Swastik N. | Profound

Swastik's Profound AI Rep

Swastik's stories span private market research, consulting engagements, and independent sector thesis work.

Swastik N.

Edges

Structuring Research from Ambiguous Briefs

Swastik builds structured research frameworks in situations where the brief is vague, the market is poorly mapped, or the available information is fragmented and conflicting. At Gain, he has done this repeatedly on private companies where public information is limited, inconsistent, or sourced from unreliable outlets. At Scholaride, he designed the research methodology from scratch for each client engagement, starting with secondary research to eliminate what was already known before designing questionnaires around the gaps only a primary source could fill. His process starts with the decision the research needs to support, breaks it into core hypotheses, and treats the initial framework as live rather than fixed. As new information comes in, the structure evolves. The result is research that is usable: logically sequenced, triangulated across sources, and structured so that someone unfamiliar with the subject can follow it and act on it.

Spotted in 2 Stories

Pattern-Level Sector Thinking

Swastik does not analyze companies in isolation. He groups them, looks for what they have in common, and asks what the pattern reveals about where a sector is heading. He applied this to 314 Capital's portfolio, grouping companies by sector and identifying the investment thesis behind the fund rather than summarizing individual companies. He applied it to vertical AI, selecting companies across legal, healthcare, coding, and enterprise productivity to test whether workflow ownership or model quality was the real competitive moat. He applied it to quick commerce, tracking how VC scrutiny shifted from category momentum to unit economics as cash burn became the defining constraint. In each case, the view came from observing the same pattern repeatedly across different companies and contexts, then testing it against new examples rather than treating it as a conclusion. The output is a hypothesis that is genuinely his own, grounded in evidence, and useful for evaluating whether a specific company is riding a real trend or short-term noise.

Spotted in 2 Stories

Designing Primary Research Inputs

Swastik designs the research methodology before gathering data, not after. At Scholaride, he built custom questionnaires for each client engagement, doing secondary research first to eliminate questions that could be answered from existing sources, then designing questions around the gaps only a founder or customer could fill. At Anand Rathi, he used structured client questionnaires to understand risk appetite, investment horizon, liquidity needs, and constraints before tailoring financial solutions. He also conducted expert interviews with equity mutual fund managers to understand stock selection processes. The discipline is consistent: define the decision the research needs to support, identify what secondary sources cannot answer, then design the primary research instrument around those specific gaps. This is an emerging pattern. The contexts are real but limited in number, and the primary research has been conducted in consulting and wealth management settings rather than formal investment research.

Spotted in 1 Story

Swastik N.

Career Stories

Researching OpenAI at Gain: From Fragmented Sources to a Usable Investment Profile

At Gain as Private Equity Intelligence - Junior Analyst

2025

Building a Sector View on Vertical AI Through Pattern Research

At Gain as Private Equity Intelligence - Junior Analyst

2025

Mapping 314 Capital's Portfolio to Form an Independent Investment View

At Gain as Private Equity Intelligence - Junior Analyst

2025

Scholaride: Building a Research and Consulting Venture from Scratch

At Scholaride as Co-Founder

2023 - 2025

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