AI startups bask in funding summer as investors double down on enterprise AI adoption
Investors are bullish on domestic AI startups, focusing on enterprise applications, agentic AI, and AI-driven services rather than foundational models, as startups rapidly scale revenue, secure Series A funding, and capitalise on growing enterprise AI adoption.
Artificial intelligence is the new MVP in town—not the MVP of startup parlance, but the Most Valuable Player—and a new favourite among investors.
It has been almost three years since OpenAI launched the revolutionary ChatGPT tool, and there has been no stopping since. Adding to the AI charm are China’s DeepSeek and OpenAI’s colossal $500 billion Stargate Project, which have increased the appetite of investors who are not shying away from pouring millions of dollars into AI startups.
Meanwhile, in India, investors are more interested in rapidly expanding AI applications and concrete use cases—spanning enterprise IT solutions, PaaS (platform-as-a-service), and agentic AI, as the country charts its course for its own foundational model.
What startups are building
The last two weeks have been a funding frenzy in India's AI startup ecosystem, with investors doubling down on applications-focused ventures. According to a report by AI investment fund SenseAI Ventures, almost two-thirds or 65% of startups are building AI applications.
It added that in 2024, Indian AI startups secured $1.35 billion in funding, with Bengaluru garnering almost 40% of the total investment secured.
Startups leveraging AI to automate enterprise workflows and IT service management, including SuperOps, , , and Cognida.ai, are securing the bulk of investments.
For instance, Chennai-based SuperOps—which leverages agentic AI to revamp IT operations— recently raised $25 million in Series C led by March Capital. According to CEO Arvind Parthiban, SuperOps’ agentic AI approach allows it to go beyond basic automation and create truly intelligent workflows.
“What distinguishes SuperOps' AI is our vision for an integrated, AI-powered workforce. We are not simply adding AI as an afterthought or bolting on a few AI-powered features. We are fundamentally rethinking how IT operations can be managed by combining the strengths of human expertise with the power of autonomous and semi-autonomous AI agents,” the co-founder tells YourStory.
Similarly, Khosla Ventures-backed Atomicwork helps enterprises reduce IT service costs and boost productivity across functions through its agentic service management platform. The San Francisco-based startup recently raised $25 million in Series A funding led by Khosla Ventures and Z47.
“It’s a bold and ambitious mission because, typically, startups build software for a specific team within a department—whether it’s customer success, sales operations, or digital marketing. In fact, 95% of startups follow that pattern. We're building for every employee across the organisation,” says Vijay Rayapati, CEO of Atomicwork.
Meanwhile, San Francisco-based TrueFoundry offers a cloud-native platform that simplifies machine learning (ML) training and deployment, enabling enterprises to manage AI applications efficiently. The AI deployment and scaling platform recently bagged $19 million in Series A funding led by Intel Capital, with participation from existing investors Eniac Ventures and Peak XV’s Surge.
“However, we’ve taken this a step further by introducing TrueFoundry agent—an AI-powered system that analyses logs, monitors metrics, and troubleshoots systems in real-time. The agent enables autopilot-driven deployments, making the process efficient,” explains Co-founder Anuraag Gutgutia.
India’s competitive edge
Despite India’s rising AI ambitions, SenseAI Ventures says, early-stage AI infrastructure and foundational startups account for just 3% of the country’s total startups. What makes them unique is their focus on various AI use cases rather than large language models (LLMs), as highlighted by influential voices like Infosys co-founder Nandan Nilekani.
Atomicwork’s Rayapati agrees. Prioritising AI applications over LLMs will enable Indian AI startups to do more with fewer resources and scale globally. “Today, every startup claims to be an AI startup. But the reality is, 99% of us aren't building models—and there's really no reason to. The returns from building models are often quite low.”
From an India SaaS perspective, AI can help build enterprise platform companies, which earlier took much longer and cost a lot of capital and manpower to build. Not to mention the significant risk involved, he adds.
Anant Vidur Puri of believes that while the foundational model opportunity does exist, and many companies will go out and build India-centric models, he suggests India's massive population is a key asset, driving long-term growth opportunities in the AI services sector.
“For India, this opportunity is much larger. We're a country of a billion and a half people, and if you take a 10 or a 30-year view, our population is a major asset,” he explains.
While the US dominates the AI sector, India shouldn’t try to replicate its high-cost model, cautions Puri. “India is not the US, and the US is not India. We can't blindly copy or believe whatever works there will work here. I don't know if we can spend the kind of money that OpenAI and Anthropic have. As a country, we can afford to do that, but our focus lies in the culture of frugality and productive engineering talent.”
Pranay Desai, Enterprise AI Partner at venture capital fund Z47 (formerly Matrix Partners India), agrees adding that the game needs to be changed.
“For example: AI products also need a lot of consulting/handholding because enterprises go through a lot of change management. Our founders can use professional services as a moat and leverage our service talent. There might be interesting GTM opportunities by partnering with services firms,” he says.
Why investors remain bullish
The rapid evolution of AI technology and the maturing of early-stage startups, especially in the application layer, have kept investors cheery.
Puri notes that founders have had two years to refine their products, identify their ideal customer profiles, and strengthen other business strategies—with the timeline aligning with the typical progression from seed funding to Series A rounds.
“We're now seeing Series A rounds. Typically, startups raise seed capital and follow up with a Series A one to two years later. Given that AI took off in November 2022, it's natural for companies that secured seed funding in 2023–24 to be entering their Series A now,” he adds.
Z47’s Desai sees the industry witnessing a “once-in-a-20-year” market transformation driven by AI, where new markets are getting built.
“Headcount budgets are the new software budgets, and industries spend $300 billion on software and $4 trillion on services. AI is applied to every market to augment or replace this manual work. As the underlying models mature, attention is moving to the application layer, and it has always been India’s strength,” he says.
In fact, many startups launched post-ChatGPT, he says, are now scaling and raising follow-on capital, including portfolio companies such as Atomicwork and Aampe.
According to Krishna Mehra, AI Partner at Elevation Capital, early-stage success relies more on qualitative validation than sheer metrics when evaluating AI startups. “For instance, at the seed stage, we'd look for validation from actual customers using the product to understand their use cases…”
He continues, “As the firm matures to a Series A, the metrics become more important. In AI, it's particularly important to understand the qualitative since the metrics can move really quickly, and the space is developing really fast.”
In the next few years, Mehra expects agentic platforms to become mainstream soon, with founders building strong data moats and executing them strategically leading the way.
Manav Garg of , an AI-focused VC firm, links the funding surge to scalability and fast-growing revenue in early-stage AI firms.
“The surge in funding is happening for two reasons. One, there are early signs of real adoption of AI and enterprises. The scalability of the early AI companies is very high. Some US companies are getting to $100 million (in ARR) in a year, and $10 to $20 million in two months. There are examples of companies where the revenue is killing really fast… which means adoption is happening,” Garg explains.
Second, he adds, “In India, there is a lot of dry powder, with a lot of funding that had not happened in the past. Some funds were waiting for a trend to emerge as to whether AI is real—that picture is beginning to emerge now.”
Edited by Suman Singh
