Explainable AI w/ James Duez of Rainbird

In this episode, James Duez, CEO of Rainbird, dives into the intricacies of how knowledge and context are represented within AI systems. We explore how structured knowledge graphs, deterministic reasoning engines, and hybrid architectures can elevate AI decision-making and trustworthiness. Whether you're a product manager, AI developer, or business leader, this conversation clarifies how to architect AI solutions that are explainable, auditable, and reliable.

• The distinction between case context (transaction-specific data) and policy context (knowledge and rules) in AI systems.
• How Rainbird leverages knowledge graphs and ontologies to formalize and reason over policy and tacit organizational knowledge.
• The limitations of large language models (LLMs) in reasoning and the importance of keeping them outside the core decision logic.
• The role of inference engines in building deterministic, auditable AI decision layers grounded in policy and knowledge.
• The evolution from traditional knowledge management tools like SharePoint to advanced knowledge elicitation and modeling using AI.
• The concept of knowledge as a first-class citizen in AI architecture, enabling faster and more reliable decision-making.
• The risks of relying solely on LLMs, including hallucinations, model drift, and lack of explainability, and how hybrid architectures mitigate these issues.
• The importance of roles like knowledge engineers and business analysts in managing and architecting organizational knowledge layers.
• Rainbird's reusable, enterprise-grade platform for building, managing, and reasoning over knowledge graphs, separable from data sources.
• The future of hybrid AI architectures: combining symbolic reasoning with probabilistic models to balance flexibility and trust.