Vedansh's Profound AI Rep
Vedansh builds structured sector views from vague briefs and finds deals before they hit the databases.
Vedansh Agarwal
Edges
Building Alternative Deal Sourcing
Vedansh builds sourcing systems that go beyond standard databases to find early-stage companies before they are widely visible. At SG Analytics, he identified that PitchBook and Capital IQ lagged by one to two months for stealth-stage pre-seed companies. He mapped LinkedIn creator networks and Reddit communities as live channels, and rebuilt the weekly deal tracker around them. The mechanism is pattern recognition across non-obvious sources: identifying which creators publish early, which communities surface new ventures, and how to filter signal from noise in unstructured feeds. The result is a sourcing edge that gives clients a timing advantage in sectors where early entry matters for valuation.
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Structuring Sector Research from Vague Briefs
Vedansh takes broad, poorly defined investment mandates and builds structured sector views that give clients a basis for decision-making. At SG Analytics, he received a brief as wide as 'invest in enterprise AI' and narrowed it to a specific sub-sector with a clear investment rationale. For the PE client, he built full-stack whitepapers on niche industrial sectors with no prior research base. His approach is bottom-up: he maps sub-segments, identifies the relevant market boundaries, cross-references multiple data sources, and calibrates against client benchmarks before drawing conclusions. The output is a structured sector view that a client can act on, not a summary of what reports say.
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Independent Sector View Formation
Vedansh forms views on sectors and technologies through his own reading, then brings those views into live client work when they are relevant. For the PE client's horizontal drilling brief, he had been independently following automation trends in US manufacturing through newsletters and industry reading. He identified a connection between rising labor costs in horizontal drilling and the timing of automation investment, and included it as a forward opportunity section in the whitepaper, without being asked. The client used that section to redirect capital toward an automation-focused company in the sector. This pattern, reading broadly and connecting it to live work, is consistent across his approach at SG Analytics.
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Building Analytical Models for Investment Decisions
Vedansh builds structured analytical models that give clients a quantitative basis for investment and credit decisions. At SG Analytics, he built a comparable deal multiples model to value a pre-seed voice AI company, choosing the approach deliberately over a DCF given the absence of reliable revenue projections at that stage. At AU Small Finance Bank, he built an employer-based credit risk categorization model that incorporated employer financial health as a signal, changing approval outcomes for stable-income customer segments. Across both contexts, the pattern is the same: identify the right analytical framework for the problem, build it from available data, and deliver an output the decision-maker can act on.
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