AI search is becoming the front door for consumer discovery, but most brands are invisible to it. Vaishnavi Varma is building khaa-lo to change that, turning the messy, specific questions shoppers ask AI assistants into marketing intelligence for emerging consumer brands.
khaa-lo launched as AI discoverability software for small consumer labels. It is now expanding into a platform that helps brands read consumer intent directly from AI search, while connecting shoppers with smaller brands that match their needs.
Why AI search signals matter
When a shopper asks an AI assistant which protein powder suits a sensitive gut, or which skincare line won't wreck oily skin, they reveal more than a search keyword ever carried. The phrasing is specific, frequently clumsy, and arrives well ahead of any purchase or trend report. Most of that signal goes nowhere useful.
Varma, an AI product designer who founded khaa-lo, saw this gap early. Most services addressing large language model discovery were aimed at enterprises. Her concern was that young brands failing to adapt early would be "quietly sorted into obscurity by systems they never learned to read," as she put it.
The market data supports her opening. Research presented at Adobe Summit this year, drawn from a database of more than 213 million language model prompts, found that 62% of brands are technically invisible to generative AI models. Only 8 to 12% of results appearing in AI answers overlap with those ranking well in traditional search.
From visibility to intelligence
khaa-lo's second phase inverts the direction of information. Instead of helping brands construct a narrative engineered to become the answer to a given prompt, the platform helps brands read demand directly. It recognizes patterns in consumer intent, evolving search behavior, and market trends, giving marketing teams a new source of intelligence for product development, positioning, and growth.
Varma's critique of existing tooling is that it solves the wrong end of the problem. Companies with existing visibility, content volume, and domain authority are already legible to AI systems, so they benefit first and most. The tooling built to interpret this shift has been designed around enterprise marketing organizations, leaving a gap for growing brands.
The authority emerging brands could never afford to build in traditional search does not transfer into AI systems, which levels a field that was never level. That is why AI search should become a strategic capability for growing brands, according to Varma. Marketing professionals looking to build this capability can start with AI for Marketing Courses to understand how these systems work.
Logging without prescribing
The consumer side of khaa-lo carries the bigger design risk. In the next feature launch, users will be able to upload receipts and track aspects of daily wellbeing that fall outside usual step counts and heart rate readings, including protein intake and gut health.
The company is explicit that this is not a health app. It offers no diagnosis and no medical advice. The purpose is reflection and logging: noticing what you actually consume, building a record of it, and using that record to find smaller brands worth trying.
Holding that line is harder than stating it. Consumer wellness products drift toward prescriptive language because prescription converts. Varma has designed against that pressure deliberately, placing khaa-lo in a quieter corner of the wellness market organized around conscious consumption and taste instead of clinical authority.
The receipt upload is the sharper mechanic. Purchase history is among the least performative records a person keeps. It captures what someone bought rather than what they told a feed they aspired to buy, making it a better input for brand discovery than engagement data.
Design discipline from enterprise security
Varma's route into product design ran through enterprise security. She holds a physics degree from Syracuse University and moved into vulnerability management at Bank of America, working with large datasets and building dashboards that had to satisfy audit requirements while staying legible to cross-functional leadership.
She now runs that thinking as a consulting practice for founders at zero to one, the stage where a product works and no stranger can tell why it matters. The work covers which screens a stranger sees first, what a non-technical founder can still maintain once the consultant leaves, and whether a feature should run slower than it technically can so the person using it understands what just happened.
Founders arrive asking for an interface and leave having had the harder conversation about what their product is for. "Technology to aid you not replace you" is her motto. It reads as modest until you notice how much of the AI product market is built on the opposite premise.
Varma's art practice informs this work. She works in mixed media using canvas and electric paint, then carries those textures into functional websites built to communicate clearly to both people and AI search systems. She hand-draws the UI and UX for a founder's app before opening a design tool. Underneath the practice is a claim about what small brands lose when they optimize: a founder-led label's cultural voice gets sanded down in the process of becoming machine-legible.
Why this matters for marketing teams
The category Varma is working in has a known failure mode. Plenty of tools promise to show brands how machines perceive them, and most resolve into charts that describe a problem without helping anyone act on it. Marketing teams should watch whether khaa-lo's approach holds up: reading consumer intent directly from AI search, rather than optimizing for what a brand thinks a shopper wants.
The bet underneath khaa-lo is that as AI search becomes the layer through which people find what to buy, the advantage belongs to companies that keep the human legible on both sides of the exchange. For marketing professionals, the immediate takeaway is practical: the signals in AI search queries are a strategic asset, and the tools to interpret them are no longer confined to enterprise budgets. Marketing managers who want to understand this shift in depth can follow an AI Learning Path for Marketing Managers to build the relevant skills.
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