MOTU.ai

Overview

Designed and built MOTU.ai: a financial intelligence platform that tracks, ranks, and analyzes the portfolio activity of top institutional money managers using SEC 13F filing data. Surfaces high-conviction signals, MOTU Score rankings, stock-level institutional sentiment, and a Strategy Lab for backtesting portfolios against the smart money.

Objectives

• Build a platform that makes SEC 13F institutional filing data actionable for retail and professional investors
• Design a MOTU Score ranking system (0–100) to surface high-conviction managers by performance, AUM, and filing consistency
• Create a stock-level view showing institutional conviction, net adds, trend scores, and manager overlap
• Build a Strategy Lab for backtesting portfolio weight adjustments against manager benchmarks with market-shock scenarios

Approach & Activities

• Designed the IA around investor mental models: managers → stocks → signals → simulation
• Manager directory with MOTU Score rankings, AUM, specialization, and conviction-level indicators
• Stock intelligence view with MOTU Index vs S&P 500 chart, most bought/sold rankings, and filing date transparency back to SEC EDGAR
• Strategy Lab: portfolio editor with manager benchmarks, simulation period controls, market shock scenarios, and resilience scoring
• AI-powered search across managers and stocks, with natural language queries surfacing 13F-derived insights
• Full Next.js App Router with AG Grid, AG Charts, Tailwind, and Playwright E2E tests

Challenges & Lessons

• Noisy 13F data required careful normalization across filing cadences and AUM thresholds to build conviction signals
• Simulation Lab had to feel actionable without misleading users into treating backtested results as investment advice
• Designing for four distinct personas (retail investor, portfolio manager, risk manager, analyst) within a single coherent interface

Impact