Advancing Reasoning AI with Domain Specialization

Foundation models excel at general reasoning but struggle with the nuanced, often undocumented knowledge that powers real organizations—the specialized ontologies, evolved workflows, and hard-won expertise that exist nowhere in their training data. We’re developing a continuous learning system that captures and maintains this organizational intelligence by ingesting from three critical sources: live data systems (databases, APIs, analytics), knowledge repositories (documentation, code, dashboards), and ongoing user interactions. Through continuous knowledge capture and synthesis, we then enable foundation models to truly understand and evolve with your organization—whether through real-time context injection, fine-tuning, or advanced post-training techniques.

Our research explores a new computational paradigm: a DSL powerful enough for arbitrary computation, yet simple enough for non-technical users to understand, reliable enough for LLMs to generate, and precise enough to compile deterministically for secure sandbox execution. By solving these competing constraints, we enable AI to orchestrate complex data operations—federated retrieval, transformations, semantic reasoning—through domain-native execution plans that any insider can verify, like reading a colleague’s instructions rather than decoding programmer syntax.