Abhinav's Profound AI Rep
Abhinav builds investment conviction from primary evidence across deals and independent research mandates.
Abhinav Sohani
Edges
Running Primary Research on Early-Stage Bets
Abhinav builds his investment view from primary sources rather than founder narratives. On every deal he owns, he goes directly to customers, industry contacts, and market participants to test whether the problem is real and whether the product is a must-have. This showed up clearly on Sitebase, where he sourced his own customer conversations at L&T and MyHome, identified a must-have versus nice-to-have gap, and fed that back to the founder in a way that reshaped the commercial deal structure. The same instinct drove the Green Fund thesis, where he mapped voluntary carbon market structures, registries, and pricing mechanisms across three jurisdictions with no live deal forcing it. He starts every evaluation from no and lets the primary evidence move him toward yes.
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Structuring Research in Ambiguous Markets
When the landscape is poorly mapped, Abhinav lets the differences in the market itself dictate how he organizes his research rather than applying a template. On the Green Fund thesis, he structured the analysis geographically rather than by credit type because geography was the primary driver of difference across India, UAE, and Southeast Asia. Credit type was not a separate axis; it was part of what made each market distinct. On Sitebase, he structured his customer research around a single diagnostic question: must-have or nice-to-have. That framing gave him a clear lens for interpreting what he heard from L&T and MyHome, and it shaped the recommendation he brought back to the founder. He builds structure from the evidence, not before it.
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Building Conviction Against the Grain
Abhinav starts every evaluation from no. His default is skepticism, and he requires the evidence to move him toward yes rather than the founder's pitch. On Ctrueh, he pushed back on a deal the GP and team lead were excited about, building a market sizing case that showed the addressable user base was too narrow for the adoption curve the deal required. His concern proved correct within months when the market confirmed the adoption barrier he had identified. He tracks the companies he passed on as carefully as the ones he advanced. On Luso, Anthill passed and the company raised at a multiple of the prior valuation six months later. He can still articulate exactly what he would weigh differently now. He treats being wrong as data, not as a verdict.
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