Artificial intelligence is not merely another technological advancement; it marks a significant industrial revolution, according to Satyakam Mohanty, co-founder and managing partner of Wyser Capital. The venture capital firm is targeting enterprise-focused agentic AI startups and companies developing AI systems capable of progressing from analysis to execution.
With a ₹200-crore fund and investments in startups like Bizlog, Pype AI, AquaAirX, and Bizom, Wyser Capital is dedicated to supporting intellectual property-led startups paving the way for the next generation of enterprise technology.
In an interview with businessline, Mohanty, who co-founded Wyser Capital in 2024 alongside Suresh Vaswani and Supria Dhanda, explains the new phase of enterprise AI, criteria for startup readiness, and the structural challenges India must overcome to cultivate globally competitive AI firms.
Why focus on enterprise agentic AI?
Agentic AI signifies the subsequent layer of AI adoption, moving beyond the current focus on generative AI, which primarily creates content or insights. Agentic AI takes a significant step further by enabling AI systems to autonomously execute tasks and workflows. This development presents vast opportunities for enterprises, including operational automation and enhanced customer outcomes, affirming the firm’s commitment to investing in this area.
What is the fund size and investment strategy?
Wyser Capital is raising a ₹200-crore fund, with an additional greenshoe option of approximately ₹80 crore. Over two to three years, the firm anticipates deploying its capital across 25 or more startups. Initial investments will range from ₹2 crore to ₹5 crore for seed-stage companies, with provisions for further investments up to ₹8-10 crore for businesses demonstrating strong performance and growth.
How do you assess whether an AI startup is enterprise-ready?
A common issue among founders is underestimating the factors needed for enterprise readiness. While solving a technical problem is the first step, adopting the product requires meeting several critical criteria, including security, access control, compliance certifications, and reliability. Many early-stage founders concentrate solely on building their solution, neglecting these vital aspects necessary for enterprise adoption.
How quickly can AI startups start generating revenue?
The timeline for revenue generation varies depending on the solution type. Software-based solutions can achieve early proof-of-concept deployments within four to six months, potentially leading to revenue generation within six to eight months. Conversely, products incorporating physical AI systems, such as robotics or hardware, may take two to three years before substantial revenue is generated.
What are some structural gaps in India’s AI startup ecosystem?
A significant necessity is access to patient capital, as deep-technology startups typically require longer maturation periods than traditional SaaS or consumer businesses. Investors should evaluate these companies not just on early revenue figures but on the underlying technology and its future potential. Additionally, enhanced collaboration between investors and founders is vital. Beyond financial backing, startups often need assistance with accessing enterprises, entering global markets, and ensuring product readiness.
