Factor-First AI Investment Analysis Narrated by a Six-Persona Committee
Built a Quality + Valuation + Growth + Momentum + Health factor composite narrated by a six-persona committee — one consistent framework across every public equity.
The Problem
Standard brokerage tools expose raw financial metrics but do not synthesize them into a conviction-level judgment. Investors still do their own mental weighting, often inconsistently and without a documented framework.
The core gap is not data access; financial APIs are commodity. The real problem is a missing, repeatable scoring architecture that combines quantitative fundamentals with qualitative moat assessment.
The platform also had to be robust enough for real decision-making with personal capital, not a demo workflow.
The Approach
Moved from a Graham-centric parallel-scorer committee to a factor-first composite (ADR-011): Quality, Valuation, Growth, Momentum, and Health each score independently and aggregate into a single composite.
Built a six-persona investment committee that narrates the factor scores rather than recomputing them in parallel — each persona owns a factor lens and explains the reading in their philosophical voice.
Added Piotroski F-Score and Altman Z-Score as diagnostic gates that surface independently, not as factor bars, so a distress zone hard-caps the composite instead of being averaged away.
Pulled quantitative signals from financial data APIs and delegated committee narration to Claude via AWS Bedrock using a constrained prompt that returns structured verdicts rather than open-ended commentary.
Grounded the LLM with factual financial context at inference time and added public no-login access with rate limiting at 10 requests per IP per hour.
The Impact
- Produces a repeatable composite score (0-100), letter grade (A+ to F), and a factor-first view with persona narration underneath
- Makes ticker-to-ticker comparisons meaningful through one consistent factor framework
- Improves explainability by showing which factor drove conviction up or down and which persona narrated it
- Grounded moat and momentum narration align closely with independently formed investment theses for active portfolio management