This Startup Helps Brands Get Recommended by AI, Not Just Ranked on Google
As AI begins to reshape how people discover products and services online, Gurugram-based DareAISearch is building tools to help brands stay visible across platforms such as ChatGPT, Gemini, and Perplexity.
Around 2024, Siddhartha Vanvani noticed two shifts happening at the same time. More consumers were asking AI platforms what to buy, whom to trust, and which brand to choose. At the same time, brands were seeing their organic visibility decline as AI-generated answers increasingly became the first point of discovery.
“Businesses had spent two decades optimising for search engines, but nobody was helping them optimise for AI recommendations,” Vanvani says. “We saw that opportunity early and decided to build for the future before it became obvious.”
That insight led to the launch of DareAISearch (DAIS) in 2026. The Gurugram-based startup helps brands get discovered, cited, and recommended across multiple AI search platforms. Founded by CEO Vanvani, CPO Siddhant Jain, and Nitisha Agarwal, all part of the leadership and founding team of the performance marketing agency Digidarts, DAIS has since grown into a 15-member team.
Building for AI Discovery
Vanvani explains that DAIS brings together AI search analytics, intelligence, and deployment on a single platform powered by what it calls its ‘Human Decision Intelligence Engine’. The system analyses publicly available marketing and discovery signals across AI platforms, search engines, brand-owned properties and the open web. These signals include AI citation data, search and behavioural trends, brand content, competitive visibility and recommendation patterns.
The analytics layer tracks how brands are discovered across AI platforms. The intelligence layer explains why AI systems recommend certain brands over others, while the deployment layer identifies and implements improvements that could increase visibility.
“We don’t believe brands need another dashboard,” Vanvani says. “They need an operating system that continuously measures, learns, and improves how AI systems discover, evaluate, and recommend them. We help brands move from measuring rankings to engineering recommendations.”
The company believes AI search is fundamentally changing how online discovery works. Unlike traditional search engines, which largely respond to existing demand, AI platforms increasingly shape consumer decisions by recommending what they consider the most relevant and trustworthy option rather than simply the most searched brand.
The Shift in How People Discover Brands
The change in user behaviour is already visible at scale. In a recent blog post, Google said AI Overviews now has more than 2.5 billion monthly active users, while AI Mode has crossed one billion monthly users.
“Consumers are increasingly asking AI what to buy, where to travel, which software to adopt and which brand to trust,” Vanvani says. “We're witnessing the biggest shift in digital discovery since Google popularised web search.”
While advertising is expected to become a larger part of AI search over time, Vanvani notes that recommendations across major large language models remain largely organic today.
“Advertising will inevitably become part of AI search,” he says. “Our roadmap already includes optimisation for both organic and sponsored AI discovery. We hope to become the intelligence and deployment layer for AI search, irrespective of how discovery evolves.”
This dual focus organic recommendation engineering today and preparation for paid formats tomorrow positions the platform to remain relevant as the ecosystem matures.
Serving Enterprise Brands Across Categories
DAIS currently works with more than 25 enterprise brands across healthcare, consumer products, retail, automotive, and technology. Its customers include ASICS, Ambuja Cement, Suntone, Earthful, and Sotheby Motorsport.
As more brands compete for visibility across AI platforms, some observers believe discovery will become increasingly crowded. Vanvani sees the situation differently.
“Every AI system that helps people discover, evaluate, and decide will require three capabilities: measurement, intelligence, and deployment,” he says. “That is the infrastructure we're building. As AI adoption grows, our intelligence engine becomes stronger because every interaction generates new intelligence about how brands are interpreted and recommended.”
The startup competes with New York-based Profound and Berlin-based Peec AI. Largely bootstrapped, DAIS has generated revenue of about ₹1.8 crore so far this year.
Looking Ahead in a Growing Market
The global AI search market is presently valued at $16.3 billion and is projected to reach $50.9 billion by 2033. Vanvani believes the company has a clear opportunity to scale as one of the category leaders, not just in India but globally.
“Our roadmap is anchored around reaching $100 million in annual recurring revenue over the next 40 months by helping enterprise brands measure, improve and scale their AI visibility and recommendation intelligence,” he says.
The company remains in its early stages, yet expects growth to accelerate as AI-driven discovery becomes more mainstream. Brands that once treated search-engine optimisation as a core digital discipline are beginning to recognise that visibility inside large language models requires a different set of capabilities ones that combine continuous measurement, interpretive intelligence and practical deployment.
“We expect to grow this several-fold over the coming quarters as AI-driven discovery becomes mainstream and more brands begin treating AI visibility as a core business metric,” Vanvani adds.
Why the Opportunity Matters Now
For two decades, digital marketing teams optimised for blue links and ranking positions. That playbook is no longer sufficient when the first answer a consumer receives is generated by an AI system that synthesises information, evaluates trust signals and issues a recommendation. Brands that appear in those answers gain disproportionate attention; those that do not risk becoming invisible even if their traditional search rankings remain strong.
DareAISearch is attempting to give brands an operating system for this new environment. By combining analytics that show current visibility, intelligence that explains the underlying reasons, and deployment tools that improve outcomes, the platform aims to turn AI search from a black box into a manageable channel.
Whether the company reaches its ambitious revenue target will depend on how quickly enterprises adopt the discipline of AI visibility and how effectively DAIS can demonstrate measurable gains in recommendations. For now, the early traction with well-known brands and the broader market projections suggest that the problem it is solving is both real and growing. In a discovery landscape increasingly shaped by large language models, the ability to be found, cited, and recommended by AI systems is becoming a strategic priority rather than a technical curiosity.