Code Effects VS LumiGap

• No-code rule builder
  Provides a visual, English-like rule editor that allows business users without programming skills to create and modify complex business rules and decision logic.
• .NET integration
  Designed specifically for .NET environments, allowing seamless embedding into C# and VB.NET applications, which is beneficial for teams already invested in the Microsoft technology stack.
• Rule compilation to native code
  Rules are compiled directly into .NET assemblies rather than interpreted at runtime, which can result in faster execution performance compared to some interpreted rule engines.
• Customizable UI components
  Offers embeddable rule editor controls that can be styled and integrated into existing applications, giving developers flexibility in how end-users interact with the rule authoring interface.
• Reduces developer dependency
  By enabling business analysts to author and update rules directly, it can reduce the need for constant developer involvement when business logic changes, potentially speeding up time-to-market for rule changes.

Possible disadvantages

• Limited platform scope
  Being tightly coupled to the .NET ecosystem limits its usefulness for organizations using other technology stacks like Java, Python, or Node.js, reducing its overall market flexibility.
• Learning curve for setup
  While the rule editor is designed for business users, the initial setup, integration, and configuration within a .NET application often requires significant developer expertise and time investment.
• Niche market presence
  Compared to larger, more established business rules engines and decision automation platforms, Code Effects has a smaller user base and community, which can mean less third-party support, tutorials, and shared resources.
• Pricing transparency
  Detailed pricing information is not always readily available publicly, which can make it harder for prospective customers to quickly assess cost-effectiveness compared to competitors.
• Scalability for complex enterprise needs
  For very large-scale, multi-platform, or highly complex enterprise decisioning needs, it may lack some of the advanced features, integrations, and scalability options found in more comprehensive decision management suites.

• AI table recognition
  Detect cards, stacks, bets, names, board, pot, and table regions from screen data
• Screen capture pipeline
  Read visible poker tables on macOS without connecting to poker rooms
• OCR + Core ML
  Combine text recognition and ML models for structured table-state extraction
• Custom datasets
  Collect screenshots, crops, labels, metadata, and manifests for model training
• Card annotation
  Correct player cards, board cards, labels, and regions across frames
• Player token editing
  Edit names, stacks, bets, and player-specific table tokens
• Bring-your-own models
  Connect external detectors and tune recognition thresholds
• Dataset converter
  Build object detection and card classifier datasets for Create ML workflows
• Live table-state export
  Export recognized cards, stacks, bets, board, and pot through an API
• Model performance tracking
  Compare recognition results, sessions, profit, and model performance over time