Saanvi's Profound AI Rep
Saanvi turns incomplete data and vague briefs into structured, evidence-backed research outputs.
Saanvi Misri
Edges
Running Primary Research End to End
Saanvi does not stop at the data she is given. Her instinct is to treat the initial dataset as a starting point and design her own research approach to validate or challenge what it shows. This pattern has appeared across TVS, KPMG, and her consulting work at SRCC. At TVS, she was handed a dataset and returned a root cause, after adding social media analysis and direct consumer calls to the brief. At KPMG, she designed questionnaires and ran competitor calls herself for a UAE diagnostics client. At Barkabuki, she conducted a personal field check when online data was not sufficient. The mechanism is consistent: she identifies what the existing data cannot answer, designs a research step to close that gap, and runs it herself. The output is always grounded in evidence she has personally gathered and validated. For teams that need research they can stand behind in front of a client or a founder, this is the capability that makes the difference.
Spotted in 3 Stories
Building Evaluation Frameworks from Incomplete Data
When the data is incomplete and the brief is vague, Saanvi builds the structure herself. She does not wait for a clean dataset or a pre-defined framework. At KPMG, she developed a six-criteria feasibility framework for Indian green hydrogen projects from scratch, after reading the industry thoroughly and identifying which factors actually determined project viability. At Dexter Ventures, she mapped government schemes across seven B2B sectors and built her own view on what made a sector investable, combining policy data, growth rates, and adoption trends. The pattern is consistent: she reads the domain, identifies the criteria that matter, and builds a framework that can be applied comparably across cases. The frameworks are grounded in research, not borrowed from a template. For investment research work where the market is poorly mapped and the brief is open-ended, this is the capability that turns ambiguity into a structured output.
Spotted in 3 Stories
Forming Independent Sector Views
Saanvi forms investment views by combining multiple evidence sources rather than relying on a single report or data point. Her instinct is to look for structural growth rather than short-term momentum. At Dexter Ventures, she analyzed seven B2B sectors and concluded that sectors with digital transformation tailwinds and government policy backing had more durable long-term potential than those driven by temporary demand. She arrived at that view by comparing industry reports, government schemes, growth rates, competitive intensity, and adoption trends across all seven sectors. She also stays current on sectors she is not actively researching, following funding announcements, regulatory changes, and consumer behavior shifts through sources including Mint, YourStory, and the Economic Times. This is an emerging capability: the evidence base is strong for one major project, and the habit of sector reading is consistent. The depth will grow with more investment-context exposure.
Spotted in 1 Story