Most open-source enterprise AI platforms use retrieval-augmented generation layered on either vector databases or knowledge graph databases. Vector-based approaches embed your documents, perform similarity searches, and pass matching chunks to the model. This works well for simple lookup tasks and factual questions. The limitation emerges with complex queries that require reasoning across multiple pieces of information or discovering connections between concepts - vector similarity alone can’t construct those insights.
We built technologies that work together to deliver a fundamentally different AI architecture.
You get answers you can trust. Every response includes explainable reasoning paths through the knowledge graphs. You see exactly how the AI connected information across your data. No black box. No guessing why it said what it said. Your AI stays current without downtime. Models update continuously as your operations evolve - incorporating new data, corrections, and feedback while staying live in production. The alternative is scheduled maintenance windows where your AI goes offline, or static models that become outdated. The model becomes yours. Ghost Weights™ train actual parameters within the transformer, not bolt-on adapters. This means genuine integration with your enterprise knowledge, not surface-level customization stacked on someone else’s foundation. The model is trained specifically for your operations and adapts continuously to your evolving needs. Reasoning beats retrieval. Synthesis Graph™ discovers connections across domains that vector search alone cannot find. This means better answers to complex questions, insights that span multiple data sources, and AI that actually understands context rather than just matching keywords.
