AI chatbots and virtual agents absorbed more queries.
The clearest independent evidence so far comes from the Pew Research Center. Analyzing the browsing activity of 900 U.S. adults in March 2025, Pew found that users clicked a traditional search result in 8% of visits when Google displayed an AI-generated summary, compared with 15% when it did not. Links inside the summaries themselves were clicked in roughly 1% of visits. The median AI summary in the study was 67 words long.
Sixty-seven words is not much space. For most businesses, it is the new front window.
What the company does
Marketing Kernal was formally established in 2026 by co-founders Vaibhav Palhade and Pratyush Shrivastava after roughly two to three years of research. It currently offers consulting work in AI visibility, generative engine optimization, entity SEO and digital PR, and is developing a family of software products under the Kernal name. The first to be named publicly, Outreach Kernal, is a B2B prospecting tool for finding and researching the right buyers with verified data. The founders decline to identify the others, beyond saying one is aimed squarely at the visibility problem described here.
Palhade, an engineer by training who spent years writing screenplays for Hindi and Marathi film and television and has published several novels, frames the shift in simple terms. “The old question was, where do we rank?” he says. “The new question is, what does the machine believe about us, and where did it learn it?”
Those beliefs, practitioners in the field generally agree, are shaped by factors many companies have never managed deliberately: how consistently a business is described across the web, whether credible publications reference it, whether its information is structured so that machines can read it, and whether its basic details such as name, location and category agree from one source to the next.
Why measurement is the hard part
Shrivastava, who leads technology at the company, says the difficulty is not collecting answers but interpreting them. The same question can produce different businesses depending on wording, model version and timing.
“If you check once, you have an anecdote,” he says. “You need consistent questions, repeated sampling, a record of which sources were cited, and a way to compare all of it over time. It looks less like a ranking report and more like a controlled experiment.”
That methodological problem is also why the category has attracted skepticism. A growing number of agencies and software vendors now sell AI visibility services, some with bold promises attached. Marketing Kernal’s founders take an unusually cautious line. The company does not guarantee rankings, AI mentions or lead volumes.
“Nobody at any agency sits inside these models,” Palhade says. “Anyone promising you a place in the answer is guessing. What you can control is your method and how honestly you report what happened.”
Regulation as a selling point
The company’s other bet is on compliance. It is targeting businesses in the United States, the United Kingdom, the UAE, Canada, Australia and India, several of which operate under demanding rules. Europe’s GDPR governs personal data, the EU AI Act sets obligations for AI systems, and Quebec’s Bill 96 imposes French-language requirements on commerce in that province.
“A lot of growth software was built on the assumption that you can scrape anything and message anyone,” Shrivastava says. “That assumption gets more expensive every year. In finance, health and other regulated sectors, it is already a dealbreaker.”
The open questions
Whether AI visibility becomes a durable business category or a passing label remains an open question. The platforms change quickly, and some of the practices sold under the banner today may not survive the next model update. Large marketing software vendors are also moving into the space, which will squeeze smaller entrants.
Marketing Kernal is early, and its founders say so. Both had previous startups that failed, Palhade three or four of them, and they describe the long research period as a direct response.
What seems less open to debate is the underlying behavior. Buyers are asking machines for advice, and the machines are answering in a few dozen words. For executives, Palhade argues, the first step costs nothing.
“Ask the assistants your customers use about your own category, in every market you sell in, and write down what they say,” he says. “Most companies are surprised. Some are alarmed. Either way, they finally know.”
