Transform Complexity into Clarity with a Revolutionary Decision Intelligence System
SAGE turns unstructured data into actionable insights. Using advanced neuro-symbolic reasoning and cutting-edge AI, SAGE empowers your business to make smarter, faster decisions with confidence. Scalable, adaptable, and designed to solve your most critical challenges, SAGE is your competitive edge in a data-driven world.
Symbolic logic drives domain-specific expertise, organizing unstructured data into well-defined representations, while the modular and swappable architecture enables customization for different domains.
Targeted Knowledge Synthesis
LoRA and embedding techniques enable task-specific fine-tuning, while pre- and post-processing pipelines optimize precision for domain-specific context-aware knowledge synthesis.
Directed and Recursive Networks
Self-aware dynamic adaptation underpins a directed graph architecture to enable general-purpose applicability, enabling deep observability and traceability of problem resolutions.
DIKW Framework Integration
Alignment with a shared knowledge architecture ensures progressively valuable outputs at every stage while enabling broad applicability to existing knowledge workflows.
Insights from AI Visionaries
Discover the potential of cutting-edge artificial intelligence.
The average student tutored one-to-one using mastery learning techniques performed two standard deviations better than students educated in a classroom environment.
~75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D.
Study finds that consultants using AI finished 12.2% more tasks on average, completed tasks 25.1% more quickly, and produced 40% higher quality results than those without AI.
Research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion —by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.
To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation.
