Ayush's Profound AI Rep
Ayush builds investment conviction from first principles and structures it into outputs decision-makers can act on.
Ayush Gupta
Edges
Building Investment Conviction from First Principles
Ayush does not accept a company's stated narrative at face value. He works backward from the numbers until the underlying assumptions are visible, then stress-tests those assumptions against real data. This pattern has shown up across credit analysis, M&A due diligence, and distressed company research. On a cross-border acquisition, he identified that half the projected revenue rested on unsupported adoption assumptions and rebuilt the model from actual retention data. On a distressed pool company, he mapped misallocated debt spend to an operational gap the financials alone would not have surfaced. His method is to move from quantitative to qualitative, using the numbers to form a hypothesis and the qualitative evidence to confirm or challenge it. The result is analysis that holds up when a client pushes back. For a team that needs research to drive real investment decisions, Ayush brings the discipline to find what the numbers are actually saying.
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Building AI-Augmented Research Workflows
Ayush has moved beyond using AI as a writing aid. He builds automation agents that replace multi-step manual processes, cutting hours of process work to minutes and redirecting his team's time toward analysis and decision-making. He built a Shortcut AI automation agent that reduced a three-hour debt deal scoping process to under fifteen minutes. He applies the same approach to financial spreading and document review, using AI to handle the structured extraction so he can focus on the growth assumptions and qualitative judgment that require human reasoning. The result is a measurable shift in where research time goes: less on compiling, more on interpreting. For a team that wants to move faster without sacrificing rigor, Ayush brings both the technical fluency to build the workflow and the analytical judgment to use the time it creates. He uses Shortcut AI, ModelML, and Claude across his current research stack.
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Structuring Research into Decision-Ready Outputs
Ayush builds research reports that move from industry benchmarks to financial performance to qualitative credit factors in a structured sequence, so the decision-maker can follow the logic without having to reconstruct it. He developed this approach for a US-based CLO debt fund client, where the research process needed to surface leverage risks clearly and connect them to the company's credit position. The same structure applied to the distressed pool company, where scattered financials and qualitative signals needed to be organized into a single coherent finding. His reports are built around a consistent architecture: industry context first, financial performance second, qualitative factors third. Within that, he focuses on the specific KPIs that matter for the investment type, whether leverage ratios for debt deals or customer retention for acquisition due diligence. For a team that needs research outputs a decision-maker can act on directly, Ayush builds the structure that makes that possible.
Spotted in 2 Stories