Anand's Profound AI Rep
Anand's view on startup evaluation shows how he separates real problems from solutions in search of one.
Anand Yadav
Edges
Ground-Level Primary Research
Anand's instinct when facing an unclear market is to go directly to the people living the problem. He does not rely on secondary sources to form a view; he forms it through direct conversation. This showed up at Katha VC, where he met fintech founders at their offices to gather the raw material for investment memos. It showed up more extensively in his MediQuik research, where he interviewed hundreds of pharmacy owners across Tier 2 and Tier 3 towns to map a fragmented supply chain. The pattern is consistent: he starts with a few core questions and lets the conversation surface what matters. He audio-records sessions and synthesizes findings into structured outputs. For an early-career researcher, the willingness to do this kind of ground work at personal expense, across hundreds of conversations, is a meaningful signal of how he approaches a research problem.
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CStructuring Research into Investment Cases
Anand takes unstructured field information and organizes it into a clear, structured output that others can act on. This is the core of what he did at Katha VC. His investment memos followed a defined structure: executive summary, problem and market opportunity, solution and product, business model and unit economics, competitive landscape, team assessment, traction and metrics, investment thesis and risk, terms and ask, and his own recommendation. He applied the same structuring instinct to MediQuik, where ground-level interviews became a business plan with a defined problem statement, solution design, delivery mechanics, and go-to-market logic. The ability to move from raw conversation to a structured, decision-ready document is an early but consistent pattern in his work.
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CAI-Assisted Research and Drafting
Anand uses AI tools as a practical accelerant in research and drafting work. When he took on legal drafting for a Supreme Court advocate navigating India's updated criminal codes, he used ChatGPT and Grok to navigate the new framework, cross-reference provisions, and produce draft documents faster than traditional methods allowed. The approach worked without loss of accuracy. He treats AI as a tool for moving faster through structured tasks, not as a replacement for judgment. This is an emerging pattern rather than a deep track record, but the instinct to reach for AI tools to solve a real workflow problem, and to do it effectively, is relevant for research roles that require speed alongside rigor.
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Anand Yadav
About Me
Anand Yadav is a researcher who goes to the ground. When a question matters, he talks to the people living the problem, not just the people writing about it.
He is currently a Research Fellow at Katha VC, where he meets fintech founders directly, evaluates their businesses, and writes investment memos that shape how the firm engages with early-stage companies.
His independent research on MediQuik, a B2B pharmacy procurement idea, came from a personal experience and led to hundreds of conversations with pharmacy owners across smaller Indian towns. He mapped the supply chain problem, identified the pain points, and built a structured business case from the ground up.
He brings the same instinct to legal drafting work, using AI tools to move faster without losing accuracy, and to his ongoing reading of India's startup ecosystem through newsletters and founder communities.
Anand Yadav
A lot of startups are solutions looking for a problem. You can tell in the first conversation if you're listening for the right things.ā
Anand Yadav
On evaluating startups