Tanvi's Profound AI Rep
Tanvi turns primary research into structured decisions across private deals and public market analysis.
Tanvi Prakash
Edges
Financial Due Diligence
Tanvi's diligence work is built around assumption auditing: she checks whether the numbers a company presents are grounded in their own operating reality or borrowed from elsewhere. This pattern showed up in her deal work at Global Bharat Fund, where she caught a UK startup modeling financials from peer companies rather than their own unit economics, and in her Hero MotoCorp research, where she validated management assumptions against external market evidence before building her model. Her process runs from data structuring through trend analysis, ratio analysis, and scenario construction. She builds the analytical foundation before forming a view, rather than fitting data to a conclusion. The result is diligence that surfaces what is actually true about a business, not what the pitch deck claims.
Spotted in 3 Stories
Top-Down Market Research
Tanvi approaches market research by starting with the macro picture and working down to the company: sector dynamics, competitive landscape, demand signals, and then the specific investment question. This showed up in her Hero MotoCorp work, where she mapped the automotive market before building the DCF, and in her defense sector research, where she mapped procurement mechanics and armed forces demand before evaluating individual companies. Her contrarian call on Hero MotoCorp illustrates the depth of this approach. She looked past headline EV disruption narratives, examined rural demand patterns and monsoon cycle data, and identified a mispricing in how the market was valuing the EV business. The output is always structured for a decision: a memo, a model, or a published thesis that others can act on.
Spotted in 3 Stories
Building Deal Pipeline Systems
Tanvi joined Global Bharat Fund when the firm was new and the operational infrastructure did not yet exist. She built the deal pipeline trackers, due diligence data rooms, and KPI tracking systems from scratch. The systems she built support a pipeline of over 20 startups across defense tech, AI, and HR tech. They give the team a structured way to track which founders are progressing, what diligence has been completed, and where gaps remain. This is early-stage operational work: building the scaffolding that makes a small investment team function at scale. The pattern extends to her research outputs, where she consistently structures findings into memos and models that others can use rather than leaving analysis in raw form.
Spotted in 2 Stories