Complex AI models often act as black boxes, making it hard to understand why they make certain decisions. A tool that visually maps how inputs propagate through model layers would help researchers and developers.
Build an interactive visualization that shows how data transforms across neural network layers, highlighting key decision points. This could integrate with popular frameworks like PyTorch or TensorFlow.
AI teams at tech companies would pay for better debugging tools, especially in regulated industries where explainability matters.
The MVP could start with static visualizations of simple models, then add interactivity and support for more architectures.
The biggest risk is that major frameworks might build this natively, making a standalone tool redundant.