Yajur's Profound AI Rep
Yajur builds investment conviction from primary data across VC and consulting contexts.
Yajur Mahajan
Edges
Building Investment Conviction from Primary Research
Yajur builds investment views from the ground up, combining primary stakeholder interviews with structured secondary analysis to arrive at conclusions that hold up under scrutiny. He has applied this across VC deal diligence at Warmup Ventures, sector thematic research on cross-border payments, and large-scale stakeholder interview programs at Deloitte. His approach is to validate the hardest assumptions first, whether that is right-to-win for a QSR brand or the addressable segment within a regulated fintech market, before building the broader analysis around them. The result is research that is structured for decision-making, not just documentation.
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Structuring Research for Investment Decisions
Yajur frames research outputs around the decision at hand, not the process behind them. He leads with the hook that matters to the senior audience, whether that is traction metrics for an investor or a cost reduction roadmap for a steering committee. This shows up in how he structured the Boba Bhai memo, opening with the brand's contribution margin trajectory and retention rate before building the supporting thesis. It also shows up in how he presented findings to the Middle East bank's management committee. His view is that the most consequential points should lead, and the supporting detail follows once the primary decision criteria are addressed. This makes his research outputs usable in live decision-making contexts, not just as reference documents.
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Forming Independent Sector Views
Yajur builds market theses from first principles, identifying where incumbents are failing users and where structural shifts are creating space for new entrants. His cross-border payments thesis identified the SME and freelancer segment as the key white space in India's outward remittance market at a time when the VC consensus was skeptical of the category. A player he had focused on later became one of the more prominent startups in the space. His Korean cuisine thesis for Boba Bhai was grounded in cultural tailwind research, geographic demand mapping, and direct founder assessment, not just the company's own pitch materials. He actively tracks deals, reads across sectors outside his immediate work, and builds views on emerging segments like micro dramas in Indian media before they become consensus themes.
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AI-Augmented Research and Benchmarking
Yajur uses AI tools as a research accelerator, not a research replacement. His approach is to use AI to validate hypotheses, generate benchmarks, and decode complex information, while verifying that every output is traceable to a credible primary source. At Deloitte, he used AI to benchmark banking product offerings across global competitors when primary data was sparse, and to summarize complex ATM fee regulatory models across multiple geographies for a Caribbean central bank engagement. His discipline is to treat AI outputs as a starting point for analysis, not a conclusion. He checks sources, flags hallucinations, and uses the tool to compress the time needed to build context, not to substitute for judgment. This makes him faster on research tasks without sacrificing the rigor that investment-grade analysis requires.
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