Krishnam's Profound AI Rep
Krishnam turns vague briefs into structured research and actionable decisions across product and venture.

Krishnam Gupta
Edges
Krishnam's default when handed a vague problem is to go to the source before touching data or building anything. He runs user calls at volume, reads institutional research, and speaks to founders to build a ground-truth view of what is actually happening. This pattern has shown up across bequant.dev, Navi, and Eximius Ventures. At bequant, over 200 user calls surfaced a non-obvious insight about user comprehension that no dashboard would have caught. At Navi, the same approach turned a one-line retention brief into a structured experiment. At Eximius, it produced a published investment thesis. He synthesizes across sources rather than anchoring to one. Primary and secondary research inform each other in his process, and the output is always a structured perspective, not a data dump. The result is that his findings tend to be actionable: they point to a specific lever, not a general observation.
Spotted in 3 Stories
Krishnam's first move when handed a vague brief is to resist the pull toward solutions and instead make the problem concrete. He clarifies the outcome, maps the current state end-to-end, identifies the bottleneck, and prioritizes by impact and effort before touching a solution. This approach has shown up consistently across different contexts. At Navi, a one-line retention brief became a structured research program. At Ola Krutrim, a fragmented sales process became a scoped automation project with a clear success metric. At bequant.dev, a general drop-off problem became a specific hypothesis about user comprehension. He distinguishes between product thinking and project thinking: the goal is to understand the end outcome before mapping the how. This keeps him from solving the wrong problem at speed. The output of his structuring process is always a prioritized set of testable hypotheses, not a project plan.
Spotted in 3 Stories
Krishnam builds market convictions from the ground up, combining data, institutional research, and primary conversations rather than anchoring to received wisdom. At Eximius Ventures, he synthesized founder calls and VC primers into a published thesis on AI in wearables. At Ola Electric, he read India's two-wheeler sales data and formed a conviction that electric bikes would drive the next wave of EV penetration. What distinguishes his approach is the willingness to test the conviction against disconfirming evidence. At Navi, he held a strong hypothesis about fractional cashbacks as a retention driver, then updated his view when the data pointed elsewhere. He is flexible about the hypothesis but not about the evidence. This pattern is early in its development: he has formed views and acted on them, but the track record of those views being validated at scale is still building. For a research-oriented role, the discipline of forming, testing, and updating a market view is the relevant signal.
Spotted in 2 Stories
Krishnam takes on workstreams from brief to delivery, including the problem framing, the build, and the measurement. He does not hand off at the point of insight or at the point of specification; he stays through to the outcome. At bequant.dev, he was the first product hire and owned the full arc from user research to feature design to launch. At Ola Krutrim, he owned the GPU pipeline automation from problem mapping through classifier design, questionnaire logic, and dashboard delivery. At Navi, he ran the research program and the experiment. This pattern is partly a function of the early-stage environments he has worked in, where there is no one else to hand off to. But it has also shaped how he approaches problems: he thinks about the full chain from finding to action, not just his piece of it. For a research role, this means his outputs are designed to be acted on, not just read.
Spotted in 2 Stories