Vipul's Profound AI Rep
Vipul turns ambiguous briefs and messy data into structured, evidence-backed research across markets.
Vipul Sahu
Edges
Building Market Sizing Models in Data-Sparse Markets
Vipul builds market sizing models in markets where reliable data is scarce, using a combination of primary research, alternative data sourcing, and iterative pressure testing to arrive at numbers that hold up. He demonstrated this in the Iraq retail banking study, where the first model failed against primary research assumptions and he rebuilt it from scratch using World Bank data, executive interviews, and deep secondary research. His approach runs both bottom-up and top-down models simultaneously, then triangulates against qualitative findings until the outputs converge. The discipline is in knowing when to discard a model rather than patch it.
Spotted in 2 Stories
Designing and Running Primary Research End to End
Vipul designs and executes primary research from scratch, including questionnaire design, stakeholder sourcing, data collection, coding, and synthesis into structured findings. He has run this loop across retail banking in Iraq, packaged snacks across Indian cities, and consumer electronics markets, each time owning the full process from brief to deliverable. His synthesis approach involves coding qualitative data into themes, then interpreting findings through multiple lenses, including investor, analyst, and client perspectives, to surface what actually matters for the decision at hand.
Spotted in 2 Stories
Sector Tracking with Independent Conviction
Vipul forms independent views on sectors by tracking benchmarks, investment activity, supply chain dynamics, and regulatory developments, rather than synthesizing consensus. He has formed grounded views on the AI valuation landscape, the competitive threat from open-source Chinese models, and the structural constraints on India's semiconductor ambitions, each backed by specific data points he tracks regularly. His monitoring approach combines Google Alerts, Screener, and Crunchbase with benchmark platforms, and he focuses on underlying shifts in customer behavior, technology viability, and regulation rather than surface-level news.
Spotted in 2 Stories
Building AI-Augmented Research Workflows
Vipul builds research workflows that use AI to accelerate output without removing the verification step, treating AI as a parallel workstream rather than a replacement for judgment. He built a custom AI research agent using an open-source model, programmed to his specific workflow requirements, and uses it to run deep research prompts while he works on other tasks simultaneously. His approach is to cross-verify every AI output against multiple sources before incorporating it, maintaining the rigor of the research while compressing the time required.
Spotted in 2 Stories