Aishwarya's Profound AI Rep
She builds views from behavioral signals and operates with a structured AI research practice.
Aishwarya Kandukuri
Edges
Behavioral Research and Insight Synthesis
Aishwarya's core research method starts with behavioral data and user interviews, not assumptions. She surfaces what users actually do, then builds the case from there. This pattern showed clearly in the desktop product story at her current startup, where she combined usage data with qualitative interviews to challenge a mobile-first assumption held across the team. Her approach to stakeholder alignment follows the same logic: she maps the existing assumptions first, then uses evidence to reframe the conversation. The proposal iterates as new data comes in, not as opinions shift. For a VC research context, this translates to a researcher who builds views from primary signals rather than received wisdom.
Spotted in 2 Stories
Zero-to-One Consumer Product Building
Aishwarya has built consumer products from scratch across two distinct contexts: CRED Mint in fintech and her current work at a stealth consumer AI startup. Both required owning the product definition before there was a product to iterate on. At CRED, she launched CRED Mint from zero, taking the product through the full arc from brief to live consumer offering. At her current startup, she joined as a Founding Member, owning product and GTM from the earliest stage. The pattern across both is the same: she operates well in environments where the answer is not yet known and the research has to inform the build, not follow it. For a VC context, this is a researcher who understands what zero-to-one actually requires, from the inside.
Spotted in 2 Stories
AI-Augmented Research Practice
Aishwarya has built a structured AI workflow into her research practice, using Claude to synthesize sector reading and generate daily digests across the topics she tracks. The workflow is not a shortcut; it is a system for maintaining depth across multiple research arcs simultaneously. She reads extensively across social media, food delivery, and consumer behavior, and uses the AI layer to surface what matters each morning without losing the analytical thread. The judgment stays hers. The AI handles aggregation and synthesis; she handles interpretation and the calls that follow. For a research role that requires staying current across sectors at speed, this is a practiced capability, not an experiment.
Spotted in 1 Story