Tushar's Profound AI Rep
Tushar turns messy data and vague briefs into structured, defensible research across deals and sectors.

Tushar Vigh
Edges
Tushar's consistent pattern is taking unstructured, incomplete, or contradictory data and building a defensible analytical structure from it. At JPMorgan, he cleaned multi-year client data dumps, reconciling bookings, backlogs, and sales cycles across products and customer segments before any model could be built. At Paytail, he worked through a loan compounding problem on paper before translating it into code and shipping it. His approach is to understand what the output needs to look like before deciding how to cut the input. He segments by the dimensions that matter to the stakeholder, not by what is easiest to extract from the data. The result is research that holds up under scrutiny, because the structure was built around the question, not around the data that happened to be available.
Spotted in 3 Stories
Tushar has developed a practical ability to manage the information gap between buyers and clients in live M&A transactions, a gap that is rarely clean and always time-sensitive. At JPMorgan, he managed due diligence flows where buyers asked multi-part questions and clients were selectively evasive. He learned to sequence disclosures, surface partial answers to keep buyers engaged, and negotiate with clients on what could be shared without jeopardizing the process. When a reconciliation error surfaced during a buyer call, he did not escalate or deflect. He identified the error, fixed it, and sent the buyer a reconciliation map that let them work through the correction themselves, while also revealing how they were thinking about the numbers. His instinct is to find the middle path: give stakeholders enough to stay engaged, protect the client's position, and keep the process moving.
Spotted in 1 Story
Tushar has built a sourcing system from scratch, mapping a startup landscape sector by sector and filtering by synergy, founder quality, and deal fit for a corporate venture arm. At Alteria Capital, he used LinkedIn, Crunchbase, and startup news platforms to identify Indian startups across security, mobility, and infrastructure verticals. He developed a multi-factor grading approach, assessing commercial synergy first, then deal size fit, then founder background and prior institutional backing. His sourcing was methodical: pick a sub-sector, map the companies, go deep on each to assess whether the synergy was real, then build a pitch for the stakeholder that explained why the match was non-obvious. One of the startups he sourced was later flagged by his partner as a live investment prospect.
Spotted in 1 Story