Vibhat's Profound AI Rep
Vibhat takes ambiguous research briefs and builds structured outputs that investment teams can act on.
Vibhat Chabra
Edges
Structuring Investment Research Outputs
Vibhat takes ambiguous research briefs and turns them into structured outputs that senior investors can act on. He works backward from the decision that needs to be made, maps the key questions, and builds the analysis around answering them. This pattern showed up clearly in the US healthcare due diligence at TresVista, where he owned the research workstream and synthesized operational metrics, utilization analysis, and financial projections into a 60-page IC memo that moved the fund to a letter of interest. His approach is to clarify the desired end state before pulling data, whether the output is a live deal evaluation, a sector thesis, or a risk assessment. That discipline keeps the research decision-oriented rather than descriptive. At this stage of his career, the pattern is emerging across two contexts: live deal due diligence and consulting publication work.
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Sourcing and Screening Investment Opportunities
Vibhat builds the front end of an investment research process: identifying relevant companies, mapping competitive landscapes, and screening opportunities against an investment thesis before deeper analysis begins. At TresVista, he independently sourced and screened US healthcare companies for a PE fund, using industry databases and AI-assisted research tools to identify relevant market trends and comparable companies. He approaches sourcing by first clarifying what decision the research needs to support, then building a first-principles map of the market: who builds, who distributes, who the end user is. That structure keeps the screening focused on the questions that matter for the investment thesis. This is an emerging pattern at this stage of his career, grounded primarily in the healthcare due diligence context.
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Building Operational and Financial Models
Vibhat builds the quantitative layer of an investment analysis: utilization models, coverage models, and roll-up projections that translate operational data into investment-relevant conclusions. At TresVista, he built a utilization model for a US healthcare clinic, calculating capacity usage per doctor against industry benchmarks, and assembled a roll-up model projecting cost savings from operational improvements. Both fed directly into the IC memo reviewed by senior investors. On the private credit side, he built coverage models for new portfolio companies and maintained existing models across a portfolio of 20+ companies, ensuring accuracy in financial data uploads and covenant compliance tracking. The pattern is consistent: he builds models that serve a specific analytical purpose, grounded in the data available and the decision being made.
Spotted in 2 Stories