Sanjeevni's Profound AI Rep
Sanjeevni turns ambiguous briefs and messy data into structured, decision-ready outputs across research and operations.
Sanjeevni Bhardwaj
Edges
Structuring Research from Ambiguous Briefs
Sanjeevni builds research frameworks when the brief is vague, the market is poorly mapped, or no precedent exists. She does not wait for a template; she constructs one from first principles. At OneBridge, she designed a vendor evaluation questionnaire from scratch, grounding it in an analysis of the firm's current services, peer offerings, and future potential before a single call was made. At Transcom, she processed a corporate action type her team had never encountered, reading the source documents directly and building the notification structure herself. Her approach is to anchor the framework in the closest available evidence: partial comparables, sector reports, source documents, or stakeholder interviews. She then structures the output so a decision-maker can act on it without needing to re-examine the raw material. The pattern holds across contexts: she moves from ambiguity to a structured, decision-ready output by doing the foundational work herself.
Spotted in 2 Stories
Synthesizing Complex Data into Decision Outputs
Sanjeevni takes high-volume, scattered, or technically dense data and structures it into outputs that decision-makers can act on directly. The work is not just cleaning; it is translating. At OneBridge, she received approximately 1,000 entries of scattered competitor and financial data, much of it in image format, and compiled it into a structured Excel model with pivot tables. The output fed directly into the information memorandum and pitch deck used to close a four-company merger. At Xceedance, she handled cash applications across thousands of transactions and audited billing entries during quarter-end close cycles, maintaining accuracy under volume and deadline pressure. Her consistent approach is to use available tools, including AI where appropriate, to move through volume, then apply manual review to catch what the tools miss. The output is always structured for the next person in the chain, not just for her own use.
Spotted in 2 Stories
Building Process Infrastructure from Gaps
Sanjeevni identifies where a process is exposed, designs a fix, and builds it into the workflow so others benefit from it. She does not flag problems; she closes them. At Transcom, she identified that AGM attendance card submissions had no verification step despite carrying a per-submission financial cost. She designed and built a three-eye tracker that required both an L1 agent and an L2 support staff member to verify information before dispatch. After introduction, the team processed all attendance cards without errors. At Xceedance, she supported reporting tool development and coordinated across five cross-functional teams within the credit control function, building operational visibility into a high-volume processing environment. The pattern is consistent: she sees the gap, builds the system, and measures whether it works.
Spotted in 2 Stories