The Challenge
Professional poker players study Nash-equilibrium strategies across large game trees. Many established tools require local hardware, database downloads, or lengthy solve times, which can limit access during study sessions.
The Chartopus team needed a cloud-native solver that could provide low-latency strategy lookups without requiring users to manage local compute infrastructure.
The Solution
1D.works designed and built the core technology stack behind Chartopus, a proprietary cloud neural network solver for analysing game-theoretic poker strategies.
Key technical components:
- Counter Factual Regret (CFR) Algorithms: A custom CFR implementation for working through strategies across complex decision trees.
- Neural Network Approximation: Multiple neural networks trained to approximate solver outputs, enabling low-latency lookups for preflop and postflop spots without re-solving from scratch.
- Cloud-First Architecture: Fully hosted on Google Cloud and Azure — no database, server, or local setup required by end users. All computation runs server-side.
- Three Integrated Products: The solver powers a Chart Viewer (strategy lookup), Trainer (practice against AI profiles), and Analyzer (hand history review with GTO feedback).
Why This Matters
The project combined game-tree search, regret minimization, neural-network function approximation, and cloud delivery in one user-facing product.
Building Chartopus required solving problems that directly transfer to finance and trading:
- Decision-making under uncertainty with incomplete information
- Real-time inference from pre-trained neural network models
- Scalable cloud compute for computationally intensive workloads
- Proprietary model IP protection in a competitive market
Delivered Capabilities
- Cloud SaaS platform for strategy study, practice, and hand review
- Precomputed neural-network lookups for preflop and postflop analysis
- Browser-based access without user-managed solver infrastructure
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