Manasvi's Profound AI Rep
Manasvi's view on her literature background shows how she connects out-of-work reading to in-work output.
Manasvi Yadav
Edges
Running End-to-End Research
Manasvi picks up a research brief in a poorly-mapped market and works through it independently to a clear recommendation. At gradCapital, she ran this pattern twice: once on an AI co-scientist platform, synthesizing complex technical signals from Google DeepMind and AlphaFold into a pass recommendation the fund adopted; and once on a proposed biotechnology arm, where she scoped the entire workstream herself. Her approach is to anchor early on the structural question, map the landscape through sustained reading, and deliver a recommendation with the primary sources attached. The output is a call, with the evidence behind it.
Spotted in 2 Stories
Synthesizing Qualitative Research
Manasvi takes large, unstructured qualitative datasets and works them down to a small number of clear, actionable outputs. At Tata Motors, she synthesized over 150 B2B commercial vehicle customer responses into three to four feature recommendations, including a smart search concept she developed with the ML team. At gradCapital, she screened hundreds of PhD candidates and identified the first hire for a new biotechnology arm. The challenge in both cases was the same: the raw material was behavioral and interpretive, not statistical. The output required judgment about what the data actually supported, not just what it said. Her synthesis process moves from raw inputs to a structured recommendation the team can act on.
Spotted in 2 Stories
Building Sector Theses from Research
Manasvi reads deeply across sectors and builds her own theses from the ground up. On AI co-scientists, she traced DeepMind's structural shift and recommended passing. On climate, she concluded the sector was not investable and landed on an energy efficiency thesis instead. Her starting question for any nascent market: will AI let this sector exist, and will it exist profitably? The views she forms are grounded in primary sources and stated plainly.
Spotted in 1 Story
Manasvi Yadav
About Me
Manasvi Yadav is an early-career research analyst with experience spanning venture capital and strategic planning. She brings a strong research instinct to nascent, poorly-mapped markets and a track record of delivering recommendations that shape real investment and operational decisions.
Most recently at gradCapital, she worked in the founders' office with visibility across fund operations, owning research verticals end to end across AI, biotech, and frontier tech.
At gradCapital, she led the full research workstream for a proposed biotechnology arm, covering firm benchmarking across the US and EU, location strategy, and candidate screening. Her recommendations on hub location and the first hire were both adopted.
She also ran independent market research on an AI co-scientist platform, synthesizing complex technical signals from Google DeepMind and AlphaFold into a clear pass recommendation the fund acted on.
At Tata Motors, she designed and synthesized a qualitative survey of over 150 B2B commercial vehicle customers, translating responses into a smart-search feature concept developed with the product and ML teams.
Manasvi Yadav
A literature degree is one of the best preparations for research-heavy analytical work. The reading habit it builds outside work is what shows up inside it.”
Manasvi Yadav
On research and background