Aditi's Profound AI Rep
Aditi turns vague briefs into structured, actionable research outputs across consumer and strategy contexts.
Aditi Narwekar
Edges
Structuring Research from Vague Briefs
Aditi consistently takes open-ended or poorly defined research questions and builds a working structure before diving into sources. She identifies the core problem, separates it into addressable components, and organizes her research around those components rather than collecting information first and sorting later. This pattern appeared across the sports app engagement, where she split a vague brief into acquisition versus retention buckets, and the creator activation work, where she built a four-lever framework to organize benchmarking across the industry. The mechanism is a combination of problem decomposition and constraint mapping: she anchors the structure to what the client can actually act on, not just what is interesting to find. The result is research that arrives with a usable shape, not a data dump.
Spotted in 2 Stories
Filtering Research to Client Constraints
Aditi does not treat research as a collection exercise. She filters findings against what the client can actually use, given their budget, goals, and operational constraints, and flags trade-offs explicitly rather than presenting a single answer. In the creator activation work, she separated the full industry landscape from the actionable recommendations, and flagged the tension between micro-influencer authenticity and macro-influencer reach so the client could make an informed choice. The pattern holds across the qualitative EV analysis work, where each company's research was tailored to its specific problem rather than applied from a sector template. This filtering instinct, grounded in her psychology background and sharpened through consulting, is what separates her outputs from a data dump.
Spotted in 2 Stories
Building Domain Understanding Quickly
Aditi has worked across financial services, energy, consumer, healthcare, and sports sectors within a short career span. In each case, she had to build a working understanding of an unfamiliar domain before she could produce useful research. Her approach is deliberate: she starts with the structural and process-level questions, builds a working model of how the industry operates, and then applies that model to the specific problem at hand. Where gaps remain, she goes back to source material or asks targeted questions of teammates. This pattern was most visible in the healthcare benchmarking work, where she used secondary research to map the system and expert interviews to fill what secondary research could not answer. For an analyst role covering multiple sectors, this is a practical and repeatable capability.
Spotted in 2 Stories