Ishita's Profound AI Rep
Ishita turns vague market briefs into structured investment theses, backed by deal data and sector tracking.
Ishita Narayan
Edges
Sector Thesis Development
Ishita builds structured investment theses from poorly mapped markets, starting with the outcome a stakeholder needs and working backwards to the research structure required to get there. This pattern runs through her pharma commercialization whitepaper, her M&A deal origination work at TresVista, and her independent healthcare sector tracking. Her method is to start broad with the industry landscape, identify the one or two drivers that actually explain market movement, and build the thesis around those. In the pharma whitepaper, that meant recognizing that the M&A thesis was the thread that explained everything else and going deep on deal-by-deal analysis to surface it. The output is research that shapes where investors focus, not just describes where the market is.
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M&A Pattern Analysis
Ishita reads M&A deal flow to surface the thesis driving a sector, going beyond deal counts to understand what acquirers are actually paying for and why. In the pharma commercialization whitepaper, she studied each transaction individually to identify the primary thesis behind it, recognizing that the shift toward point-of-solution providers was the thread connecting the market's deal activity. Across three live M&A mandates at TresVista, she performed precedent transaction analysis and comparable company analysis as part of the valuation and origination work, building the competitive positioning that shaped pitch books and CIMs. Her instinct is to use deal data as a leading indicator of where a sector is heading, not just as a valuation input.
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Research Infrastructure Building
Ishita builds the systems and workflows that keep a research function current and consistent, not just the individual outputs. At TresVista, she built a Capital IQ-based market performance database that the team used to track indexes, companies, and M&A deal flow on an ongoing basis. She also integrated Model ML to automate financial data collection and visual summary generation, reducing the time required to produce senior-ready market snapshots. She ran weekly healthcare sector newsletters for senior bankers throughout her tenure, synthesizing deal flow and sector shifts into a format the team could act on between active mandates. The instinct behind this work is to make research repeatable and accessible, so that the team's sector knowledge compounds over time rather than resetting with each new mandate.
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