Vishva's Profound AI Rep
Vishva turns raw data and founder conversations into structured, decision-ready research.
Vishva V
Edges
Financial Pattern Recognition
Vishva reads patterns in raw, unstructured data and connects them to the intention behind the numbers. This is not surface-level analysis; it is the discipline of asking why a transaction, a market, or a business is structured the way it is. At Standard Chartered, he ran this daily, processing raw transaction data, spotting structuring schemes like cuckoo smurfing, and writing the reports that went to regulators. At AIC, the same instinct shows up when he pushes founders on the market questions they haven't asked themselves. The mechanism is consistent: start from the macro context, drill to the individual data point, and surface the narrative that connects them. He does this across financial crime, market research, and investment evaluation. For a research role, this means he arrives at a thesis by working through the evidence, not around it.
Spotted in 3 Stories
Founder Market Pressure Testing
Vishva's instinct with founders is to find the question they haven't asked themselves. He approaches every founder conversation with the assumption that the product thesis is incomplete until the market reality has been stress-tested. With Griffin AI, he identified that the pilot environment was the wrong test for the product and connected them with a mentor network to run a more revealing one. With Scipla AI, he pushed on willingness-to-pay before the founder had modeled it. With Simulit, he rebuilt their pitch deck when their market framing didn't hold up. The pattern is consistent: he lays out the facts, names the gap, and lets the founder make the call with better information. He doesn't push for a pivot; he pushes for clarity. This makes him useful in early-stage research where the brief is often vague and the market is poorly mapped.
Spotted in 3 Stories
Building Research Infrastructure
Vishva builds the systems that make research reproducible. At AIC, he designed a Notion-based portfolio monitoring database covering 40+ startups, structured so any team member can pull a founder's full history without reconstructing context from email threads. At Standard Chartered, the same discipline showed up in daily report production: raw transaction data in, structured MLRO-ready report out, four times a day, every day for one and a half years. The common thread is that he designs for the next person, not just for himself. The output is always structured to be queryable, auditable, and handoff-ready. For a research role, this means he doesn't just produce findings; he builds the workflow that makes findings usable over time.
Spotted in 2 Stories