Kartik's Profound AI Rep
Kartik works across the full capital-raising cycle, from evaluating opportunities to structuring narratives and managing investor relationships.
Kartik Sodhi
Edges
Managing High-Stakes Investor Relationships
Kartik works with fund managers and limited partners where the stakes are high and the outcomes are rarely certain. His role at Finex involves weekly calls with GPs managing billions in AUM, where maintaining confidence and clarity is as important as the fundraising activity itself. This pattern runs across his career. At the boutique firm, he managed HNI and angel investor relationships through the full fundraising cycle. At Bespoke, he held the manager-side relationship from first contact through pipeline qualification. What makes this consistent is his ability to **separate what is in his control from what is not**, and to communicate that boundary clearly to clients without damaging the relationship. The Australian healthcare VC situation is a direct example: a roadshow with no closes, a frustrated client, and a reset that kept the engagement alive. The result is a track record of relationships that survive difficult outcomes and move forward on revised terms.
Spotted in 3 Stories
Structuring Investment Narratives from Raw Data
Kartik takes unstructured information and turns it into a story an investor can act on. At the boutique firm, most startups arrived with data but no coherent narrative: some had never built a pitch deck, others had materials too dense to follow. His job was to identify what mattered, cut what did not, and sequence the rest into a clear investment case. The framework he applied consistently covered problem, solution, founder pedigree, TAM, realistic market capture, revenue or traction, and early user feedback. Each element had to earn its place by answering a question an investor would ask. This is not just deck production. It is **judgment about what a specific investor audience needs to see** to move from interest to commitment. That judgment was applied across multiple mandates, with check sizes up to 1 crore. The same structuring instinct shows up in his fund placement work, where he prepares DDQs and update materials that give LPs a clear picture of fundraising progress.
Spotted in 2 Stories
Evaluating Early-Stage Investment Opportunities
Kartik has screened early-stage businesses across two contexts: startups seeking fundraising support at the boutique firm, and fund managers seeking placement at Bespoke Connections. In both cases, he applied a structured evaluation before committing the firm's resources. For startups, his framework covered founder conviction, problem clarity, TAM, traction, revenue, and user base. For fund managers, the key filter was first-close timing: managers approaching their first close are most motivated to engage, making them the highest-probability targets for placement outreach. What connects these is a **consistent instinct to qualify before committing**, rather than pursuing every inbound opportunity. The Pets of Paradise onboarding decision is a direct example: he made the internal case based on specific signals, not general enthusiasm. This evaluation discipline is still developing into a more formal research practice, but the underlying judgment is already present across his work.
Spotted in 2 Stories
Building AI-Assisted Research Tools
Kartik builds functional tools using AI coding assistants, independently and outside of a formal engineering role. LoanReady, a web application that automates bank file preparation for chartered accountants, was built using Claude Code, Gemini, and OpenAI Codex. It solves a real workflow problem in the CA space and is already functional. He is also developing a private company intelligence tracker that pulls data from GST filings and other public sources to generate timely updates on unlisted companies, a tool directly relevant to private markets research. What this demonstrates is a **willingness to move into unfamiliar technical territory** using AI tools as an accelerant, rather than waiting for engineering support. The output is working software, not prototypes. This is an emerging capability, but the pattern of identifying a real problem and building toward a solution independently is already established.
Spotted in 1 Story