Harshit's Profound AI Rep
Harshit turns primary research and independent financial modeling into investment conviction.
Harshit More
Edges
Building Primary Research Processes
Harshit does not treat third-party reports or founder narratives as the final word on a market. He designs and runs his own research processes to get to ground truth. Across Elevate Now and Stage, he went directly to the people closest to the subject: drug researchers at Eli Lilly and Novo Nordisk, IMDb reviewers, platform users, and colleagues. Each time, the primary research surfaced something the secondary sources missed. On Elevate Now, SME interviews revealed that Indian drug pricing would be far lower than US levels, a key input to the market viability thesis. User surveys he designed revealed that price was not the retention risk; coach consistency and post-program support were. He approaches research design with a clear question in mind, structures the inquiry to surface both common patterns and demographic differences, and compiles findings into actionable outputs rather than raw data.
Spotted in 2 Stories
Building Independent Unit Economics Models
Harshit builds his own financial models even when third-party analysis is available. He treats external reports as a starting point, not a conclusion, and verifies the numbers from his own analytical frame. On Elevate Now, the deal had a third-party financial due diligence process running in parallel. He still built an independent CAC-LTV model because marketing was the dominant cost driver and the unit economics were the crux of the investment thesis. The model covered current ratios, historical trends, and projected improvement as the platform scaled and Meta ad dependency reduced. On Huddle, he analyzed daily, weekly, monthly, and annual active users alongside booking frequency and renewal rates to assess whether the platform was building genuine user habits across both its business lines. His approach is to identify the one or two metrics that determine whether the business model works, then build the analysis around those specifically rather than producing a comprehensive model that buries the signal.
Spotted in 3 Stories
Building Stakeholder Conviction on New Markets
Harshit builds IC conviction by layering multiple types of evidence into a single coherent narrative, rather than presenting analysis as a data dump. On Elevate Now, two of three IC partners were skeptical about investing in a market with no regulatory framework. He and the team built the case across three layers: the global track record of GLP-1 drugs, primary SME interviews with researchers at Eli Lilly and Novo Nordisk, and direct user survey data. The combination moved the IC from doubt to conviction. On Stage, the IC questioned the ceiling of the regional OTT market. He addressed this with a bottom-up market sizing model combined with IMDb review data and direct product usage, presenting the evidence as a narrative rather than a set of disconnected analyses. His approach is to identify the specific objection the IC is likely to raise, then build the evidence directly against that objection rather than presenting a comprehensive memo and hoping the IC finds the relevant section.
Spotted in 2 Stories