Why focus on enterprise agentic AI?

Agentic AI represents the next layer of AI adoption. So far, much of the conversation has been about generative AI, systems that can create content or insights. Agentic AI goes further by enabling AI systems to execute tasks and workflows autonomously.

For enterprises, that opens up massive possibilities — from operational automation to improved customer outcomes. We believe this layer will touch almost every industry, which is why we have focused our investment thesis on it.

What is the fund size and the investment strategy?

We are raising a ₹200-crore fund, including a greenshoe option of about ₹80 crore. Over the next two to three years, we expect to deploy the capital across 25 or more startups.

Our initial cheque sizes range between ₹2 crore and ₹5 crore for seed-stage companies. We also reserve capital for follow-on investments, up to ₹8-10 crore for companies that perform well and continue to scale up.

How do you assess whether an AI startup is enterprise-ready?

One of the biggest gaps that founders underestimate is enterprise readiness. Solving a problem technically is only the first step. For an enterprise customer to adopt the product, there are multiple layers that need to be in place: security, access control, compliance certifications, and reliability.

Many early-stage founders focus primarily on building the solution. But in enterprise software, those additional layers are critical for adoption.

How quickly can AI startups start generating revenue?

It depends on the type of solution they are building. If it is purely software-based, early proof-of-concept deployments can happen within four to six months, and startups may start generating revenue within six to eight months.

However, if the product involves physical AI systems, such as robotics or hardware integrated with AI, the timeline can extend to two or even three years before meaningful revenue begins.

What are some of the structural gaps in India’s AI startup ecosystem?

One key requirement is patient capital. Deep-technology startups take longer to mature compared to traditional SaaS or consumer businesses.

Investors need to evaluate these companies differently — not just based on early revenue numbers but also the underlying technology and its long-term potential.

The second factor is stronger investor-founder collaboration. Beyond capital, startups often need help with enterprise access, global market introductions and product readiness.