TAUG — Research Operating System

An investment research workspace for individual investors. TAUG helps users research companies, form investment theses, track decisions, and learn from outcomes.

What TAUG Is

Research Operating System — Not a terminal, not a dashboard, not a screener.

Core workflow:

Discover → Research → Thesis → Decision → Portfolio → Outcome → Learning

Features

Research Workflow

• Company Workspace — Overview, Financials, Research tabs with data trust indicators
• Research Questions — Track open investigation threads with priority levels
• Investment Theses — 10-field structured theses (stance, conviction, bull/bear case, assumptions, catalysts, risks, exit conditions)
• Research Notes — CRUD notes linked to companies
• Evidence Tracking — Connect notes to thesis fields (supports, contradicts, updates, context)

Decision Support

• Thesis → Position Bridge — Create positions directly from theses with auto-populated conviction
• Portfolio Workspace — Active/closed positions with P&L tracking
• Lessons Learned — Record outcomes and insights from each decision
• Pattern Intelligence — Stance accuracy, conviction accuracy, common lesson themes

Data Trust

• Quality Scores — 7-component breakdown (historical coverage, completeness, validation, verification, freshness, restatement support)
• Freshness Indicators — Visual badges showing data age
• Restatement Tracking — Identify restated financial statements
• Source Attribution — Track data provenance

Research Intelligence

• Research Progress — 4-step checklist (Notes → Thesis → Questions → Position)
• Needs Attention — Priority-sorted list of items needing action
• Research Freshness — Track when research was last reviewed
• Invalidation Conditions — Structured exit triggers (planned)

Tech Stack

Layer	Technology
Framework	Flutter Web
Language	Dart SDK ^3.12.2
State	signals
Routing	go_router
Database	Supabase (schema: taug)
Workers	Python (SEC data pipeline)
Hosting	Vercel

Architecture

lib/
├── core/          # Config, theme, errors, network, schema, utils
├── features/      # Feature modules
│   ├── auth/      # Authentication
│   ├── company/   # Company workspace (overview, financials, research)
│   ├── portfolio/ # Portfolio workspace (positions, lessons, patterns)
│   ├── research/  # Research workspace (questions, theses, notes)
│   ├── companies/ # Companies list
│   ├── settings/  # User settings
│   └── ...
├── shared/
│   ├── models/    # Shared data models
│   └── widgets/   # Reusable UI components
└── main.dart

Getting Started

Prerequisites

• Flutter SDK ^3.12.2
• Supabase project
• Python 3.12+ (for workers)

Setup

1. Clone and configure:

git clone <repo-url>
cd taug
cp .env.example .env
# Edit .env with your keys

2. Install and run:

flutter pub get
dart run build_runner build
flutter run -d chrome

3. Database setup:

supabase db push

4. Deploy Edge Functions:

supabase functions deploy

Data Pipeline

SEC data pipeline runs as scheduled GitHub Actions:

Schedule	Job
Daily 2:15 AM	sync-sec-submissions
Daily 2:30 AM	sync-sec-companyfacts + parse + compute-metrics
Daily 2:45 AM	fetch-sec-filing-documents
Daily 3:00 AM	compute-data-quality
Weekdays 2:00 PM	sync-price-snapshots
Weekly Monday 6:00 AM	sync-fred-series + sync-bps-series

Testing

# Flutter tests
flutter test

# Python worker tests
cd workers && python -m pytest tests/

Deployment

Push to main triggers GitHub Actions:

1. Analyze (flutter analyze)
2. Test (flutter test)
3. Build (flutter build web --release)
4. Deploy to Vercel

License

MIT License — See LICENSE

Links

• Handoff Documentation
• Governance Documents
• Architecture Docs