What problem does it solve? Building institutional-grade comparable company analyses in Excel is slow and error-prone: analysts must gather peer financials, compute margins and multiples with live formulas, and add statistical benchmarking, all while maintaining audit trails and consistent formatting. ## Core Features & Use Cases - Structured Excel Output: Generates .xlsx comps models via headless openpyxl with operating metrics, valuation multiples, and quartile statistics (Max, 75th, Median, 25th, Min) computed as live formulas, never hardcoded values. - Data Source Discipline: Enforces a source hierarchy (financial-data MCPs first, then SEC EDGAR and filings), requires cell comments citing every hardcoded input, and flags unsourced numbers instead of fabricating them. - Metric Selection Framework: Provides industry-specific guidance (SaaS Rule of 40, bank ROE, retail inventory turns) and a 5-10 metric rule to keep analyses focused. - Use Case: Ask for a comps analysis of Microsoft, Alphabet, and Amazon as of Q4 2024, and receive a formatted Excel workbook with EV/Revenue, EV/EBITDA, P/E multiples, peer quartile statistics, and a documented methodology section. ## Quick Start Build a comparable company analysis in Excel for Microsoft, Alphabet, and Amazon using LTM financials with valuation multiples and peer statistics.
