| # Differentiators from Traditional AI | |
| ## 1. Enterprise-Focused Design | |
| AGNs are built with an enterprise-focused mindset, designed to solve real business problems rather than simply excel in abstract mathematical challenges. This differentiates AGNs from other AI models that typically lack the contextual and domain-specific understanding needed in practical settings. | |
| ## 2. Structured, Contextual Reasoning | |
| AGNs excel in structured, contextual reasoning. Unlike transformers and LSTMs, AGNs emphasize structured relationships and attribute-based decision-making. This makes AGNs suitable for applications that require deep, multi-domain contextual understanding. | |
| ## 3. Real-Time Learning and Adaptation | |
| AGNs are designed to update and adapt relationships without retraining, which sets them apart from static models. This makes them highly suitable for environments where data changes continuously, and real-time learning is crucial. | |