Arpit's Profound AI Rep

Arpit turns primary conversations and multi-source data into focused, decision-ready investment research.

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Arpit Tevatia

Edges

Building Primary and Secondary Research

Arpit runs research end-to-end, combining primary conversations with structured secondary analysis to arrive at evidence-backed conclusions. He does not treat these as separate workstreams; the primary conversations validate and sharpen what the secondary data surfaces. This pattern appeared at Propacity, where he spoke with brokers and sales representatives to validate pricing trends and buyer preferences, and at AKMV Consultants, where he profiled institutional investors and synthesized sector research to support live M&A mandates. His method starts with defining the decision the research needs to support, then breaking the problem into structured workstreams before collecting data. He refines the framework as new information arrives rather than locking in an approach upfront. The result is research that holds up under scrutiny because the conclusions are grounded in both market data and on-the-ground validation.

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Synthesizing Research Into Investment Narratives

Arpit's strongest output is not the volume of research he collects but the clarity of what he produces from it. He consistently distills large, multi-source research sets into focused memos and reports that answer the decision at hand. At AKMV Consultants, he prepared a sector research brief for a transport and logistics M&A mandate, drawing on BCG and Deloitte reports, company filings, government data, and competitor analysis. Rather than presenting everything, he identified the themes that mattered for the investment case: market growth, industry fragmentation, technology adoption, and margin drivers. At Propacity, he synthesized broker conversations, infrastructure data, and developer analysis into a structured report that highlighted which micro-markets had the strongest growth potential and why. He also applies editorial judgment to what gets included. When data points do not support the investment case, he recommends moving them to an appendix rather than padding the output, backing his view with evidence.

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Forming Independent Sector Views

Arpit builds investment perspectives on sectors he is not actively researching, tracking AI, fintech, and climate tech through industry reports, startup news, and founder and investor commentary. The goal is to maintain context so that when a live project begins, he already has a baseline understanding of the market, key players, and emerging trends. He formed an independent view that vertical AI applications will create more durable businesses than horizontal AI tools, reasoning that startups solving specific enterprise workflows can build stronger competitive advantages through proprietary data and deeper integration. He arrived at this view by combining industry reports, startup funding trends, and observations about enterprise AI adoption patterns. This habit of continuous sector reading shapes how he approaches live research. He spends less time building baseline context and more time asking better questions and validating assumptions. The view is one he continues to refine by tracking new startups, funding rounds, and enterprise adoption data.

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