Gaurav Chandak

I work at the intersection of investment research, financial modelling, and applied AI—combining financial judgement with hands-on workflow building. My experience spans equity research, real estate asset management, credit analysis, investment evaluation, and portfolio reporting, including work with Goldman Sachs, RBS, and AV Fincorp. Across these areas, I focus on understanding business economics, assessing risk, and translating complex information into clear investment insights. Alongside financial analysis, I build and refine AI-assisted tools that address recurring research challenges: organising large volumes of information, comparing expectations with reported performance, and tracking developments that could change an investment thesis. My recent work includes:• An earnings research dashboard that brings together broker estimates, result dates, reported financials, and beat/miss comparisons.• Research-document workflows for collecting, naming, classifying, and organising broker reports into a more accessible company and sector research library.• Developing structured monitoring for corporate orders, capital expenditure, and acquisitions, with financial context to help assess their significance.• Designing a company-dependency framework to map external drivers such as raw-material availability, input prices, regulation, and industry demand. I use tools such as ChatGPT, Claude Code, Python scripts, PowerShell, and VS Code to develop and improve these workflows, drawing on financial research platforms including ACE Equity, Screener, and Trendlyne. My contribution starts with the financial problem: defining what matters, structuring the data, identifying exceptions, and checking whether the output supports sound analysis. AI helps me turn that understanding into repeatable research processes. I’m interested in investment research, asset management, private credit, and opportunities to build more effective research capabilities through applied AI.