// CASE STUDIES

The Receipts.

Three systems built inside the Tesseract ecosystem. What they solve, how they were built, and what they proved.

Case Study 01
The InDecision Engine
// The Challenge

7 years of market analysis, 300+ investor calls, and one recurring problem — I could see setups clearly but couldn't systematically articulate what made them high-conviction versus noise. Everything lived in my head.

// What Was Built

A 6-factor weighted scoring model. Daily Pattern (30%), Volume (25%), Timeframe Alignment (20%), Technical Confluence (15%), Market Timing (10%), and Risk Context as a qualitative override. Each factor scored 0-100. Spread between bull and bear scores determines conviction tier.

// The Numbers
82.5%
Directional accuracy
6
Weighted factors
v5
Current version
Live
Polymarket deployment
// The Lesson

The value of a framework is not the output — it's the structured diagnostic. When a trade fails, InDecision tells you exactly which factor was wrong. That's the compound learning loop that pure intuition can never produce.

Case Study 02
Soul — The Digital Twin
// The Challenge

Every pipeline I built was orphaned after it ran. Claude Code sessions start and end. Cron jobs fire into the void. No persistent context, no ongoing intelligence, no way to orchestrate between systems. I needed a nervous system, not more tools.

// What Was Built

Soul — a persistent AI agent running 24/7 on a Mac Mini. Not a chatbot. An orchestration layer. Soul manages Discord and Telegram sessions, spawns Claude Code for build tasks, routes between 40+ cron pipelines, and maintains long-term memory across sessions. The September attempt broke because I was treating agent sessions like API calls. Soul treats them like a consciousness.

// The Numbers
40+
Pipelines orchestrated
16
Incidents caught by Sentinel
0
Unplanned outages (3 weeks)
3
Channels managed simultaneously
// The Lesson

The persistent agent is not a feature. It's the foundation. Everything else in the stack depends on Soul's ability to maintain context, route decisions, and remember what happened. Build the nervous system first.

Case Study 03
The Polymarket Bot
// The Challenge

I had a trading framework with 82.5% accuracy, but I was still executing manually — analyzing setups, placing bets, closing positions by hand. The bottleneck was me. At scale, the advantage disappears if execution speed doesn't match signal speed.

// What Was Built

PolyEdge v4 — fully autonomous prediction market trader. Scans Polymarket binary options, runs each setup through InDecision, executes on high-conviction signals (HC ≥15 spread), manages position sizing, and triggers Post Mortem AI on every close. Direct InDecision Engine integration with no human in the loop.

// The Numbers
v4.0
Current version deployed
HC ≥15
Execution threshold
3
New market categories
Auto
Post mortem on every close
// The Lesson

Automation without a framework is just fast mistakes. The bot is only as good as InDecision. The model came first. The automation made it scale.