Riddhi's Profound AI Rep
Riddhi's view on reading data shows how she separates surface numbers from what the market actually shows.
Riddhi Sharda
Edges
Primary Research Validation
Riddhi's core research instinct is to treat a data discrepancy as a question, not a rounding error. When the numbers don't hold up, she goes back to the source. This pattern has shown up across her EdTech consumer due diligence, where she caught an affordability perception gap through primary callbacks, and her agri-tech market sizing work, where she validated a rebuilt methodology through expert interviews before presenting it to investment bankers. Her approach combines survey design, in-depth interview execution, and data triangulation. She does not stop at what secondary sources say; she builds the primary layer that makes the finding defensible. For a team building a live market view, this means the research that comes from her has been stress-tested before it lands.
Spotted in 2 Stories
Structuring Ambiguous Market Briefs
When a research brief is vague or the market is poorly mapped, Riddhi builds structure from what is available rather than waiting for a cleaner starting point. Her approach is to run her own secondary diligence first, identify what can be validated through primary, and then align with the stakeholder on scope when genuine ambiguity remains. On the agri-tech IPO engagement, this meant rebuilding the methodology entirely when the first approach failed to capture the full market scope. She has applied this across IPO DRHP industry reports and commercial due diligence engagements, where the brief often requires the analyst to define the structure as much as execute it. For a fund building sector views from scratch, this is the instinct that turns an open question into a usable framework.
Spotted in 1 Story
Rapid Sector Immersion
Riddhi has built working knowledge of multiple sectors from scratch across her engagements: EdTech, agri-tech, BFSI, healthcare, and technology internet. Each project required her to develop enough sector depth to write a credible industry narrative, conduct expert interviews, and present findings to bankers or PE investors. Her approach to staying current combines macro-level reading with project-level synthesis. She tracks how macro indicators connect to specific companies and sectors, and she builds that context into her research outputs. This pattern is visible across her IPO DRHP work, where she has had to construct industry narratives for sectors she was not previously expert in, and her commercial due diligence work, where sector context shapes the validity of every finding. For a fund covering consumer, frontier tech, and industrial sectors, this is the ability to get up to speed fast and produce credible work without a long ramp.
Spotted in 2 Stories
Riddhi Sharda
About Me
Riddhi Sharda is a Business Analyst at 1Lattice, accelerated to the role within 1.5 years (among the top performers in her cohort) for consistently delivering client-ready market research, strategy, and due diligence outputs across five sectors. She works across ECM, Technology & Internet, BFSI, and Healthcare verticals, leading research engagements spanning market sizing, IPO advisory, and commercial due diligence.
She manages end-to-end research workstreams, from defining methodologies and conducting primary research to financial benchmarking, data analysis, and client-facing delivery. She has supported 30+ IPO/QIP industry reports and delivered 15+ industry research engagements, leveraging tools such as Excel, and GenAI to improve analysis efficiency and reduce turnaround time.
She has led cross-functional collaboration with investment bankers, internal leadership, and research teams while managing quality assurance and timely delivery of client outputs. She has also driven capability-building initiatives by developing standardized research frameworks, reusable knowledge sources, macroeconomic databases, and relationship repositories that improved execution efficiency across engagements.
Riddhi Sharda
A number that looks right on the surface can be completely wrong once you understand the sales funnel behind it.”
Riddhi Sharda
On reading data correctly