Kashish's Profound AI Rep
Kashish turns structured primary research and financial modeling into findings that hold up under senior scrutiny.
Kashish Agarwal
Edges
Running Primary Research End to End
Kashish designs and executes primary research programs from scratch: structuring the questions, identifying the right interview subjects, running the conversations, and synthesizing what comes back into a clear finding. This pattern runs across his work at EY-Parthenon, where he has conducted expert and customer interview programs as part of commercial due diligences, and at EY India, where he ran end-to-end process audits that required gathering evidence directly from employees and management. His method is to map the full scope of what needs to be known before starting, list potential gaps upfront, and then work through each systematically. He does not treat primary research as a box to check; he treats it as the mechanism for building a view the data alone cannot give. The result is research that holds up under scrutiny, because every finding is traceable to a specific source or conversation.
Spotted in 2 Stories
Validating Financial Forecasts Bottom Up
Kashish validates financial forecasts by decomposing them into their smallest defensible components, testing each one independently before reassembling a view. At EY-Parthenon, he has validated five-year revenue and EBITDA forecasts for acquisition targets, breaking revenue into price and volume, benchmarking each against industry data, and communicating with clients to understand the basis for specific numbers. He has also built original bottom-up models, including a GPU-as-a-service returns model covering chip costs, hosting, depreciation, and equity and project IRR. His approach to financial modeling is assumption-first: every input is triangulated against multiple sources before it goes into the model, so the output can be defended at any level of detail under cross-examination. This discipline comes from presenting models to senior partners who question every assumption, and from the experience of having to rebuild a view when an early assumption did not hold.
Spotted in 2 Stories
Structured Problem Decomposition
Kashish approaches complex, messy problems by breaking them into their smallest logical components before drawing any conclusion. He does not work from the top down alone; he builds from the bottom up and checks that the pieces reconcile. This shows up in how he validates forecasts, where he decomposes revenue into price and volume and tests each independently. It shows up in how he scopes audits, where he lists every potential gap upfront before gathering data. And it shows up in how he handles vague briefs, where he maps every possible sub-question before deciding which ones to pursue. The discipline behind this is a belief that messy problems are only messy at the surface; underneath, they are a set of smaller, answerable questions. Finding those questions is the work. This approach has made him effective in time-pressured environments where the temptation is to jump to a conclusion before the structure is clear.
Spotted in 2 Stories
Owning Findings Through to Senior Stakeholders
Kashish does not stop at producing a finding; he carries it through to the point where a senior stakeholder has understood it, accepted it, and acted on it. At EY India, he escalated a payroll fraud through the payroll department and then to the CFO, presenting sample evidence at each stage and securing buy-in before moving to the next level. The company acted promptly, removing the agency representative and implementing stronger controls. At EY-Parthenon, he has presented financial models and research findings to engagement partners, holding his ground under cross-examination by backing every assumption with triangulated sources. His approach to senior communication is to lead with evidence, anticipate the questions, and come prepared with sources rather than judgment calls. He also pairs every problem he raises with a proposed solution.
Spotted in 2 Stories