Jai's Profound AI Rep
Jai builds and defends investment research end to end, from primary data through valuation to IC presentation.
Jai Bachhawat
Edges
Building IC-Ready Investment Research
Jai builds investment research from the ground up: primary data gathering, market sizing, company analysis, valuation, and IC presentation. He does not rely on a single method or a single data source. This pattern has shown up at UCEA, where he owned the full investment memorandum on a $95M pre-money infrastructure deal, and at Fine Threads, where he structured primary research across three distinct stakeholder groups before building the business plan he used to raise capital. His approach to valuation is deliberately non-traditional when the situation calls for it. For pre-revenue or loss-making companies, he moves beyond standard multiples to methods like RNTV analysis and creative comparable frameworks using non-financial metrics. The result is research that holds up under IC scrutiny, including live pushback on assumptions, market sizing, and execution risk.
Spotted in 3 Stories
Startup Valuation Across Deal Types
Jai applies valuation methods across a range of deal types and company stages, adapting the approach when standard methods do not fit the evidence. For pre-revenue or loss-making companies, he moves beyond EV/Revenue multiples to methods like RNTV analysis, probability-weighted outcomes, and non-financial multiples such as EV per daily active user. He has applied this across infrastructure, med tech, and biotech deals at UCEA. He also valued and negotiated the acquisition of his own company, Fine Threads Apparels, without external advisory support, arriving at a price that returned 53% to investors. His valuation work is built to be defended: he pre-aligns assumptions with senior managers before IC, and he has held his positions under live pushback on methodology, market sizing, and execution risk.
Spotted in 3 Stories
Building Finance Automation Tools
Jai builds automation tools that remove repetitive work from finance and research workflows, using Python and API integrations. At PwC, he built a Streamlit-based web platform for a MedTech client that replaced a static Excel email chain with real-time deal visibility, cutting the sales closing timeline by two weeks. At UCEA, he automated portions of the investment memorandum process by connecting to PitchBook via API, and built outreach automation tools for co-investor engagement. The pattern is consistent: he identifies a repetitive or bottlenecked process, builds a tool that removes the friction, and makes it available to colleagues, not just himself.
Spotted in 1 Story
Sector Framing for Deal Prioritization
Jai forms independent sector views from live deal work and uses them to structure how he allocates research time across a high-volume deal flow. Working across 20 to 30 pitch decks a day at UCEA, he developed a framework distinguishing first-order AI infrastructure companies from second and third-order application companies that were already generating cash flows. He used this to prioritize which deals to take into deep review first. The view was formed from pattern recognition across live deals, not from secondary reading alone. It shaped which data rooms he accessed earlier and which deals he deprioritized in the initial triage. This kind of sector framing, built from deal evidence and applied to live prioritization decisions, is the practical version of the capability.
Spotted in 1 Story